chimera obscura

Chimera Obscura:Investigations Into Non-Linear Process

Towards The Design Of A Zoo

by

Gabriel Friedman

A thesis

presented to the University of Waterloo

in fulfillment of the

thesis requirement for the degree of

Master of Architecture

Waterloo, Ontario, Canada, 2010 © Gabriel Friedman 2010

I hereby declare that I am the sole author of this thesis. This is a true copy of the

thesis, including any required final revisions, as accepted by my examiners. I under-

stand that my thesis may be made electronically available to the public.

iii

AUTHOR’S DECLARATiON

Across diverse territories - that might otherwise be considered separate from one

another - similar morphogenetic and emergent processes appear to be at play. The

continuous re-inscription of the world, and its inner workings, in perceptible terms

is impelled by a desire to intervene in it with greater precision and subtlety. But if

emergence is already an operational force from which reality is spun, what, if any,

benefit might be gained from a practice that negotiates its subject on these same

terms?

The thesis departs from the premise that both the world and our cultural con-

structions of it are products of emergence, and thus, they may be described using

a convergent set of terms. The research tests this proposition in a design for a new

zoological environment. Besieged by a set of incompatible objectives, the contested

territory of the traditional zoo program is approached with an ethics of emergence

- one where large problems are resolved into small, knowable truths or rules. The

proposed design transgresses the one-sided theatricality of the fixed perspective,

frame, and stage set, reconceiving it instead as an organism in a state of dynamic

equilibrium in which human visitors are active participants, thoroughly imbricated

and entangled.

The work is centred on process-oriented investigations in computational environ-

ments. Through experiments with parametric modeling, object-oriented program-

ming, and other rule-based simulations, the research delaminates the zoo complex

into a series of parallel machines. Each is conceived as a rule-driven procedural

sequence with its own set of inputs and outputs, which, when coupled together,

form the notional feedback loops of adaptive living organisms. These processes are

deployed in order to satisfy a longing for spatial conditions that are at once finite

and indeterminate, both bound by gravity and disorienting, a place that is at the

same time tempered and wild.

v

ABSTRACT

This thesis would not have been possible without the support and generosity of many people.

Thanks to my committee, each of whose vantage points were crucial to the work. Mark, for his

encouragement and expertise, Philip for his insistence on refining and clarifying. Lola’s tire-

less efforts never ceased to amaze me. I am very grateful to have been able to work under her

supervision.

Thanks to faculty members who contributed in small but crucial ways to the course of the

research: Marie Paul MacDonald, Kathy Velikov, Anne Bordelau, Andrew Levitt, Jon McMinn, and

Geoff Thun. Thanks too to Andri Lima as well as the staff at the Musagetes Library.

A special thanks to John Danahy and the Centre for Landscape Research at the University of

Toronto for materials documenting the Lakeview site as well as Elizabeth McQuaig and the Mis-

sissauga Library System who were kind enough to provide historical photos.

Many distributed thanks to a network of researchers, code writers, software users, and animal

specialists who contributed in small ways, allowing access to very specialized territories (in no

particular order): Patrick Keenan and thmvmnt crew, Lee Byron, David Rutten, Giulio Piacentino,

Dirk Anderson, Jeffrey Traer Bernstein, Guillaume LaBelle, Shajay Bhooshan, Jason Black, Dimit-

rie Stefanescu, Rob Laidlaw, Rajaa Issa, Sean McCullough, Rob King, Steven Janssen, Joshua Cot-

ten, Daniel Shiffman, Cheryl Qian, Roland Snooks, Ben Doherty, Andrew Kudless, Kyle Steinfeld

amongst many more, too numerous to name here.

I will probably never be able to say enough praise to do justice to all the love, encouragement

and support (read:shlep’ing) my parents - George and Lynda - have given me. I will forever be

indebted to them for all their generosity, both material and emotional. They, as well as Sol,

continuously provided amazing feedback that helped to ground the work when it seemed to be

floating away..

Much love & praise to friends Yoni, Momi, Kelly, Neary, Oli, Caroline, EJae, Danny, Duncan, Yiu-

Bun all for their encouragement and support.

But above all, I would like to thank my dearest friend and confidante, Vien, for her enduring

courage and faith in me and the generous contributions she made in editing and production. I

can say with the utmost certainty that without her presence in my life, I would not have been

able to come this far.

vii

ACKNOWLEDGEMENTS

To Gertrude and Phil, and the late Istvan and Szosanna.

(...and to all the wild things in my life, past, present, and future...)

ix

DEDiCATiON

iii AUTHOR’S DECLARATiONv ABSTRACTvii ACKNOWLEDGEMENTSix DEDiCATiONxi TABLE OF CONTENTSxiii LiST OF FiGURESxix PREFACE

CHAPTER 1 TOWARDS A GENERAL SETTiNG FOR THE RESEARCH

3 iNTRODUCTiON5 MORPHOGENESiS AND EMERGENCE11 ANiMALS AND ZOOS 21 THE CYBORG’S TRANSGRESSiON31 THE TECHNOLOGiCAL ORiENTATiON39 LAKEviEW MiSSiSSAUGA

CHAPTER 2 CONSTRUCTiNG THE ZOO MACHiNE

51 iNTRODUCTiON55 PROGRAM DEFiNiTiON67 ENviRONMENTAL CALiBRATiON83 iNTERNAL DiFFERENTiATiON93 viSiTOR PATHWAYS107 ENvELOPE

CHAPTER 3 EMERGENT PHENOMENA

121 THE DRAWiNG SET143 CONCLUDiNG REMARKS

APPENDiCES

159 PROCESSiNG iNTRO161 PARAMETRiC MODELiNG iNTRO165 THE FORCE DiRECTED LAYOUT SiMULATiON175 CELLULAR ORGANiSATiON177 WET THREADS SiMULATiON183 WET THREAD PROCESSiNG185 ATTRACTOR PATTERN187 TRiANGULATiON SCHEMA

BiBLiOGRAPHY

193 ARCHiTECTURE, PRACTiCES194 ARCHiTECTURE, THEORY195 BiOMiMiCRY/ANiMAL ARCHiTECTURE195 COMPLExiTY THEORY195 COMPUTATiON196 CULTURAL THEORY196 ECOLOGY196 FiCTiON197 PARADiGMATiC SOURCES197 ZOOS198 ANiMAL DATA SOURCES199 LAKEviEW

xi

TABLE OF CONTENTS

5 FiG 1.1 FLOCKiNG STARLiNGSSOURCE: ASTROBRI PHOTOSTREAM UPLOADED ON JUNE 23, 2006

HTTP://WWW.FLICKR.COM/PHOTOS/_BRI_/173183373/SIZES/O/

11 FiG 1.3 ZOO ENCLOSURE AS MiLiEUSOURCE: PHOTOGRAPH BY AMY STEIN

13 FiG 1.4 POSTER ADvERTiSiNG ‘WALKiNG iN THE ZOO’, 1871SOURCE: HTTP://OGIMAGES.BL.UK/IMAGES/015/015HZZ000001561U00008001%5BSVC2%5D.JPG

15 FiG 1.5 ANiMAL MARGiNALiZATiONSOURCE: PHOTOGRAPH BY ETHAN CHUNG

HTTP://WWW.ETHANCHUNG.COM/SUBDOMAIN.MEDIA/PHOTOS.2004/PICS/041024%20SAN%20DIEGO%20CA%20%28SAN%20DIEGO%20ZOO%29%2029.JPG

17 FiG 1.6 THE iMPOSSiBiLiTY OF ANiMAL ENCOUNTERSOURCE: PHOTO ASSEMBLAGE BY AUTHOR

19 FiG 1.7 MALAYAN TAPiRSOURCE: PHOTO FROM VOLKER SEDING’S “CAPTIVE” SERIES

DAULT, GARY MICHAEL, AND VOLKER SEDING. CAPTIVE : ANIMALS & ARTIFICE : THE ZOO PHOTOGRAPHS OF VOLKER SEDING = CAPTIFS : ANIMAUX ET ARTIFICE : LES PHOTOGRAPHIES DE JARDINS ZOOLOGIQUES DE VOLKER SEDING TEXTES DE GARY MICHAEL DAULT ; TRADUITS DE L'ANGLAIS PAR MARIE-CLAUDE ROCHON. MONTRÉAL: LES 400 COUPS, 2007. PP 13.

21 FiG 1.8 THE vACANTi MOUSE SOURCE: HTTP://SONGSHUHUI.NET/WP-CONTENT/UPLOADS/2009/02/MOUSE-EAR.JPG

21 FiG 1.9 STELARC, PERFORMANCE ARTiSTSOURCE: HTTP://DEADLINESCOTLAND.FILES.WORDPRESS.COM/2009/04/STELARC.JPG

23 FiG 1.10 CYBERNETiC iMAGERYSOURCE: COVER OF "ANIMALS/MACHINES: EXPLORATIONS IN COMMUNICATIONS AND CONTROL" BY

GARNET HERTZHTTP://WWW.CONCEPTLAB.COM/UCI/2004FALL/PENNY/TUTORIAL/1.JPG

25 FiG 1.11 x-RAY OF HUMAN HANDS WiTH RFiD iMPLANTSOURCE: PHOTOGRAPH BY AMAL GRAAFSTRA

HTTP://AMAL.NET/BLOG/LINKS/2006-03-30_-_HANDS.JPG

27 FiG 1.12 “WHAT GOES ON IN OUR HEAD WHEN WE SEE AN AUTO AND SAY ‘AUTO’”SOURCE: ORIGINAL ILLUSTRATION BY FRITZ KAHN, DAS LEBEN DES MENCHEN, STUTTGART, 1929

HTTP://TOOMANYINTERESTS.FILES.WORDPRESS.COM/2008/09/KAHN-STRUCTURE-P539-SEE-AND-SAY-AUTO-CROPPED.JPG

29 FiG 1.13 CYBERNETiC DEPiCTiON OF BALL TURRETSOURCE: TIME MAGAZINE, 24 JAN 1944 , VOL. 16, NO. 4, PP 66-67

HTTP://BOOKS.GOOGLE.CA/BOOKS?ID=-VYEAAAAMBAJ&PG=PA66&DQ=SPERRY&AS_PT=MAGAZINES&EI=RE6WS6FKA53EMAZPICIM&CD=1#V=ONEPAGE&Q=SPERRY&F=FALSE

31 FiG 1.14 WiM DELvOYE’S CLOACA MACHiNESOURCE: WIM DELVOYE, CLOACA N° 5, 2006

PHOTO BY WIM VAN EGMONDHTTP://WWW.UQAM.CA/NOUVELLES/2009/GALERIE-EXPO-2009/WIM-DELVOYE/WIM_DELVOYE_CLOACA1.JPG

33 FiG 1.15 JAPANESE ROBOT CAT, SEGA TOYS, 2006 SOURCE: SCREEN CAPTURE OF SEGATOYS WEB PAGE FOR YUMENEKO, A YUMEPET

HTTP://WWW.SEGATOYS.CO.JP/YUMEPET/YUMENEKO/INDEX.HTML

35 FiG 1.16 BASiC CONTROL STRUCTURESSOURCE: DIAGRAM BY AUTHOR

37 FiG 1.17 BOiDS ALGORiTHM DiAGRAMSSOURCE: DIAGRAM BY AUTHOR

39 FiG 1.18 AERiAL PHOTOGRAPH OF LAKEviEW GSSOURCE: ONTARIO POWER GENERATION

HTTP://WWW.OPG.COM/POWER/IMAGES/LAKEVIEW_HIGH.JPG

41 FiG 1.19 • MAP OF SOUTHERN ONTARiO’S GOLDEN HORSESHOESOURCE: MAP BY AUTHOR

41 FiG 1.20 • SiTE CONTExTSOURCE: PLAN BY AUTHOR

xiii

LiST OF FiGURES

43 FiG 1.21 viEWS OF THE FOUR SiSTERS BEiNG DEMOLiSHED iN 2006SOURCE: MISSISSAUGA LIBRARY SYSTEM

PHOTOS BY LAWRENCE R. NICOLL

45 FiG 1.22 iLLUSTRATiON OF HiSTORiCAL DEvELOPMENT OF ZOOSSOURCE: DIAGRAMS + ILLUSTRATION BY AUTHOR

47 FiG 1.23 • PANORAMA OF LAKESHORE RD.SOURCE: PHOTO ASSEMBLAGE BY AUTHOR

47 FiG 1.24 • METRO TORONTO ZOO SUPERiMPOSED ON LAKEviEW GSSOURCE: VISUALIZATION BY JOHN DANAHY

CENTRE FOR LANDSCAPE RESEARCH, U OF T

51 FiG 2.2 PROCESSUAL OvERviEWSSOURCE: ILLUSTRATION BY AUTHOR

53 FiG 2.3 DRAGONFLY WiNGSSOURCE: PHOTO BY WONG.

SABAH, MALAYSIA. MARCH 2007HTTP://WWW.ESABAH.COM/DRAGONFLY/AGRIONOPTERAINSIGNIS/MALE/AGRIONOPTERAINSIGNIS7.JPG

55 FiG 2.4 PROCESSUAL FLOWSSOURCE: ILLUSTRATION BY AUTHOR

57 FiG 2.5 MULTiPLE ANiMAL viEWSSOURCE: ILLUSTRATION BY AUTHOR

59 FiG 2.6 ACTivE MEDiUMS AND BEHAviOURSSOURCE: DIAGRAM BY AUTHOR

61 FiG 2.7 GRAPH OF COLLECTiON SOURCE: GRAPH BY AUTHOR

63 FiG 2.8 MOviNG FROM TREE TO RHiZOMESOURCE: ILLUSTRATION BY AUTHOR

65 FiG 2.9 ANiMAL DiFFERENCE TABLESSOURCE: CHARTS BY AUTHOR


69 FiG 2.11 FORCE DiRECTED LAYOUTSOURCE: SCREEN CAPTURE OF SIMULATION BY AUTHOR

71 FiG 2.12 LARGE-SCALE ONLiNE SOCiAL NETWORK viSUALiZATiONSOURCE: VISUALIZATION BY JEFF HEER, 2004

HTTP://WWW.CS.BERKELEY.EDU/~JHEER/SOCIALNET/SOCIALNET_EDGES.PNG

73 FiG 2.13 AxONOMETRiC OF SiTE ATTRACTORSSOURCE: DIAGRAM BY AUTHOR

75 FiG 2.14 ANiMAL ExHiBiTSSOURCE: SCREENCAPTURE OF SIMULATION BY AUTHOR

77 FiG 2.15 CLiMATiC ANCHORS SOURCE: SUPERIMPOSITION OF SIMULATION CAPTURES BY AUTHOR

79 FiG 2.16 iLLUSTRATiON OF ExAMPLES OF CONvERGENT EvOLUTiONSOURCE: HTTP://WWW.NWCREATION.NET/MARSUPIALS.HTML

81 FiG 2.17 AxONOMETRiC DiAGRAMS MAPPiNG ExHiBiT DiSTRiBUTiONSOURCE: DIAGRAMS BY AUTHOR


85 FiG 2.19 iNvESTiGATiONS iNTO NEiGHBOURHOODS SOURCE: STUDIES BY AUTHOR

87 FiG 2.20 AxONOMETRiC OF GROUND OPERATiONSSOURCE: AXONOMETRIC BY AUTHOR

89 FiG 2.21• EDGE CONDiTiONS MAPSOURCE: DIAGRAMS BY AUTHOR

xiv

89 FiG 2.22• MATRix OF SPATiAL ADJACENCiESSOURCE: DIAGRAMS BY AUTHOR

91 FiG 2.23 ANiMAL QUARTERSSOURCE: DIAGRAM BY AUTHOR


95 FiG 2.25 ZOO PUBLiC CiRCULATiON STRATEGiESSOURCE: DIAGRAMS BY AUTHOR

ATOP AERIAL VIEW OF GENERIC SUBDIVISIONHTTP://WWW.TREEHUGGER.COM/CUL-DE-SAC-2.JPG

97 FiG 2.26 • METRO TORONTO ZOO MASTERPLANSOURCE: MASTERPLAN DRAWN BY AUTHOR

BASED ON ORIGINAL DRAWINGS BY M. TESHIMA PUBLISHED IN"ZOOLOGICAL PARK, METROPOLITAN TORONTO." CANADIAN ARCHITECT 13 (1968): 57-8. PRINT.

97 FiG 2.27 • GRiD CiRCULATiON STUDYSOURCE: DIAGRAMS BY AUTHOR

99 FiG 2.28 AxONOMETRiC DiAGRAM OF 3 STRATA & CONNECTiONS BETWEEN ExHiBiTSSOURCE: DIAGRAM BY AUTHOR

101 FiG 2.29 DiAGRAMS iiLLUSTRATiNG SiMULATiON iN DEvELOPMENTSOURCE: SIMULATION CAPTURES BY AUTHOR

103 FiG 2.30 PATH OPTiMiZATiON SiMULATiON SOURCE: SIMULATION DRIVEN DIAGRAM BY AUTHOR

105 FiG 2.31 • SWELLiNG DiAGRAMSOURCE: PLANIMETRIC DIAGRAM BY AUTHOR

105 FiG 2.32 • AxONOMETRiC SWELLSSOURCE: RENDERED AXONOMETRICS BY AUTHOR


109 FiG 2.34 AxONOMETRiC OvERviEW OF ENvELOPESOURCE: AXONOMETRIC BY AUTHOR

111 FiG 2.35 iLLUSTRATiON DESCRiBiNG THE PROCESS BEHiND THE GENERATiON OF THE SURFACESSOURCE: ILLUSTRATION BY AUTHOR

115 FiG 2.36 OvERviEW SHOWiNG DiSTRiBUTiON AND ALLOCATiON OF PANELSSOURCE: DIAGRAM BY AUTHOR

117 FiG 2.37 DETAiL OF FRAGMENT AND iNDiviDUAL PANELSSOURCE: FRAGMENT PLAN + ILLUSTRATION BY AUTHOR

122 FiG 3.1 SiTE PLANSOURCE: SITEPLAN BY AUTHOR

123 FiG 3.2 AERiAL PERSPECTivESOURCE: RENDERED PERSPECTIVE BY AUTHOR

125 FiG 3.3 ROOF PLANSOURCE: PLAN BY AUTHOR

126 FiG 3.4 PLAN - ABOvE ExHiBiT LEvELSOURCE: PLAN BY AUTHOR

127 FiG 3.5 PLAN LEvEL - BELOW ExHiBiT LEvELSOURCE: PLAN BY AUTHOR

130 FiG 3.6 SECTiON ASOURCE: SECTION BY AUTHOR

130 FiG 3.7 SECTiON B SOURCE: SECTION BY AUTHOR

132 FiG 3.8 SECTiONAL PERSPECTivE OF FRAGMENTSOURCE: RENDERED SECTIONAL PERSPECTIVE BY AUTHOR

xv

133 FiG 3.9 ExPLODED AxONOMETRiC OF FRAGMENTSOURCE: EXPLODED AXONOMETRIC DIAGRAM BY AUTHOR

134 FiG 3.10 PLAN FRAGMENT - OF ROOFSOURCE: PLAN BY AUTHOR

135 FiG 3.11 PLAN FRAGMENT - THROUGH ROOFSOURCE: PLAN BY AUTHOR

136 FiG 3.12 PLAN FRAGMENT - ABOvE ExHiBiT LEvELSOURCE: PLAN BY AUTHOR

137 FiG 3.13 PLAN FRAGMENT - BELOW ExHiBiT LEvELSOURCE: PLAN BY AUTHOR

138 FiG 3.14 viGNETTE - viEW FROM WiTHiN SERviCE CHANNELSOURCE: RENDERED PERSPECTIVE BY AUTHOR

139 FiG 3.15 viGNETTE - viEW FROM GiANT ANT-EATER ExHiBiTSOURCE: RENDERED PERSPECTIVE BY AUTHOR

140 FiG 3.16 viGNETTE - viEW FROM MEDiTERRANEAN HORSE-SHOE BAT ExHiBiT

SOURCE: RENDERED PERSPECTIVE BY AUTHOR

141 FiG 3.17 viGNETTE - viEW ALONG viSiTOR PATH iN TREE CANOPYSOURCE: RENDERED PERSPECTIVE BY AUTHOR

143 FiG 3.18 viGNETTE - viEW FROM CLOUDED SNOW LEOPARD ExHiBiTSOURCE: RENDERED PERSPECTIVE BY AUTHOR

A-xix FiG A.1 SCREENGRAB OF THE PROCESSiNG iDE iN USESOURCE: SCREENCAPTURE BY AUTHOR

A-xxi FiG A.2 SCREENGRAB OF GRASSHOPPER iN USESOURCE: SCREENCAPTURE BY AUTHOR

A-xLiv FiG A.3 SCREENGRAB OF THE PROCESSiNG iDE iN USESOURCE: SCREENCAPTURE BY AUTHOR

A-xLv FiG A.4 SCREENGRAB OF THE PROCESSiNG iDE iN USESOURCE: SCREENCAPTURE BY AUTHOR

A-xvii FiG A.1 SCREENGRAB OF THE PROCESSiNG iDE iN USESOURCE: SCREENCAPTURE BY AUTHOR

A-xix FiG A.2 SCREENGRAB OF GRASSHOPPER iN USESOURCE: SCREENCAPTURE BY AUTHOR

A-xxxiii FiG A.4 TERRiTORiAL PARTiTiONiNGSOURCE: SCREENCAPTURE BY AUTHOR

A-xxxiii FiG A.3 LANDCOvER SCHEMASOURCE: SCREENCAPTURE BY AUTHOR

A-xL FiG A.5 WET THREADS MANiPULATiONSOURCE: SCREENCAPTURE BY AUTHOR

A-xLvi FiG A.6 ENvELOPE PANEL ARTiCULATiONSOURCE: SCREENCAPTURE BY AUTHOR

A-xLvi FiG A.7 ENvELOPE GEOMETRYSOURCE: SCREENCAPTURE BY AUTHOR

A-xLviii FiG A.8 STRUCTURAL GRiDSOURCE: SCREENCAPTURE BY AUTHOR

xvi

xix

PREFACEHOW TO READ THE THESiS

The title of the thesis - Chimera Obscura - implies a hybrid cybernetic organism, part

animal, part machine, one, whom, through a perverse coupling, has the capac-

ity to reveal by way of its murky conflation. It is a sort of unity of difference; the

zoo mixes not only different species but also different worlds - particularly that of

the human and the animal - and it can do so only by way of architecture and its

technological interventions. The zoo program’s long history has been propelled by

human curiosity with the Animal. Concerns about the treatment of animals as well

as our cultural complicity in their internment have posed major challenges to its le-

gitimation. Even if potential study and reverence for the natural world were enough

to possibly warrant animal suffering, questions remain about which natural world

it is that these reconstructions refer to. With the promise of reviving the presence of

animals in our lives all but vanquished, the zoo program may still be able to facili-

tate meaningful and vital experiences. The question, however, is what possibility is

there for the experience of the majesty and mystery of the wilderness in a space so

confined, so bereft, and so neutralized?

Chimera Obscura reconceives the zoo as a living entity, one assembled like a flock; its

wholeness is one of perception, and cannot be deduced from its parts in isolation.

Its edges are hazy, its centre is everywhere and nowhere, it has no front or back. Its

primary structural device is one of relations, a concern with the spaces between

things over the things themselves. With this ambition, the intent is to create an

environment that registers diversity and difference.

This thesis is not an inquiry into the ethical implications of keeping animals in cap-

tivity. In light of the contentiousness surrounding this issue, the thesis breaks the

large fundamental questions down into a series of smaller ones, from which more

resolute ends may be generated. It embraces a position of operating from within

this very fraught territory to maximise the gains it might yield, while minimizing

any of its negative consequences. It is first and foremost an architectural practice-

based research, one which necessitates a healthy dose of gravity.

While the thesis is grounded in critical discourse on culture, it focuses on theories

of self-organization and computation as tested through craft, not theory.

xxi

The thesis engages computation with the art of the “hack”; it operates in processes

and languages on territories which it may only be loosely acquainted with. The use

of computer scripting in the project evolved as its needs arose, and in this way, there

may have been more efficient, clearer, and more appropriate methods than those

that were employed. It purposefully crosses between exclusive territories of special-

ization to splice together new productive combinations. It opportunistically exploits

misappropriation, abuse, and cross-contamination as co-operants in the transfor-

mation of the possible into the actualized.

This thesis, like its subject, is a work of synthesis, an emergent phenomena whose

form must be “read” out of the aggregate and broad range of overlapping concerns.

It is part methodology, part design project. It ventures to bridge realms that would

otherwise be mutually exclusive by literally incorporating disparate flows – embed-

ded in information matter – into consistent planes of operation. To be absolutely

clear, the thesis tests strategies by which real-life phenomena from the world is

transformed into data to then be processed, translated, and finally converted into

inhabitable space.

It is a response and contribution to a lineage of architectural practice concerned

with generative procedures, particularly those that are autopoetic and self-organi-

zational. The project is to be seen as marking and taking part in a recent shift that

progresses beyond pure theory or experiment into scenarios of greater consequence.

As a document, it illustrates the ideas and strategies engaged with over the course

of research, to be unfolded in three parts. It begins with a chapter on its methodolo-

gy, developing a framework within which the work should be viewed, and the detail-

ing the seeds of its investigations. It is followed by a section depicting five notionally

separate generative “machines” behind the design for a zoo. Each is framed by a

section describing it in terms of its distinct process, sources, scripted forms, and

implementation. The final chapter concludes the thesis with a drawing set of the

design speculation, and a reflection upon the potential and limitations of the use of

non-linear generative procedures in architectural design.

WHAT THE THESiS iS

THE FORMAT OF THE THESiS

1

CHAPTER 1 TOWARDS A GENERAL SETTiNG FOR THE RESEARCHSiTUATiNG THE WORK

2TOWARDS A GENERAL SETTiNG FOR THE RESEARCH

3 iNTRODUCTiON

This chapter frames the research in terms of a number of parallel investigations.

It is an outline of some of the issues it contends with; those provoked both by its

subject - the zoo, and the animal - and its themes - emergence, self-organization,

and morphogenesis. From these theories of complexity, it segues into a discussion

of technology and computational practice as media capable of simulating emergent

phenomena. In the final section the thesis proposes that the grounds of the recently

retired Lakeview Generating Station, in Mississauga, Ontario, be made home to a

new zoo.

iNTRODUCTiON

5 MORPHOGENESiS AND EMERGENCE“THE WiLL TO ANiMATiON”1

It is otherwise with emergents, when, instead of adding measurable motion to measurable motion, or things of one kind to other individuals of their kind, there is a co-operation of things of unlike kinds. The emergent is unlike its components insofar as these are incommensurable, and it cannot be reduced to their sum or their difference.2

1. PiA EDNiE-BROWN. ARCHITECTURAL DESIGN 71.2 (2001): 64.

2. G.H. LEWES, PROBLEMS OF LIFE AND MIND (FIRST SERIES) VOL. 2 (LONDON: TRÜBNER, 1875), 412.

FIG 1.1 FLOCKiNG STARLiNGSEUROPEAN STARLiNGS FLOCKiNG TO CREATE THE PHENOMENON KNOWN AS “BLACK SUN” THE PHOTO WAS TAKEN ON APRiL 5 FROM 19.30 TO 20.30 AT TØNDERMARSKEN iN DENMARK

MORPHOGENESiS AND EMERGENCE“THE WiLL TO ANiMATiON”

1


If, as Manual De Landa contends in his A Thousand Years of Nonlinear History, “reality

is a single matter-energy undergoing phase transitions of various kinds, with each

new layer of accumulated ‘stuff’ simply enriching the reservoir of nonlinear dynam-

ics and nonlinear combinatorics available for the generation of novel structures and

processes.” 3, then where does this leave the architect? What does this mean for the

practice of architecture? If space is, by this definition, already a product of nonlinear

dynamics - of contingencies within the world - the thesis asks what can be gained

from taking on a position that both explicitly acknowledges these processes and

seeks to operate on the system instead of its dimensions, striated and separated. It

looks for the potential in reflecting on design and formative processes themselves

instead of matter directly. What can this self-consciousness mean to our construc-

tion of the world, and how will it subsequently feed back on the way we lead our

lives?

Emergence is a far-reaching scientific concept, traversing scales, and even manag-

ing to conflate - and thus do away with - the dualism inherent to the nature/culture

discourse; it does all this in its attempt to describe the rules or behaviours that give

rise to complex adaptive systems. Its subjects are diverse, from flows in patterns

of weather, to the coordinated flocking of birds, and on to the fluctuations in the

stock market. It is typically characterized by the indivisibility and irreversibility of

its wholes, which are arrived at only by way of the accretion of low-level rules. The

ability for form [or any persistent identifiable body] to arise without the oversight

or command by a single agent or “decider” has captivated scientists, designers and

artists since its inception into popular consciousness in the early 90’s.

Since that time, much theoretical discourse and a limited amount of practice has

been directed towards its application in architecture. Countering the hylozoismi

of the architect as auteur, in emergence architects have seen the promise of novel

spatial conditions through actualizations of mass virtual forces at play including

structural, environmental, and even those of culture and economics. Wrenched

from the hands of the staid control of centralized regimes, architecture, it has been

envisioned, might be liberated towards diverse, and indeterminate ends. And since

“more is different” 4, the inclusion of a greater field of inflections might transpire an

architecture embodying the pluralism of its situation.

NON-LiNEAR DYNAMiCS

3. MANUEL DE LANDA, A THOUSAND YEARS OF NONLINEAR HISTORY. (NEW YORK: ZONE BOOKS, 1997), 21

ON EMERGENCE

i. RHiZOMATiC, AS iN DELEUZE & GUATTARi’S NOTiON OF THE RHiZOME, AS COUNTER TO THE ORGANiSATiONAL STRUCTURE OF A TREE AND iTS BRANCHES. THE RHiZOME iS AHiERARCHiCAL, WiTHOUT BEGiNiNNiNG OR END, ENTRY OR ExiT

4. P. W. ANDERSON, “MORE iS DiFFERENT: BROKEN SYMMETRY AND THE NATURE OF THE HiERARCHiCAL STRUCTURE OF SCiENCE”, SCIENCE 177 (1972): 393


While these promises may have been enough to inspire a new orientation to the

world, they have come part and parcel with new demands on practice. The in-

corporation of a developmental time - as required by emergent processes - into

architectural praxis has necessitated new practices and mediums, wherein one can

now simulate the real-time feedback processes of evolution. These developments

have forced us to reconsider the profession’s current atomic approach to seeing and

constructing the world, one which has firmly invested itself in discrete parts.

Questions still remain as to what emergence will mean to architecture in light of

the fact that emergence may always be at play even when it is not explicitly stated

in so many ways. This thesis tests the allegations of a new paradigm in research

that suggest that there may be something legitimately novel and useful occurring

when architects start to simulate natural processes as generative tools to form mat-

ter, rather than using them simply as discursive metaphorsii. Emergent processes,

more than the optimization of component parts, are about a search for an increase

in collective fitness, and their implicit correlate is that the parts become trans-

formed in entirely novel ways in the process. Its wholes are unforeseeable through

the summation of their respective contributions; and their resultant conditions,

more than the performative gains they promise, generate novel ambient conditions

with highly evocative capacities.

ii. ARCHiTECTURAL DESiGN JOURNAL (AD) HAS PUBLiSHED A NUMBER OF iSSUES ON THE SUBJECT. EDiTORS MiCHAEL HENSEL, ACHiM MENGES, AND MiCHAEL WEiNSTOCK ALSO DiRECT THE EMTECH PROGRAM AT THE AA

11 ANiMALS AND ZOOS THE SUBJECT (OF THE ENQUiRY)

The animal scrutinizes him across a narrow abyss of non-comprehension. This is why the man can surprise the animal. Yet the animal - even if domesticated - can also surprise the man. The man too is looking across a similar but not identical, abyss of non-comprehension. And this is so wherever he looks. He is always looking across ignorance and fear. And so, when he is being seen by the animal, he is being seen as his surroundings are seen by him. His recognition of this is what makes the look of the animal familiar. And yet the animal is distinct, and can never be confused with man. Thus, a power is ascribed to the animal comparable with human power but never coinciding with it. The animal has secrets which, unlike the secrets of caves, mountains, seas, are specifically addressed to man.5

ANiMALS AND ZOOS THE SUBJECT (OF THE ENQUiRY)

5. JOHN BERGER, ABOUT LOOKING (NEW YORK: PANTHEON BOOKS, 1980), 5.

FIG 1.2 ZOO ENCLOSURE AS MiLiEU


The Stilton, sir, the cheese, the O.K. thing to do, On Sunday afternoon, is to toddle in the Zoo. Weekdays may do for Cads, but not for me and you, So, dress’d right down the road, we show them who is who. The walking in the Zoo, walking in the Zoo, The O.K. thing on Sunday is the walking in the Zoo. Walking in the Zoo, walking in the Zoo. The O.K. thing on Sunday is the walking in the Zoo.

Sparking outrage from the Fellows of the Zoological Society’s Gardens (what would

eventually become the Regent’s Park Zoo in London), the colloquial term “Zoo” was

coined with these lyrics toward the end of the 19th century, sung by the Victorian

Music Hall Artist, Alfred Vance. This betrayal of the zoo, which had been previously

legitimated as a scientific endeavour, instantaneously conjured the mass appeal

that animals have historically had as objects of our curiosity; subsequently, the zoo

was to be appropriated by the public as a popular destination for recreational activ-

ity.

If the very first modern zoos (early 19th century) were testaments to science’s

increasing reign over the natural world, so, too, were the earliest animal collec-

tions on record. As displays of power and wealth, they signalled a different sort of

dominion over the natural world. Animals were returned as bounty upon conquest

into foreign lands, often exchanged as gifts between rulers. As elements of one’s

private property, they were kept as sources of personal entertainment and symbolic

resonance.7

The largest private collections were typically owned by royalty, such as the one held

by Louis XIV at Versailles. At these country villas, animals were routinely kept in

cages set in adjoining gardens. Each specimen would be housed in their own villa

decorated either with folklore related to it, or with symbols of the country of its geo-

graphical origins.. There was no effort made to intimate a likeness to the animals’

native habitats, privileging neither the animal resident nor the human spectator.8

New concerns with cataloguing and classifying the natural world emerged dur-

ing the Enlightenment, first amongst doctors and pharmacists, and eventually by

6. CHRiSTOPHER PULLiNG, THEY WERE SINGING AND WHAT THEY SANG ABOUT (LONDON: GEORGE G. HARRAP & CO. LTD., 1952), 14.

A WALK THROUGH THE ZOO.

7. ELiZABETH HANSON, ANIMAL ATTRACTIONS : NATURE ON DISPLAY IN AMERICAN ZOOS (PRiNCETON, N.J: PRiNCETON UNivERSiTY PRESS, 2002)

8. DAviD HANCOCKS, A DIFFERENT NATURE : THE PARADOXICAL WORLD OF ZOOS AND THEIR UNCERTAIN FUTURE (BERKELEY: UNivERSiTY OF CALiFORNiA PRESS, 2001)

FIG 1.3 POSTER ADvERTiSiNG ‘WALKiNG iN THE ZOO’, 1871


European aristocrats. The animal kingdom was one branch amongst many in nature

that demanded mastery. This was first made spatially manifest in the Wunderkam-

meren. These cabinets of curiosities lay the groundwork for what would become

our Museums of Natural History, in their display of taxidermic specimens along-

side shells, insects, and other exotic artefacts. The common aim of these spaces

and the many scientific texts that were to follow the same pursuit - typified most

famously in works by Carl Linnaeus, Charles Darwin, and Ernst Haeckel - was to

bring together diverse bodies of knowledge into comprehensive systems. In them,

the world was pictured for the first time as a single entity, one that cohered, was

ordered, and which might be divulged of all its secrets. It also signalled, for the first

time, the inherent gap between the world and its representation.9

The first modern zoos began to appear alongside the industrialization and urban-

ization of the European continent in the early part of the 19th century. These first

institutions intended to elevate the viewing of animals from the casual curiosity

of roadside spectacle towards full-fledged civic institutions dedicated to research

and study. Animals were to be exhibited as taxonomic specimens, one to a cage or

pavilion. Inertly framed by confined enclosures, they were only moderately more

lively than the taxidermic specimens typical of the panoramic tableaus at natural

history museums.10

From the 1960’s onward, rising concerns for the natural environment and the

welfare of wildlife, sparked changes in zoo design. In North America, the creation

of zoos for entertainment was cast as socially unacceptable. New zoos specified

that their main beneficiaries would be the animals themselves. Immersive exhibits

began to appeariv, affirming the primacy and inseparability of the relationship be-

tween species and their environment. The hope was that the animals could serve as

mascots for the conservation of wild and endangered places and creatures.

The zoo narrative continues to this day to be challenged and rewritten. The benefits

of its purported pedagogical capacity, captive breeding programs, habitat conserva-

tion, and environmental awareness missions remain questionable when set against

the real issues that confront animal welfare.

9. SUZANNE MACLEOD, RESHAPING MUSEUM SPACE: ARCHITECTURE, DESIGN, EXHIBITIONS (NEW YORK: ROUTLEDGE, 2005), 147.

10. iBiD., 95

iv. SEE JONES AND JONES LANDSCAPE ARCHiTECTS’ SEATTLE WOODLAND PARK ZOO

FIG 1.4 ANiMAL MARGiNALiZATiON


[…] animals are always the observed. The fact that they can observe us has lost all significance. They are objects of our ever-extending knowledge. What we know about them is an index of our power, and thus an index of what separates us from them. The more we know, the further away they are.11

In his text entitled Why Look at Animals? John Berger writes that the marginalization

of the animal took root with industrialization. What began in the Age of Reason as

an attempt to uncloak the natural world of its secrets has ended with the almost

complete disappearance of wildlife from our lives.

The zoo to which people go to meet animals, to observe them, to see them, is, in fact, a monument to the impossibility of such encounters. Modern zoos are an epitaph to a relationship which was as old as man.12

Particularly endemic to the program of the zoo are questions concerning agency

and to whom, precisely, it shall serve. For the purposes of the thesis, it constitutes

a highly contested territory with divergent forces and ambitions competing for pri-

macy. Attesting to its impossibility, the zoo is a place underpinned by the sublima-

tion of life. Nowhere are the less palatable aspects of life - death, decay, predation

- left bare. The wilderness inside its animals has been all but dispelled, leaving

visitors wondering why the animals seem less than what they believed them to be.13

The thesis aims to liberate the repressed wilderness by focusing on the performa-

tive aspects of the zoo’s architecture. By making the welfare of the animals a central

concern, the zoo becomes an instrument for the registration of difference and di-

versity, a key aspect of its pedagogical intent. It posits that the zoo’s program might

benefit from relinquishing some of its control; control that is primarily enforced

through spatial means. The design strategy is one of searching out where program

is convergent, and in doing so, achieving a sort of collective gain [read:emergence].

The very qualities of the animal world that zoo visitors seek out, are the very same

that are suppressed as a consequence of captivation. For animals to be furnished

with environments capable of their full actualization and sustenance, and for hu-

man visitors to be held, once again, captive by an environment capable of returning

their glance, the two must be imbricated in one another. The thesis proposes treat-

WHY LOOK AT ANiMALS

11. BERGER, ABOUT LOOKING, 16.



FIG 1.5 THE iMPOSSiBiLiTY OF ANiMAL ENCOUNTER


ing the diverse range of programmatic requirements, human, animal or otherwise,

as if, within them, lay the seeds of the zoo’s morphogenetic development.

The positioning of the animals as hypothetical clients is an amplification of the

already tenuous relationship that architecture has with those it serves. In doing so,

emergence, as a strategy to produce wholes from small, knowable, truths or rules,

suggests a possible course of relief to a more widespread situation than just that of

the immediate territory of the zoo. Furthermore in light of the fragility and in-

accessibility of standards that might help inform the housing of animals in captivity

the design is given a little more room to speculate on the possible worlds it might

engender. [And yet, by the very same token, it has the power to cause heightened

moments of anguish over the very same leaps one affords themselves.]

The thesis suggests looking at architecture as an ecological milieu in which parts

must operate in interdependent and adaptive ways. It is an opportunity to construct

the landscape, somewhere between the dueling opposites of the natural/artificial

discourse.

The zoo forces one to negotiate issues related to the simulacrumiii; the constructed,

camouflaged to appear as a by-product of natural, unmediated forces. Though the

exhibits intend to represent animals, and their nature, there is tension between

them - creatures of our curiosity as exhibited - and their genetic doubles, liv-

ing uninterrupted in the wild. The question is, how can we preserve [maximize]

the verisimilitude and vicissitude of life while at the same time dampening some

of its range [minimize] necessitated by the artificial condition of condensed and

intertwined space. [Therein lies concerns about control and to what extent we can

relinquish it while still maintaining any authority.] With the zoo, the thesis imagines

a return to the wild, like that of a feral animal. Somewhere between the wild and

the domesticated, exists a creature acquainted with the human world, but that has

an inner wilderness that can condition its visitors, just as much as they do it.

iii. SIMULACRUM, AS iN A PORTRAYAL OR REPRESENTATiON

FIG 1.6 MALAYAN TAPiR, FROM vOLKER SEDiNG’S “CAPTIVE” SERiES

21 THE CYBORG’S TRANSGRESSiONA CULTURAL PERSPECTivE

Cyborg unities are monstrous and illegitimate; in our present political circumstances, we could hardly hope for more potent myths for resistance and recoupling.14

THE CYBORG’S TRANSGRESSiONA CULTURAL PERSPECTivE

14. DONNA HARAWAY, SIMIANS, CYBORGS, AND WOMEN : THE REINVENTION OF NATURE (NEW YORK: ROUTLEDGE, 1991), 154.

FIG 1.7 THE vACANTi MOUSE

FIG 1.8 STELARC, PERFORMANCE ARTiST


The territory of the zoo program, and emergence as a way of understanding com-

plexity, should be read as foils to larger cultural concerns to which this thesis will

address only obliquely.

With the prospect of a design for a new zoo, a number of boundaries stand poised

to be breached; universals, such as those between man and animal, and between

life and technology; related to the zoo program, that between the zoo and the city,

amongst distinct species or types, and between the zoo visitor and captive animal.

The ambition in the proposal, and its underlying processes, is to disorder these

distinct breaks, to disrupt their dualities, making it possible to see continuity and

likeness across boundaries.

It is implicit, then, that the thesis is also concerned with the perception and con-

stitution of bodies or wholes. Both the zoo program and emergence share in this

preoccupation; the zoo - as a museological institution that arranges and classifies

the world - and emergence - as a conceptual device for, both, perceiving, and under-

standing complex phenomena. Since the widespread acceptance of the Linnean

systemv, zoos have prioritized taxonomic classification of the biological diversity

of the world. The shared genetic traits that come to define each species - primarily

manifested in visible morphological traits - confine the individual to a type, thus

ignoring local particularities. From the population, it is either the statistical average

that comes to define the group or that which is remarkable (largest, fastest, most

ferocious) by human perception. With emergence comes an animate (“hub bub”), ir-

resolute energy, injected into an otherwise closed, fixed unity; it describes the same

organisms as an amalgam of units, the whole emerging only through their inter-

action. Emergence is an orientation with a distinctive rhizomatic bent. Non-linear,

ahierarchical, and connective across diverse territories, it reinterprets the world as a

constant becoming.

The aim of the thesis is to render the territory open to continuous re-territorializa-

tion. It intends to break down the stiff linear boundaries between worlds to reveal

by registration of contiguity across distinct boundaries. The process of identification

is conceived to be a continuous one; a body - human or animal - might be described FIG 1.9 AGRiCULTURE WiTH OxEN

v. SYSTEMA NATURAE, PUBLiSHED iN 1735.


in terms of assemblages of constituent parts (molecules or cells or...) persisting in

time, and the same approach could hold true for populations just as it would for

geological, economic, political or other processes.

In its material and spatial portrayal of the animal kingdom as composed of atomic

entities, enclosed and separated from one another by concrete walls, bars, or

trenches, the zoo has done its audience a disservice. If, in ecology, Ernst Heackel first

advanced the primacy of the environment in shaping the individual organism, the

“umwelt” as posited by Estonian zoologist, Jakob von Uexküll, made the relationship

more complex.15 It further de-centered the world as a singular one, given to, and

oriented around, the human. It describes a sort of reciprocity between body and mi-

lieu, in which the milieu is presented to each organism only insofar as its body and

sensory organs are equipped to perceive it. The animal, then, does not “see” others

as such, so much as elements of its environment that prompt its body into action.

Insofar as ecology and the umwelt explain that bodies are perpetually re-territorial-

ized by operations of adaptation, mutualism, and coevolution, recursive reciprocal

inflections such as that of the orchid and the wasp16, illustrate models that architec-

ture has only infrequently and casually reproduced.

Each subject spins out its relationship to certain properties of things, like the silken threads of a spider, weaving them to a firm web that supports its existence.17

Contemporary zoos allege to transport their visitors to the alternate realities of

far-off worlds. Whereas the simulacra, according to Baudrillard, threatens at every

moment to replace the real – becoming corrupted, or losing its legitimacy, authen-

ticity or depth – Deleuze counters with its validation; that “reality” is an ongoing

construction, perpetually undergoing synthesis, and that it is precisely through the

act of simulation that our perceptions are transformed18. It is the covert situation

of a project within the tissues of reality by resemblance that bends it. It is in the

‘mistakes’ achieved by way of the camouflage - of the simulacrum for reality - that

locates its efficacy in replacing the real. The malevolence of this surrogacy is not

given by the deception itself, but by what it enables.

POST HUMANiSM & ECOLOGY

15. AGAMBEN, GiORGiO, THE OPEN : MAN AND ANIMAL (STANFORD: STANFORD UNivERSiTY PRESS, 2004), 40.

16. GiLLES DELEUZE, FÉLix GUATTARi, AND BRiAN MASSUMi, A THOUSAND PLATEAUS : CAPITALISM AND SCHIZOPHRENIA (LONDON: CONTiNUUM, 2004), 10-12.

17. JAKOB vON UExKULL, “A STROLL THROUGH THE WORLDS OF ANiMALS AND MEN: A PiCTURE BOOK OF iNviSiBLE WORLDS.” SEMIOTICA 89.4 (1992), ORiG. (1957): 14.

SiMULACRA

18. BRiAN MASSUMi, REALER THAN REAL: THE SIMULACRUM ACCORDING TO DELEUZE AND GUATTARI (iN COPYRiGHT, NO.1, 1987) 90-97

FIG 1.10 x-RAY OF HUMAN HANDS WiTH RFiD iMPLANTiMPLANTED CAPSULE CAN REvEAL GPS LOCATiON


The question seems to be, if architecture might be considered to be a significant

part of the human’s milieu, as distinct from the animal, what if it were to be

generated out of evolutionary processes that more closely approximated those

that operate in unmediated, adaptive, and conceptually disordered ways[and at a

different temporal scale]? The question of the animal, and what distinguishes man

from it, are far too broad and deep to enter into here but, the thesis calls for one to

step away momentarily from the humanist tradition which grants primacy to the

supposed superiority of the intellect, and imagine a world generated by embedded

material intelligence. In humankind’s attempt to re-calibrate their environment, we

might want to weigh what we forfeit in the process; in all the disease, discomfort,

and suffering that we may sublimate through artifice, we are removed, too, from the

depths of complexity, ambiguity, and wilderness. In the design for the zoo, the thesis

imagines a position that embraces and co-opts these qualities.

Descartes internalized, within man, the dualism implicit in the human relation to animals. In dividing absolutely body from soul, he bequeathed the body to the laws of physics and mechanics, and, since animals were soulless, the animal was reduced to the model of a machine.19

Written almost 20 years ago, Donna Haraway’s Cyborg Manifesto seems more pre-

scient today than ever. In her essay, she spells out three groupings of opposites,

across which border wars are enabling a new myth of the cyborg: human and

animal, human-animal and machine, and the physical and non-physical. In these

breakdowns, she sees a potential way out of the eternal struggles of the dualisms

that attend dominance - self/other, body/mind, human/animal, public/private,

man/woman, nature/culture, primitive/civilized. Cyborgs are not ruled by reproduc-

tive politics - they completely bypass the origin story in the western humanist sense

- and thus do not necessitate a return to innocent wholeness. They do not seek

unitary identity which gives rise to the aforementioned dualistic conception of the

world. Their irony is in the potential subversion of their teleology, itself rooted in

militaristic and patriarchal capitalism. On the one hand, the cyborg may be viewed

as an implementation of the grid of control across the planet. But it may just as

well “be about lived social and bodily realities in which people are not afraid of their

joint kinship with animals and machines, not afraid of permanently partial identi-


FIG 1.11 “WHAT GOES ON IN OUR HEAD WHEN WE SEE AN AUTO AND SAY ‘AUTO’”, FRiTZ KAHN, DAS LEBEN DES MENCHEN, STUTTGART, 1929


ties and contradictory standpoints.” As illegitimate offspring, the cyborg - since their

fathers are inessential - promises to be unfaithful to its origins.20

Taking cues from these texts, the zoo imagines, and takes pleasure in the collapse

of these borders. It proposes using the program of the zoo as a re-orientation device;

blurring primacy between animals and humans, and imagining machines operating

as animals, and animals replacing machines, all to breakdown the body/mind split

that continues to underpin domination.

20. HARAWAY, SIMIANS, CYBORGS, AND WOMEN, 154.

FIG 1.12 CYBERNETiC DEPiCTiON OF BALL TURRET, TiME MAGAZiNE


FIG 1.13WiM DELvOYE’S CLOACA MACHiNE, 2000

31 THE TECHNOLOGiCAL ORiENTATiONA SiMULATED MiLiEU

The computer is being used to re-enliven old, especially mythological, animals that had begun to die in the imagination.21

THE TECHNOLOGiCAL ORiENTATiONA SiMULATED MiLiEU

21. CATHERiNE iNGRAHAM, ARCHITECTURE, ANIMAL, HUMAN : THE ASYMMETRICAL CONDITION (NEW YORK: ROUTLEDGE, 2006), 326.

FIG 1.13WiM DELvOYE’S CLOACA MACHiNE, 2000


The original intention of the research was to enquire about the capacity for tech-

nique to help bridge the gap between theory and practice. Taking cues from a vast

reserve of writings and practices dealing with the role of computation in progressive

architectural practice - especially those that approximate natural systems - this

included the acquisition of literacy in programming and writing code.

In computation, the non-linear dynamic flows of biological and other natural pro-

cesses can be re-created in such a way that they operate on structural levels; shap-

ing flows and behaviours rather than attuning to the reason of the eye. Instead of

the symbolic or the narrative, both of which are employed typically from a singular

perspective, the architecture may operate by a registration of a field of difference. A

field condition has no singular story to tell. It is one waiting to be told, with various

points of entry and exit, and different ways of reading the same material.

Catherine Ingraham [as have others] outlines the “functionalist ethic” that persists

in computational work, one connected to material production in service of capital.22

On the other hand, the computer has roots in warfare and as an instrument of

political power. The computer, here, is used for the same abstraction it has afforded

these regimes but it reappropriates them to subvert the orientation which views

animals and the natural world as a standing material reserve.

The primary assumption of the thesis is that in order for emergence to act coopera-

tively in the design process (not discounting the latent merits of the emergence that

will occur over the life of project in term of its appropriation, use, and misuse), one

would have to synthesize its processes, those which involve numerous agents acting

independently in time.

Nature produces its forms by parallel process and though architecture professes

to operate in a similar way, experience in practice attests to the flimsiness of this

correspondence. What one actually encounters is an overly hierarchical top-down

system of discrete atomic processes. Feedback tends not to be reciprocated; it

moves in one direction. The behaviour of behind-the-scenes’ installations yield

to the ambitions of the visual domain or vice-versa. Computers are, by definition,

22. iNGRAHAM, ARCHITECTURE, ANIMAL, HUMAN,316.

FIG 1.14 JAPANESE ROBOT CAT, SEGA TOYS, 2006


serial machines; they are only capable of processing sequential lists of procedures.

Despite this fundamental structural limitation, there are mechanisms that allow for

the simulation of the parallel. One is the time slicing loop; time is divided into single

units that only progress after completion of an entire run of its list of procedures

and subroutines. Though each pass through a script may be sequential in itself,

to the computer, everything occurring within it is concurrent. The other essential

mechanism is buffered storage, a reserve of memory appointed to storing processes

whilst others are operating. When combined, the two allow for a single computer to

behave as if it were multiple; each devoted to its own element in a program. Certain

problems, like those of agent-based systems, exploit these mechanisms to examine

their behaviours in ways that would be impossible to do with pen and paper.23

The strength of the computer is in repetitive action. It will tirelessly execute long

lists of commands in fractions of the time it would take to perform by hand. With

the speed afforded by computation - allowing the execution of tasks that might oth-

erwise be deemed to be time consuming beyond feasible human persistence or de-

votion - also, and perhaps, more significantly, comes a qualitative change in what is

possible. Additionally, their structural logic of modular subroutines eases our ability

to dissect them in search of the source of qualities, be they desirable or not. Eventu-

ally, with refinement, we can build mathematical theory around these functions.

The experimentation of simulating natural processes, particularly that of Artifi-

cial Life, can be seen as a halfway point between theory and practice. Though not

directly operating on matter or what we might call “reality” itself, in a surrogate en-

vironment, computational experiments allow searches and explorations within the

vast realm of the possible. And still, they are rigorous if they are nothing else. There

is no ambiguity about the relationship between the definition of the model and the

results it will generate. These will hold as true only for the particular settings used

when it is executed, and due to this required rigour, it enforces discipline. As John

Holland - preeminent researcher into complex systems, and inventor of the genetic

algorithm - puts it, “no amount of clever rhetoric or wishful thinking will cause a

computer to deviate one iota from the consequences of the rules it embodies.”24

23. STEvE GRAND, CREATION : LIFE AND HOW TO MAKE IT (CAMBRiDGE: HARvARD UNivERSiTY PRESS, 2001), 106.

24. JOHN H. HOLLAND, EMERGENCE : FROM CHAOS TO ORDER (READiNG: ADDiSON-WESLEY, 1998), 121.

FIG 1.15 BASiC CONTROL STRUCTURES


FIG 1.16BOiDS ALGORiTHM DiAGRAMS


Object Oriented Programming (OOP) is an orientation to computer programming

that instructs the behaviour of objects in genotypic code. These rules are embedded

in classes, each one serving a distinct type of object. Classes are identified by names

and instantiated by constructors. They can be given fields to carry variables and

methods describing their behaviour in time; they are templates for indeterminate

form spelling out how each object relates to its environment, and does not explicitly

give form. Conceived as being separate from, but potentially responsive to, their

environment, OOP is instrumental in exploring and simulating multi agent-based

systems.

Multi-agent based systems provide a way to illustrate emergence; objects with

simple rule sets interacting with one another to generate complex wholes. The

Boids algorithm, written by Craig Reynolds, demonstrates how, given a basic class

with only three simple methods – cohesion, separation, and orientation - its objects

can very easily reproduce the flocking behaviours exhibited in the uncoordinated

behaviour of various species.

Incidentally, many introductions to OOP concepts use analogies of zoos and ani-

mals. Structured around ideas of type and taxonomy, natural order appears to help

initiates with the logic of classes, inheritance, and permutation. These examples are

thoroughly imbricated with an essentialist conception of the world where “object”-

ness is transcendental. Rather than viewing combination as a tactic by which to

generate divergent possibilities, somehow these examples appear to miss the poten-

tial to move beyond the confines of type.

Computer based models greatly expand our opportunities for using intuition. Ex-

panding on the informality of napkin sketches, they allow one to entertain ‘what if’

scenarios but allow too for the inclusion of metric details that would otherwise in-

fringe on the immediacy of the napkin. Of course with this capacity comes a greater

responsibility and need for attentiveness to only those details that matter. Too

much detail defeats the purposes of the model, but this danger does not foreclose

its possibilities.

OOP

FIG 1.16BOiDS ALGORiTHM DiAGRAMS


FIG 1.17AERiAL PHOTOGRAPH OF LAKEviEW GS

39 LAKEviEW MiSSiSSAUGATHE TESTiNG GROUND

Late twentieth-century machines have made thoroughly ambiguous the difference between natural and artificial, mind and body, self-developing and externally designed, and many other distinctions that used to apply to organisms and machines. Our machines are disturbingly lively, and we ourselves frighteningly inert.25

LAKEviEW MiSSiSSAUGATHE TESTiNG GROUND

25. HARAWAY, SIMIANS, CYBORGS, AND WOMEN, 152.

FIG 1.17AERiAL PHOTOGRAPH OF LAKEviEW GS


FIG 1.18 •MAP OF SOUTHERN ONTARiO’S GOLDEN HORSESHOE

FIG 1.19 •SiTE CONTExT

41 LAKEviEW MiSSiSSAUGATHE TESTiNG GROUND

Zoos, by their very definition, are places not about the local, but the foreign. They

house life forms alien to their context, and in so doing, they bow in varying degrees

to unfamiliar pressures. Their orientation is inward, towards transporting their

visitors to remote worlds with completely different landscapes. They are thus allied

with the simulacra, and their relationship to their immediate context is a tenuous

one. The thesis imagines the zoo as a program embodied primarily by a set of rules

governing adaptive behaviour, and thus not bound to any one specific site. For the

purposes of demonstration, Lakeview is used as a prototypical condition, whose

situation might be well-suited to host a zoo.

The Lakeview area is located along the northern shores of Lake Ontario, halfway

between the urban cores of Toronto and Hamilton. The area, on Mississauga’s

eastern border, is bound roughly by the Cooksville Creek at its western edge and the

Etobicoke Creek at its eastern edge. It is just east of Port Credit and was amalgam-

ated into the Town of Mississauga in 1968.26

The community has deep roots in industry and the military. Its primary genus loci

comes from its situation along the waterfront, outside the city, but within reach

of a number of large civic centres. Its environment and scenery, however, might be

viewed as the vague terrain of an environment long polluted by large industrial

facilities amongst which include a large operating coal fired power plant, a waste-

water sewage treatment facility, a water treatment plant as well as a significant

stretch of light industrial grounds. These programs have created a disconnect from

the water’s edge, as well as an affront to the environment, whether in their dis-

regard for the ambient streetscape (driven by the logistics of trailer trucks) or the

smells emitting from the sewage facility.

But before there was industry, there was military. In fact, the area is remembered as

the place where Canada’s aviation history began. In the early part of the 20th centu-

ry, Canada’s first airport was located on what was, until the recent past, the grounds

of the Lakeview Generating Station. It was there that Canada’s air force was first

trained in aerial warfare, and it later provided launch points for the air force’s entry

into WWII. An armoury, sited at the eastern end of Lakeview, served various military

A PROTOTYPiCAL URBAN CONDiTiON

GEOGRAPHY

26. HTTP://WWW.LAKEviEWRESiDENTS.COM/

iNDUSTRY

MiLiTARY

FIG 1.18 •MAP OF SOUTHERN ONTARiO’S GOLDEN HORSESHOE

FIG 1.19 •SiTE CONTExT


FIG 1.20viEWS OF THE FOUR SiSTERS BEiNG DEMOLiSHED iN 2006.

43 LAKEviEW MiSSiSSAUGACURRENT STATUS

functions including weapons manufacture and storage, training and barracks. Large

quantities of armament were produced at the site during the war.

Prior to European settlement, the land witnessed a perpetual succession of owner-

ship with invasions and counter-invasions by numerous native groups. In time,

most of the Credit Mississauga’s land was lost during the Mississauga purchase but

they maintained a small reserve flanking the shores of the Credit River. Industrially

polluted river water caused the group illness and disease, which eventually forced

them to flee these lands. In the 19th century, farm communities settled along Lake-

shore road and along each of the rivers, creeks and roads it crossed. With this wave

of development, the original forest cover was virtually wiped clear.27

Today, Lakeview is in a liminal state. A major step in the site’s evolution arrived

with the long-awaited demolition of the ‘Four Sisters’ of Lakeview Generating Sta-

tion in June of 2006. The coal-fired power plant had been a landmark for the area,

with each of its four smokestacks standing at almost 500 feet tall, dwarfing its

neighbouring light-industrial and low-density residential sprawl. Its power supply

once supplied about 17 per cent of Ontario’s electricity needs.28

Plans by the City of Mississauga are underway to revitalize nearby Marie Curtis Park

at Lakeview’s easternmost edge. With the remediation of contaminated soil at the

former site of the Arsenal lands, the existing park space has expanded its presence

westward. With the demolition of the power station, and the announcement that

the Ontario government had no intentions of replacing the plant with a new gas-

fired generator, much speculation on the future of the area has ensued.

Amongst the visions for the now vacant Generating Station lands are a new rec-

reational waterfront to be developed in conjunction with a larger renovation of

land use from light industrial to mixed live-work spaces. The Lakeview Ratepay-

ers Association has envisioned a scheme for the land that includes - in addition

to residential, commercial, and institutional uses - a “destination” waterfront area

with trails, parks, and even major tourist attractions that might include a stadium,

an aquarium, and a pier with more recreational attractions such as a Ferris wheel.

PRE-COLONiALiZATiON

27. HTTP://WWW.LAKEviEWRESiDENTS.COM

CURRENT STATUS

28. [BROCHURE_LAKEviEWRETRO.PDF] [HTTP://WAPEDiA.MOBi/EN/LAKEviEW,_MiSSiSSAUGA> ]

A NEW DESTiNATiON

FIG 1.20viEWS OF THE FOUR SiSTERS BEiNG DEMOLiSHED iN 2006.

http://www.lakeviewresidents.com/index.php?page=history-general&PHPSESSID=54642605e537d9c889b66b5455178b1c

http://wapedia.mobi/en/Lakeview,_Mississauga

http://wapedia.mobi/en/Lakeview,_Mississauga

45 LAKEviEW MiSSiSSAUGAFERTiLiTY

Remarkably, during a charette process, one resident enquired into the possibil-

ity of housing a new zoo, which prompted a collage of the Metro Toronto Zoo onto

the site. The single most consistent aspect of all prospective plans is centred on a

renewed relationship between residents and the lake’s edge.

For the purposes of the thesis exploration, the site operates on a number of levels.

Between its location and connectedness, existing landscape elements, available

land, and adjacency to industry, a number of qualities lay in the site that identify it

as an ideal prototypical condition for a zoo.

At just over 200 hectares, the site provides a generous amount of space for a zoo

program while not earning the distinction – as Metro Toronto Zoo has – of being

the largest zoo in the world. The first modern zoos, sited within the density of their

respective urban cores, tend to be a quarter of the size at roughly 50 hectares each.

Lakeview’s land provides a happy medium between the gluttonous excesses of the

zoo set within a pastoral landscape, and that of the oppressive enclosures of the zoo

set within a dense urban core.

Partway along the Golden Horseshoe, between Hamilton and Toronto, the site is eas-

ily accessed by a population of 8 million potential visitors. It sits just south of the

Lakeshore Boulevard West, a major thoroughfare, connected nearby to the Queen-

sway Express Highway nearby. Regional rail connections are also provided for by

nearby Long Branch Go Station. Local connections include Mississauga Bus routes

and biking trails. The redevelopment plans for the area have also called for an

extension of Toronto’s Lakeshore LRT route to extend into Mississauga, which now

stops nearby at Long Branch.

The site’s primary distinguishing feature is its large stretch of shoreline, enabling an

unmediated edge where its architecture and the surrounding landscape can be wed

seamlessly together. Many of the zoo’s species and exhibits require water facilities;

and these installations can suggest extensions of the lake. Even before plans for the

area’s rejuvenation start to be put in place, the site has the seeds of several distinct

landscape conditions. These latencies allow the zoo to covertly slip into place; its

FERTiLiTY

FIG 1.21 iLLUSTRATiON OF HiSTORiCAL DEvELOPMENT OF ZOOSSTUDY OF NOTABLE PRECEDENTS iN SCALE & CONTExT ; EACH ZOO iS LOCATED GEOGRAPHiCALLY AND iN TiME


FIG 1.22PANORAMA OF LAKESHORE RD.

FIG 1.23METRO TORONTO ZOO SUPERiMPOSED ON LAKEviEW GS.

47 LAKEviEW MiSSiSSAUGAFERTiLiTY

body addressing its environment by amplification and extension of existing condi-

tions.

The most striking symbolic potential of the site, however, is in the now adjacent and

soon to be, memory of the site’s industrial and military past. This operates on two

levels concurrently; on the one hand, there is the notion of the constructed land-

scape, a land so tampered with that it has been stripped of any possible sense of

sacred ground to be preserved or reconstructed- sanctifying further manipulation,

this time in pursuit of the replication of ‘nature’. Concomitant is the site’s place in

the cultural imagination of local residents, which, due to the restricted, dangerous

nature of the, now defunct, power plant, has long been uninhabited. The vacuum

this has created is just the right quality to engender radical visions for the future.

With the choice of grounds retrieved from land blighted by large industry, there is

also the implicit intent to superimpose the two programs - habitat/recreation &

industrial - on one another. Berger has noted that the marginalization of animals

appeared just as they disappeared from our daily lives, and that this coincided

with the growth of industry and mechanization. In supplanting the industrial with

natural living systems, the move provides for the generation of fertile, evocative

imagery to spark discourse on society, and in particular, our relationship with our

closest living relatives.

FIG 1.22PANORAMA OF LAKESHORE RD.

FIG 1.23METRO TORONTO ZOO SUPERiMPOSED ON LAKEviEW GS.

49

CHAPTER 2 CONSTRUCTiNG THE ZOO MACHiNEAN ExPOSiTiON OF PROCESS

50CONSTRUCTiNG THE ZOO MACHiNE

An abstract machine in itself is not physical or corporeal, any more than it is semiotic; it is diagrammatic (it knows nothing of the distinctions between the artificial and the natural either). It operates by matter, not by substance; by function, not by form.[...] The abstract machine is pure Matter-Function - a diagram independent of the forms and substances, expressions and contents it will distribute.29

This chapter breaks down the architectural process into a series of mechanical

procedures and explains each in terms of both its intent and its conscientious as-

sembly. The separation into distinct sections, though counter to the indivisibility

and simultaneity of parallel processes, serves two purposes. First, it is for the sake

of clarity and economy. Second, it parallels the actual development of the project.

The aggregate design of the zoo should still be considered as a single emergent

product of parallel processes operating and feeding back onto each other recursive-

ly. In fact, it is this treatment of the design process - as an ecology in and of itself

- that the thesis aspires to.

Each section within the chapter is dedicated to one generative or “abstract ma-

chine"30 which has been implemented specifically as it relates to or performs for

the particularities of the zoo design; but it could just as well be separated out as

a so-called “design pattern” – to be re-purposed or hacked into for completely un-

related scenarios. Each has its own lineage regarding phenomena from which it has

sampled, though there are reoccurring themes and overlaps amongst them. Each

renders its initial assumptions open so that they may be subsequently interrogated,

particularly as it might relate to decisions about composition and the order of its

events.

Several tropes might be recalled in this work that have become all-too familiar

within contemporary cultural practice - some that may seem exhausted of potential

by now - but here, nowhere is there a suggestion of uncovering untapped phenom-

ena. These processes have not been selected for their symbolic resonance, nor for

their shape as signs, but for the structural qualities they have in shaping space.

The aspiration is to take explicit procedural steps that address programmatic

requirements, and couple them together to produce a space that is indetermin-

29. DELEUZE, GUATTARi, & MASSUMi, A THOUSAND PLATEAUS, 141.

30. iBiD., 142.

51 iNTRODUCTiON

iNTRODUCTiON

A MEASURED LOSS OF CONTROL

FIG 2.24 PROCESSUAL OvERviEWS

ate. The desirable spatial effects common amongst them - ambiguous boundaries,

territorializing without closing off, fuzzy edges, continuous, overlapping surfaces -

are imagined to be everywhere, all the time, but in different levels of intensity. The

resultant atmosphere, one which the project actively cultivates and is in control

of – but, of course, only to a limited degree - is one of disorientation, ambiguity, and

open to continuous interpretation.

All the phases should be (empirical) machines in themselves. Machines connect only to each other, as molecules, which means the phases in a process need to be steps in a procedure. Finding rules. But when we use step-wise procedures does it not mean that the whole procedure is fixed in advance? Generally, ‘procedure’ is used in a deterministic, mechanical manner, but perhaps we should consider procedures as mechanical blocks that in themselves are straightforward and linear but that when linked can form nonlinear strings. The blocks have complex edges, multiple hinges and many ways of connecting to create a complex chain of techniques. It is path-dependent, like cooking: it works with techniques and recipes, but what actually comes out is not fully predictable.3131. LARS SPUYBROEK, NOX : MACHINING ARCHITECTURE,

(NEW YORK: THAMES & HUDSON, 2004), 8.

53 iNTRODUCTiON

FIG 2.25 DRAGONFLY WiNGS

The organizational and informational stage is material. Not immaterial as is so often put forth. It is the material potential. The material’s distributed intelligence that sets the machine in motion.32

32. LARS SPUYBROEK, "THE STRUCTURE OF vAGUENESS", NOX : MACHINING ARCHITECTURE (2004): 354.

55 PROGRAM DEFiNiTiON

PROGRAM DEFiNiTiON

MAPPiNG OUT THE BODY

FIG 2.26 PROCESSUAL FLOWSANiMAL DATABASE


COMMON NAME

Southern TamanduaSCiENTiFiC NAME

Tamandua tetradactyla

MO

RPH

OLO

GY

ORDER

PilosaCLASS

MammaliaBODY COvER

FurWEiGHT

10LENGTH

28

HA

BiT

AT CONTiNENT

South AmericaBiOME

RainforestTEMPERATURE

HotHUMiDiTY

WetCOvER

75;15;0;0;0;10

BEH

Av

iOU

R

ELEMENTAL ADAPTATiON

TreesDiURNAL CYCLE

nocturnalDiET

insectivoreiNTERACTiviTY

solitaryMOTiLiTY

sedentary

COMMON NAME

Short-Beaked Echidna

SCiENTiFiC NAME

Tachyglossus aculeatus

MO

RPH

OLO

GY

ORDER

MonotremataCLASS

MammaliaBODY COvER

fur & spinesWEiGHT

10LENGTH

15

HA

BiT

AT CONTiNENT

AustraliaBiOME

ForestTEMPERATURE

temperateHUMiDiTY

mixedCOvER

15;50;0;35;0;0

BEH

Av

iOU

R


undergroundDiURNAL CYCLE

diurnalDiET

insectivoreiNTERACTiviTY

solitaryMOTiLiTY

motile

COMMON NAME

CapybaraSCiENTiFiC NAME

Capybara

MO

RPH

OLO

GY

ORDER

RodentiaCLASS

MammaliaBODY COvER

furWEiGHT

100LENGTH

48

HA

BiT

AT CONTiNENT

South AmericaBiOME

WetlandsTEMPERATURE

hotHUMiDiTY

mixedCOvER

0;30;0;0;0;70

BEH

Av

iOU

R


waterDiURNAL CYCLE

crepuscularDiET

herbivoreiNTERACTiviTY

socialMOTiLiTY

sedentary

COMMON NAME

Zebra DuikerSCiENTiFiC NAME

Cephalophus zebra

MO

RPH

OLO

GY

ORDER

ArtiodactylaCLASS

MammaliaBODY COvER

furWEiGHT

38LENGTH

32

HA

BiT

AT CONTiNENT

AfricaBiOME

ForestTEMPERATURE

hotHUMiDiTY

mixedCOvER

75;25;0;0;0;0

BEH

Av

iOU

R


groundDiURNAL CYCLE

diurnalDiET


solitaryMOTiLiTY

motile

COMMON NAME

Mouse DeerSCiENTiFiC NAME

Tragulus napu

MO

RPH

OLO

GY

ORDER

ArtiodactylaCLASS

MammaliaBODY COvER

furWEiGHT

14LENGTH

30

HA

BiT

AT CONTiNENT

AsiaBiOME

RainforestTEMPERATURE

hotHUMiDiTY

wetCOvER

65;15;0;5;0;15

BEH

Av

iOU

R


groundDiURNAL CYCLE

nocturnalDiET


solitaryMOTiLiTY

sedentary

COMMON NAME

OkapiSCiENTiFiC NAME

Okapia johnstoni

MO

RPH

OLO

GY

ORDER

ArtiodactylaCLASS

MammaliaBODY COvER

furWEiGHT

550LENGTH

98

HA

BiT

AT CONTiNENT

South AfricaBiOME

Tropical RainforestTEMPERATURE

hotHUMiDiTY

wetCOvER

75;15;0;0;0;10

BEH

Av

iOU

R


groundDiURNAL CYCLE

diurnalDiET


socialMOTiLiTY

sedentary

This portion of the project investigates its primary subject- the wildlife to be ex-

hibited at the zoo. Its design intends to transgress the systematic and mechanical

classification of its biological specimens; in this phase, however, the process ap-

proaches its subject with just such an orientation. The intention is to transpose the

world of matter and encode it into a numerical dataset. The numbers themselves

are vehicles to break material out of the confines of their cultural constructions

by way of abstraction. It is in this format that they are amenable to the number

manipulation of computational processes. This initial section begins by detailing

the process of acquiring the data, followed by its processing into seeds to instigate

later generate processes. As a component of conventional architectural practice, its

equivalences and correspondences opportunistically exploit its dilettante status to

create a viable model to map the world.

This step in the process imagines using the very logic of scientific enframing but its

intent is subversion. Through its abstractions and conceptual delaminations, the

model aspires to describe identity by the inclusivity of features and their combina-

tion, and thus, allowing connections and associations across boundaries that typ-

ically confine types. By capturing their essence in numerical data, the animals are

conceived to be notionally liberated from the trappings of cultural packaging and

open to recursive cultural reconstruction.

It begins with the assumption that it, like most zoos, is principally about offering

its visitors views into animal worlds, the likes of which might be impossible in any

other scenario. In the spirit of this zoo-going experience - and with the desire to

explore overlap and combination between cultural opposites - it demands that the

broadest variety of habitats and the fullest diversity of terrestrial species be ac-

counted for. The number and diversity of species from each major terrestrial biome

approximates the proportion between the amount of biomass it sustains and its

surface area.

In order to affirm the zoo as a new type of chimeric entity, species have been chosen

that, for different reasons, draw attention to the evolutionary production of novelty

through combination and hybridization. They are selected as a function of their

CHOiCE OF ANiMALS


FIG 2.27 MULTiPLE ANiMAL viEWSDiFFERENT ExHiBiTiON DiMENSiONS ATTACHED TO EACH ANiMAL


FIG 2.28 ACTivE MEDiUMS AND BEHAviOURSADAPTATiON TO MATERiAL/ SPATiAL CONDiTiON

perceived capacity to undermine the cultural associations drawn between taxonomy, typ-

ically based on genetic traits, and identity, made manifest in behaviour or event. For example,

where some of the largest species of the class of the feline or canine classes might typically

indicate an example of an ecosystem’s predatory apex, such as lions or wolves, the program

instead opts for those species that are taxonomically related but may be considered to be less

caricatured, such as caracals and fennet foxes. On the other hand, predatory species from

classes not typically associated with such qualities are preferred, such as those belonging to

amphibians or marsupials.

In the same spirit, species have also been given preference when they appear to lay along a

morphological continuum between ends typically perceived as distinct. Examples of such

species are often identified with names that compound two species such as the Mouse Deer

(a very small artiodactyl), the Zebra Duiker (a bovid with striped hide), and the Golden Lion

Tamarin (a primate with an elaborate mane). Likewise, nature, in its indiscriminate pro-

cesses, does not “see” the borders inscribed by human geography; animals have evolved in

the wild with an attunement to the particularities of their environment without respect to

political boundaries. Distinct taxonomic (and genetic) types have been shown to converge

through evolutionary processes on similar morphological traits corresponding to their shared

ecological niches. These inherent tendencies attest to the possibility that there may be a

limited reserve of operations from which nature generates its perpetual novelty. Some forms,

it seems, may be better suited to deal with certain situations.

The most general information about each species is manually assembled from a variety of

internet based resources. Data about each species typically summarizes its population in

singular values representing the breadth of its spectrum; in the cases of body sizes, statistical

averages are employed while behaviours are described in terms of one of several possible

types. Standardized information about each species can be contentious and sometimes dif-

ficult to acquire. Specifications regarding holding animals in captivity are far scarcer, and

the few that are published by zoological licensing bodies often do not satisfy what animal

welfare advocates believe to constitute sufficient spatial parameters.

The assumptions of population are diametrically opposed to those of the typologist. The populationist stresses the uniqueness of everything in the organic world. What is true for the human species, that no two individuals are alike, is equally true for

HYBRiD TYPES

EvOLUTiONARY CONvERGENCE

ACQUiRiNG THE DATA


FIG 2.28 ACTivE MEDiUMS AND BEHAviOURSADAPTATiON TO MATERiAL/ SPATiAL CONDiTiON

all other species of animals and plants. [...] All organisms and organic phenomena are composed of unique features and can be described collectively only in statistical terms. Individuals, or any kind of organic entities, form populations of which we can determine the arithmetic mean and the statistics of variation. Averages are merely statistical abstractions; only the individuals of which the populations are composed have any reality. The ultimate conclusions of the population thinker and the typologist are precisely the opposite. For the typologist, the type (eidos) is real and the variation an illusion, while for the populationist the type (average) is an abstraction and only the variation is real. No two ways of looking at nature could be more different. 33

This first stage - one of acquiring and parsing the data - is about a negotiation between

the individual and the collective. Perpetually under revision, and with an ever-increasing

specificity, the task of amassing programmatic information underscores issues related to the

fragility and limits of attempting to describe populations in terms of averages. As a mapping

procedure, it contends with how much detail may be dropped and still maintain a useful

resemblance to its source. It pursues the development of a model that has, at once, both bet-

ter resolved correspondences to the territory it represents, and a rhizomatic disembodiment,

such that it might be open to reinterpretation.

33. ERNST MAYR, (1970) SOURCE: HTTP://HOMEPAGES.WHiCH.NET/~GK.SHERMAN/BAAAAACU.HTM


FIG 2.29 GRAPH OF COLLECTiON EACH ANiMAL iS REPRESENTED BY A BAND; EACH COLUMN iS AN iNDEx OF A PARTiCULAR DiMENSiON

The data is intended to operate as a seed, to stimulate the adaptive response in successive

layers of a generative design process; its raw information must undergo its own processing.

So that it may be amenable to these processes, the data is formatted largely in terms of num-

erical strings in varying degrees of specificity.

Aside from the common and scientific names given to animals - already compound identi-

ties - as strings to help manage information farther along in the process, the inputs typically

come in one of three primary kinds, each with increasing resolution, and less generaliza-

tion. First, booleans - simple binary statements, zero or one - indicate each animal as either

belonging or not. Examples of this are : social dynamics, and sedentary lifestyle. Strings or

integers they represent - whole numbers - indicate attributes such as diet type, primary

adaptive behaviour, climate attributes, and diurnal cycles. Floating point values are used for

more precise variables, with a greater range of inputs, such as body weight, body length and

enclosure areas.

The list of selected animals is a tentative one; conceived in parallel alongside the others as a

representational system, it models any species of animal through a number of key identify-

ing values. The final list includes ninety distinct species, which represent the full breadth

of animal life on the planet. The terms used to describe the species have undergone several

iterations; as the model was continuously reworked as an appropriate means to act as inputs

to subsequent procedures. Early versions included information such as gestations periods, as

well as descriptors that were already culturally packaged types – such as named ecosystems,

geographical regions - but these were later deemed to be extraneous. In order to reinforce

lateral associations, compound entities were delaminated into only the most essential.

The assembly of the list was always done with the sense that at any stage during the process

the specific animals as chosen could be swapped out with others or removed from the list al-

together without the breakdown of the system. From the outset of the project those animals

that were chosen were deemed to not be important so much as the qualities or matter that

they are vehicles for. The project eschews any sense of a perfect collection of species, rather

it is its ability to respond and adapt to any list thereof that is prioritized.

Early visualizations generated from the database involved processes of shuffling so as it

THE DATABASE: A SEED

COLLECTiON OvERviEW

A TENTATivE LiST


FIG 2.30 MOviNG FROM TREE TO RHiZOMEREORGANiZATiON OF THE SAME COLLECTiON BY DiFFERENT CRiTERiA


FIG 2.31 ANiMAL DiFFERENCE TABLESEACH ANiMAL iS DONNED A SERiAL CODE WHiCH DESCRiBES iT ACCORDiNG TO A NUMBER OF KEY iDENTiFiERS; THE iNTERSECTiON OF EvERY POSSiBLE PAiRiNG PRODUCES A TRAvEL-LiKE TABLE THAT ATTEMPTS TO DESCRiBE PROxiMiTYna

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might enable different kinds of seeing. They are an attempt to contend with how

we might look with ambivalence at the same dense world of information, and how

the shifts, which inherently necessitate movement in time, might become fixed

spatially. The static diagrams required a striation such that the different possible

orientations would maintain their clarity. Along the vertical axis, the same collec-

tion is sorted into different groupings, each species acting as a virtual portal into a

different orientation.

One attempt to model the collection consists of a pseudo-genetic code that might

encompass all of its key traits, be they about morphology, habitat, or behaviour.

Each species is described by a ten digit number which is generated through the

concatenation of database keys. Each portion within the concatenated string

designates a key trait, its number corresponding to that animal’s particularity. With

each animal described in part by their number, the degree of difference from one to

another could be notionally described by a numerical difference between the two.

The smaller the numbers are that define the relationship, the closer their similarity

is. What the system allows, is an equivalence between types of similarity such that

this could arise from shared behavioural trait just as much as it could come from

shared habitat or morphology. The figures might be used to drive the proportional

lengths of edges in a network graph, describing relatedness in spatial terms.


FIG 2.31 ANiMAL DiFFERENCE TABLESEACH ANiMAL iS DONNED A SERiAL CODE WHiCH DESCRiBES iT ACCORDiNG TO A NUMBER OF KEY iDENTiFiERS; THE iNTERSECTiON OF EvERY POSSiBLE PAiRiNG PRODUCES A TRAvEL-LiKE TABLE THAT ATTEMPTS TO DESCRiBE PROxiMiTY

The challenge that the virtual poses for architecture lies more in its unform nature than its abstractness. How can the run of the unform be integrated into a process whose end is still-standing form? The answer for many has been: topology.34

ENviRONMENTAL CALiBRATiON

SiTUATiNG THE BODY

34. MASSUMi, BRiAN. “SENSiNG THE viRTUAL, BUiLDiNG THE iNSENSiBLE.” ARCHITECTURAL DESIGN 68 (1998): 16.

67 ENviRONMENTAL CALiBRATiON

FIG 2.32 PROCESSUAL FLOWSSiTE PLANNiNG & ExHiBiTiON ORGANiZATiON

This section describes the construction of a generative machine which is tasked

with the arrangement of exhibits on a site. Its point of departure are a series of

landscape traits that are registered and which subsequently bear on exhibits to

which they correspond. The process involves the embodiment of information from

the database into agents within a simulation. Upon each run, the system generates

new lists of exhibit positions.

Behind this procedure, lies aspirations to both convey the restlessness of ecology

as a dynamic system as well as perform as a support for multiple narratives using

proximity - and landscape - as a barometer of relatedness. The collection is con-

ceived as nothing but a topological body, underpinned, and made coherent not by

shape but by the relationships or connections between points on its body.

The employed method samples from the force directed layout algorithms incor-

porated into interactive network graphs. These graphs work as graphic interfaces

to explore vast data sets and their relationships, and have become prevalent with

the rise of information technologies and genomic research. They are interactive,

allowing users to shift the graph's focus by selecting nodes around which the graph

will self-organize; it is in their animate behaviour that the dense virtual space of

information becomes accessible. The graph’s spatial metaphor is an abstraction,

drawn on-screen as a network of nodes and lines. An algorithm underlying the sys-

tem is embedded with the rules of Newtonian physics, whereby each body or node

is impelled to move by elastic forces of springs and attractive and repellent forces

of nearby particles. The system defines relationships, but not shape or form; it

ensures that distances between nodes are maintained, but that their positions and

movement are not made explicit. Over the course of thesis research, this particular

line of inquiry started to appear in architectural research. Recent projects by thesis

students at the Hyperbody Group at Delft TU35 and research by Proxyarch36 have

used these force-directed graphs to migrate from the virtual space of information in

architecture - the program - to a material one..

What this discussion is partially circling, between ideas of points and relations,

non-linearity, and machines, is the concept of topology. Topology is concerned with

CONTExTUALiSiNG THE GRAPH APPROACH

35. KAS OOSTERHUiS AND LUKAS FEiREiSS, THE ARCHITECTURE CO-LABORATORY : GAMESETANDMATCH II : ON COMPUTER GAMES, ADVANCED GEOMETRIES, AND DIGITAL TECHNOLOGIES (ROTTERDAM: EPiSODE PUBLiSHERS, 2006). 36. WWW.PROxYARCH.COM


FIG 2.33 FORCE DiRECTED LAYOUTTiME-ELAPSED iMAGE OF SiMULATiON

the study of spatial qualities of objects which persist or remain intact even as they

undergo certain kinds of transformation. These qualities are typically defined not by

metric space, as in their dimensions, size or shape, but as consequences of internal

relationships or connectivity. In topological space, a coffee mug is equivalent to the

doughnut it can be stretched into, by transformations that do not cut or tear its

surface. Objects are defined by broad sets of principles under which variants in its

shape or form do not disturb its constitution.37.

Likewise, the zoo is approached as a topological entity. The locii of landscape

features are conceived as notional neighbourhood or districts in space which are

connected to, and inflect the positioning of the animals’ exhibits. They register

“rapidities and slowness” 38. The zoo, and its identity subsist despite the variants to

be actuated by the simulation of forces; an approach which allows for both indeter-

minacy and constraint.

Like the steering based system of the Boids algorithm39, the Force Directed Layout

algorithm behind these simulations is driven by a function which accumulate

the forces, given by vectors, on each time slice. Unlike Boids, which are encoded

with rules that require them to seek out targeted objectives, the particles in these

systems, blindly seek out moments of homeostasis. On each time-slicing loop, every

particle accumulates and resolves the conflicts between opposing forces. These

simulations are about describing a dynamic entity that is both one and many.

Landscapes might be described as the physical embodiments of dynamic systems,

the material residue of unorchestrated events unfolding in time. Like clouds of air-

borne moisture they are better described by measures of intensity than by extensiv-

ity of metric space. The site at Lakeview makes the transition into the virtual space

of the simulation via spatial anchorings as approximations of spatial loci, steered by

intuition. Beginning from guesses in a feed-forward operation, they exploit the met-

ric indeterminacy of topological space. The features or qualities that these points

refer to, may be either linear or with depth; the intention is not to suggest the em-

bodiment of landscape element in a virtual point, but instead to locate the centre of

a control mechanism. These controls will trigger the spatial arrangement of exhibits

37. MANUEL DE LANDA, INTENSIVE SCIENCE AND VIRTUAL PHILOSOPHY (NEW YORK: CONTiNUUM, 2002) 14.

FUZZiNESS

38. MANUEL DE LANDA, "MATERiALiTY: ANExACT AND iNTENSE", NOX : MACHINING ARCHITECTURE (2004): 371.

39. HTTP://WWW.RED3D.COM/CWR/BOiDS/

HOW THE SiTE BECOMES DATA


FIG 2.34 viSUALiZATiON OF A SOCiAL NETWORKiMAGE BY JEFFREY HEER HTTP://WWW.CS.BERKELEY.EDU/~JHEER/SOCiALNET/SOCiALNET_EDGES.PNG

in order to extend existing conditions into, and through the site. The feedback re-

ceived by “playing” with the positions of these anchors engenders further precision

in their positioning. Like the controls of a marionette doll, it is from these initial

instruments that the zoos orchestration is directed; unlike the marionette, however,

the zoo does not allege to resemble behaviour or any thing that is pre-existing.

The first anchors are conceived as two axes along which the climatological qualities

of temperature and humidity are charted. To the immediate north of the Lakeview

GS grounds, lay playing fields that form a buffer between the zoo and the single

storey light industrial land. To the West, lay a string of small parks and a marina,

each of which is equipped with extensive paved parking lots. These “dry” conditions

are set as counterpoint to the “wet” edge at lake-side. To the east, the wastewater

treatment plant extends from Lakeshore Road to the lakefront, without interrup-

tion. “Hot” and “cold” anchors are approximately oriented to perpendicular to the

moisture axis.

Following these primary anchors, landscape features are amplified upon the site.

Beyond the wastewater treatment facility, on what used to be the Arsenal Lands, lies

Marie Curtis Park. A “forest” anchor corresponds to an old existing woodlot within

the park that migrates westward along the northern edge of wastewater treatment

plant. The forested area is identified, by a survey conducted in 2003, to be con-

taining mature Carolinian specimens of tree and ground flora, features which have

become rare within Southern Ontario.40 “Wetlands” (distinct from the “wet” indicat-

ing a humid quality) are positioned within the lake and “grasslands” within the

sprawling grass lawn of the playing fields. A “mountain” anchor, bearing no resem-

blance to an existing feature, is positioned at the zoo’s northern interface.

The anchors act as tethering devices and the system is conceived in terms of

generic conditions rather than the specific ones found at Lakeview. The anchors’

positions are designated explicitly but one might just as easily imagine that these

positions might themselves be modified by feedback from the system. They operate

to bear on the behaviour of particles representing exhibits, more than acting as

precise locations; acknowledging this profane relationship, the simulation could

40. TORONTO AND REGiON CONSERvATiON. ARSENAL LANDS MASTER PLAN ADDENDUM, 2007.

FIG 2.35 AxONOMETRiC OF SiTE ATTRACTORSEACH POiNT REFERS TO A DiFFERENT DiSTiNCT LANDSCAPE CONDiTiON WHiCH SUBSEQUENTLY PULLS ON CORRESPONDiNG ExHiBiTS


begin to adjust these positions to suit better inter-exhibit configurations. The system

is designed such that the zoo collection might be deployed on a series of different

sites, with different contexts, and it would be through the anchoring mechanisms,

that the program would bind to the particularities of the local.

Before digressing deeper into the mechanics of the simulation it might be wise to

reaffirm that its intent is not to reproduce a behaviour seen in the animals or the

different habitats themselves. This is wholly about the development of a tool, toy

or machine, one to assist and augment the design process. There is nothing in it

that demands to be considered as real or true, only what works spatially to varying

degrees.

FIG 2.36 ANiMAL ExHiBiTSSiTED WiTH CLiMATiC ANCHORS


The approach to the site was developed in conjunction with the development of a

particle system based simulation developed in Processing. A physics based force

directed layout algorithm ensures spacing between nodes and enforce connections

between them. Values are given to the edges between related objects and con-

sequently a constellation of ideas, objects or people can be expressed as a spatial

concept that is navigable. The simulation that was developed to address the site

likewise intends to express the relationship between species and the landscape in

terms of forces which inflect and direct each exhibit centre’s position.

The final iteration of the simulation implements a version of the physics library by

Jeffrey Traervi, modified and recompiled to improve its performance. The particle

system simulates the physics of Newtonian forces where the behaviour of moving

bodies is described as a function of each objects mass, and the system’s gravitation-

al forces and drag forces. The system is comprised of simple object oriented Classes

defining each object’s behaviour and interaction. Amongst its features, the Particle

class has weight, and vector fields, and is central to the schema, as a purveyor and

conductor of stable data. In physics based simulations, there is typically an attempt

to embody information outside the simulated system. A Spring class enforces re-

lationships between particles, operating like string elements. They are constructed

with variables for their rest length, their strength and dampening values, all of

which cumulatively bear on the qualities of this connection, ranging from springy

and elastic to rigid and pole-like. Another class for attractions (and inversely repul-

sions) further elaborates on inter-particle relations. Along with global variables of

gravity and drag, the system can be used to simulate many real life visual phenom-

ena.

The simulation extends beyond the physics library by creating a series of new

classes encapsulating its elements. Each one augments the basic system with new

fields that store animal-specific information, and through its screen-drawn ele-

ments as a visual interface to the user. The visible aspect of the simulation is used

primarily as a mechanism by the user to assess the functioning of the underlying

processing of data.

THE FORCE DiRECTED LAYOUT SiMULATiON

vi. HTTP://WWW.CS.PRiNCETON.EDU/~TRAER/PHYSiCS/

FIG 2.37 CLiMATiC ANCHORS


An Anchor class encapsulates a particle within the particle system, fixed to an

explicitly given position. The initialization runs through each of the climatic ends -

hot, cold, wet, dry - and creates a new anchor within the Zoo at each one’s coordin-

ates. Though early schemes aspired to have the positioning evolve over the simula-

tions course as an adaptive element, the final version kept these positions fixed.

A Space class, encapsulates particles and has fields for its types, a label and its spa-

tial properties - including area and elevation. This class is further extended through

a bifurcation into Animal and Human.

The Animal class is constructed using information from the database tokenized

into arguments. Along with the inherited methods and fields from the Space class,

it is initialized with floats for the areas of land covers, vectors for the position of the

landcovers relative to each exhibit’s centre, vectors for the position of their private

quarters with respect to their exhibit centres and a series of strings and booleans

that describe the animal’s behaviour. Depending on each Animal’s perceived ele-

mental adaptive behaviour - running, flying, climbing, swimming, digging - each

is assigned an elevation with respect to the grade of the terrain. As each of these

is parsed, they are also anchored to one of several landscape anchors with a Link

object. Each animal is tethered to two additional anchors that correspond to their

exhibit’s humidity levels and temperature.

The Human class is constructed using a reduced set of arguments that in some

cases include explicit positions that remain fixed over the simulation’s duration.

They are distinguished by a type field, identifying them as service (back of house)

or public, to be inhabited and used by zoo visitors. The Human objects, by their

anchors and Links, are far more restricted in their range of motion.

A Link class encapsulates the Spring class and augments it by storing the spaces it

is connected to in accessible fields. Links can be created either between spaces or

between spaces and anchors. They can be identified by their type and sorted into

different lists to distinguish and apply modifications as is seen fit. Inter-particle

links are turned on and off as particles move in and out of range of one another. FIG 2.38 iLLUSTRATiON OF ExAMPLES OF CONvERGENT EvOLUTiON


After the simulation has been running for a period of time, movement progressively

becomes dampened, and the particles will tend to settle into poised positions. Upon

execution of an export function, data processed in the simulation is written to a

number of text files in tab separated format.

A series of mappings generated by processing the exported data through paramet-

ric models, describe the system in terms of several identifiers, including strata,

temperature and humidity, diet type, and land cover. Like the visual component

of the simulation, they are used as feedback elements to assess the results of its

processes.

FIG 2.39 AxONOMETRiC DiAGRAMS MAPPiNG ExHiBiT DiSTRiBUTiONCOLLECTiON MAPPED ACCORDiNG TO SEvERAL DiMENSiONS


iNTERNAL DiFFERENTiATiON

GROUND OPERATiONS

FIG 2.40 PROCESSUAL FLOWSTERRiTORiAL CHANNELS

83 iNTERNAL DiFFERENTiATiON

FIG 2.41iNvESTiGATiONS iNTO NEiGHBOURHOODS STUDiES OF ExHiBiT DiSTRiCTS AND THE POTENTiAL FOR viSiTOR PATHWAYS TO ACT AS SEPARATiONS BETWEEN ANiMALS; CONTiGUOUS NEiGHBOURiNG ExHiBiTS WOULD BE CREATED BY SEPARATiNG THE viSiTOR PATHWAY FROM THE ExHiBiT GRADE


This section departs from the previous by transforming its generated data - particu-

larly vectors denoting exhibit centres - into a spatial territorializing of the ground.

This operation depends on distinctions made between predator and prey species in

order to allocate contiguous ground surfaces, creating service corridors which allow

access onto animal holding spaces.

The ground is conceived as an archipelago; bodies of land connected tenuously to

form a perceptible whole. Like the flocking examples too, the same body can be par-

titioned in a number of ways and still maintain its fidelity. Each exhibit is defined

only by a concrete retaining wall and a separation by trench to its adjacent exhibit.

Depending on amicability between neighbours, the void created by these channels

is bridged by a light open metal grating.

The mechanism for constructing the planimetric arrangement is a two dimensional

voronoi algorithm. The computational algorithm generates a tesselation of space,

dividing it amongst a given set of points. The voronoi has been used formally in

various research areas from geography - the computation of nearest neighbour

searches and to aid in finding ideal locations for new points of distribution in a

territory - to robotics - to aid the autonomous navigation around obstacles - to

computer graphics - the procedural generation of natural-looking patterns like that

of giraffe skins and soap bubbles.

In the case of the zoo, the voronoi is a mechanism for automatically dividing space,

its inputs having already been conditioned by the force directed layout. The territory

is conceived as a bound whole that is internally distributed or partitioned amongst

the points as given. It imagines that the zoo is in an embryonic state, the cellular

division being the first stage in a series of morphogenetic developments that will

continue to articulate and specialize its spatial units to suit its particular situation.

Though it may be deployed with a homogenous regularity, the perceived irregularity

- characterized by varying angles of intersection of its bounding walls - reinforces

the intent to create a space of disorientation and ambiguity. It does away with sym-

metry and orientation. Where one cell ends, the next begins, each one inflecting and

FIG 2.41iNvESTiGATiONS iNTO NEiGHBOURHOODS STUDiES OF ExHiBiT DiSTRiCTS AND THE POTENTiAL FOR viSiTOR PATHWAYS TO ACT AS SEPARATiONS BETWEEN ANiMALS; CONTiGUOUS NEiGHBOURiNG ExHiBiTS WOULD BE CREATED BY SEPARATiNG THE viSiTOR PATHWAY FROM THE ExHiBiT GRADE


being inflected by its neighbours. As opposed to the figure/ground approach typical

of zoos, this zoo is all figure, or all ground, depending on how one chooses to look at

it. Delineations between species’s exhibits are intended to be ambiguated, always

seeking overlap where permissible. There is, consequently, no perfect vantage point

from which to experience the exhibits.

The voronoi field is a homogenous one, deployed consistently across the zoo’s

grounds. Insofar as it is a process of division, the voronoi strategy permits further

division without a loss of structural integrity. The geometry is amenable to further

subdivision should further animals be inserted into the zoo. There is no direct

correlation between the areas given by the animal database and the land allotted

by the voronoi division scheme. The zoo is conceived as a finite piece of terrestrial

real estate to be administered and distributed amongst its inhabitants. Exhibits, far

exceeding the minimum space allocation given by zoo standards, can accommodate

larger social groupings as well as possible subdivision, both without compromising

its spatial integrity.

The zoo appropriates the landscape ha-ha, a spatial device originally used in Eng-

lish landscaping to conceal barriers to the movement of cattle. It was reincarnated

at Carl Hagenbeck’s zoovii to create cages without bars for his animals. His moats

were used to give the illusion of a panoramic halcyon-like scene with incompatible

species living in inexplicable proximity of one another.

vii. TiERPARK HAGENBECK, HAMBURG, GERMANY, 1907

FIG 2.42 AxONOMETRiC OF GROUND OPERATiONS


FIG 2.43•EDGE CONDiTiONS MAPDiAGRAM MAPPiNG EACH EDGE CONDiTiON TO ONE OF THE SPATiAL ADJACENCY CONDiTiONS

FIG 2.44•MATRix OF SPATiAL ADJACENCiES


Departing from the Force Directed Layout simulation, the ground operations are

triggered by the animal exhibit centres and their respective properties.

Before the ground is differentiated into enclosures, the zoo is demarcated and sep-

arated from its context by incisions in the ground. At all but its northern edges, this

separation happens by way of a recess in the land or water by a concrete channel

or ha-ha. These features are inserted inconspicuously to suggest continuity with its

context. An “urban” edge is generated by cutting and lifting the ground; a topo-

graphical mountain oriented southward to the lake, and a glazed vertical façade

oriented north towards the zoos entry point and built areas further on. Behind this

glazed façade, under the lifted habitat surface, a lobby area provides meeting point,

visitor orientation and other buried interpretive programs such as auditorium, and

screening rooms. The land is driven by the accumulation of mountainous animals

along this edge which were anchored in the force directed layout stage.

The ground, now bound, is divided by the recorded positions fed to a voronoi algo-

rithm. Explored in several media and with different versions of the algorithm, the

final version used an algorithm built into the parametric software. The basic con-

struction of a voronoi diagram involves taking a sampling of points within a metric

space and dividing it amongst them. Each point becomes bounded by a bounding

wall, constructed by the perpendicular bisectors passing through the midpoints be-

tween each adjacent pairing of points. Those bisectors immediately “visible” to each

point become its bounding geometry. Every point within the cell’s walls are closer to

its centre (sample point) than to any other in the system. In this way, voronoi can be

seen to be related to the abstract machine behind other morphogenetic searches for

minima like that of soap bubbles and their minimal surfaces.

The cells generated by the algorithm, are separated out into lists of “friends” and

“foes”, each specifying its corresponding animal’s compatibility with others. No-

tional cell walls between adjacent friends are conceived as tenuously connected

tissue, bridged with open metal grating. Around cells identified as foes, the drop in

grade poses a spatial barrier to interaction.

ANiMAL TERRiTORiES

FIG 2.43•EDGE CONDiTiONS MAPDiAGRAM MAPPiNG EACH EDGE CONDiTiON TO ONE OF THE SPATiAL ADJACENCY CONDiTiONS

FIG 2.44•MATRix OF SPATiAL ADJACENCiES


FIG 2.45ANiMAL QUARTERS


The separations between enclosures double as conduits for service flows. In this

way, they are conceived as hybrid mechanisms – architectural mediation devices

which simultaneously separate (animals) and connect (support).

The separation distances between incompatible cells are conditioned by a list of

values from the animal database that reflect jumping capacities and vehicular

passage. The space between animal territories, at a height 18 feet below the terrain,

is used as a service conduit for the movement of animal keepers and zoo main-

tenance staff, for the flows of food and water, and the return flows of waste. The

jumping distance data is conditioned by requirements regarding such flows through

mapping and truncating functions.

Each exhibit has a corresponding private quarters for animals to bed down at night

and to facilitate temporary isolation as it may arise. These spaces are fully enclosed,

and are modest in size relative to the open exhibits. Accessed by animals through

descending ramps within the exhibits, they provide nodal points where animal

keepers can get access to treat and observe the animals within a manageable

space. Interfaces between the worlds of the animals and zoo staff, they are directly

connected to both; most quarters lay under the terrain with direct access from the

service conduits, while those that sit elevated in the tree canopy have access from

the roof above.

The service level is conceived as an urban network, where communities with shared

qualities are drawn towards each other to form cores of intensity. The position of

each animal’s private quarters is driven by vectors generated by the force directed

layout (FDL) simulation; the Animal class is equipped with methods to search its

neighbourhood for animals who have similar dietary requirements. Should any be

found, vectors are generated that point to average positions amongst those being

considered. The cumulative effect is to create clustered neighbourhood nodal points

within the network. These points can be further re-enforced with human programs

related to the study of certain kinds of species, or as depots for the distribution of

materials, food, or waste.

ANiMAL SERviCiNG

FIG 2.45ANiMAL QUARTERS


viSiTOR PATHWAYS

STRUCTURED DERivES

FIG 2.46 PROCESSUAL FLOWSPUBLiC CiRCULATiON

93 viSiTOR PATHWAYS

FIG 2.47 ZOO PUBLiC CiRCULATiON STRATEGiESiLLUSTRATiON OF ExiSTiNG TYPES AND THE PROJECT'S THE iNTENT WiTH REGARD TO viSiTOR PATHWAYS OvERLAiD ON AERiAL viEW OF GENERiC RESiDENTiAL SUBDiviSiON


This section describes a simulation developed for laying out pathways and follows

with a portion about a strategy for locating and drawing out singularities within the

system, moments for pause, reflection and concentrated engagement amidst the

web of perpetual motion.

We might read the evolution of the zoo’s internal logic - particularly that of visitor

circulation and exhibit plan - as one paralleling the suburbanization of the city. The

exhibits of the first European inner-city menagerie style zoos were laid out as lots

bound by the dense street like passageways. Animals were typically held in separate

roofed in enclosures of concrete or steel bars. The zoos built from the mid-20th cen-

tury onwards tend to be much more spatially generous in order to foreground ecol-

ogy, but its consequence is that the lengths of paths connecting exhibits are longer

and their points of connection are few and far between. These zoos resemble the

morphology of suburban enclaves with their gate-like demarcations, arterial roads,

and benign crescents and cul-de-sacs. Both systems emphasize exclusivity, limit

choice and chance, and implement a system of controlled passage. The confinement

accompanying the spatial evolution of the zoo restricts the zoo visitor’s ability to

draw their own novel and personal connections between exhibits and species. They

are forced instead to passively observe their environment along overly structured

corridors.

With the design for the zoo’s system of visitor pathways, the intent is to return a

sense of wilderness - whether that of the urban derive or the nature walk - to its

experience. This step is conceived as a hybrid system somewhere between the open-

ness of the bidirectional grid and the directed control of its counterpart, the line

or corridor. Lying somewhere between these two bookends, this thesis posits, is a

nuanced middle-ground that offers its visitors the interactivity of constructing their

own narrative through a dense set of pathways, which are conditioned by material

logic of relatedness and proximity. The collection is liberated from the control of a

single-serving narratives - a reduced one whose future revision is almost a certainty

- instead rendered as a sort of “choose your own adventure”viii experience where it is

expected that visitors will have more meaningful experiences as active agents.

viii. CHOOSE YOUR OWN ADVENTURE BOOKS, FiRST PUBLiSHED BY BANTAM BOOKS FROM 1979-1998, NOW BY CHOOSECO, HTTP://CYOA.COM

FIG 2.47 ZOO PUBLiC CiRCULATiON STRATEGiESiLLUSTRATiON OF ExiSTiNG TYPES AND THE PROJECT'S THE iNTENT WiTH REGARD TO viSiTOR PATHWAYS OvERLAiD ON AERiAL viEW OF GENERiC RESiDENTiAL SUBDiviSiON

95 viSiTOR PATHWAYS


FIG 2.48 •METRO TORONTO ZOO MASTERPLANAS A HiERARCHiCAL CENTRAL LOOP - EACH ECO-REGiON iS DiSTiNCT AND iSOLATED FROM EACH OTHER

FIG 2.49 •GRiD CiRCULATiON STUDYDiAGRAMS iiLLUSTRATiNG THE POTENTiAL FOR ALTERNATE CiRCULATORY ROUTES GivEN BY GRiD SYSTEM

This work is indebted to its precedents in the material experiments of Frei Otto,

which were since followed with the digital reconstructions by Lars Spuybroek (NOX)

and more recently by MRGD Architects. This portion of the thesis intends to delve

deeper into the already explored territory and examine the potential in the abstrac-

tions of such a simulation to become a material system, and its consequences.

What results from the simulation is a network of human passageways smoothly

bifurcating between possible alternate courses. The paths run through the exhibits

connected to their centres. Each exhibit is defined by an ideal viewing elevation cor-

responding with its animal’s adaptive behaviour. Reduced to three primary levels,

each exhibit is to be viewed either from a path at grade with the habitat dipping

underneath it, a path burrowing below grade, and one elevated on posts. Flooring of

open steel grating, and guardrails comprised of vertical steel-plate pickets allow for

the unobstructed passage of spatial phenomena - visual, aural, tactile, olfactory -

into and through the paths.

This stage also intends to develop a structurally informal system as counterpoint to

the supposed right-ness of man. A staple of humanistic thought, man is privileged

as elevated above other forms of life; this is evidenced by his standing upright, his

thinking, and building with straight lines and right angles. Le Corbusier went an

extra step by his suggestion that human life is opposed to animal life and that the

irrational meandering donkey paths, around which ancient cities evolved, attested

to this fundamental incompatibility, as centres of congestion and disease. Within

the animal context of the zoo, these roles are reversed, and the “logic” of the donkey

path takes on an altogether different meaning.

BUiLDiNG iT “RiGHT”

97 viSiTOR PATHWAYS

FIG 2.48 •METRO TORONTO ZOO MASTERPLANAS A HiERARCHiCAL CENTRAL LOOP - EACH ECO-REGiON iS DiSTiNCT AND iSOLATED FROM EACH OTHER

FIG 2.49 •GRiD CiRCULATiON STUDYDiAGRAMS iiLLUSTRATiNG THE POTENTiAL FOR ALTERNATE CiRCULATORY ROUTES GivEN BY GRiD SYSTEM

Serving to increase connectivity between disparate parts of the zoo, a tangled web

of linear paths connect exhibits of related species. To optimize and limit inordinate

redundancies within the system, the pathways are simulated as moistened threads

which have a tendency to bundle and gather together. A consequence of this

bundling is the smooth transitioning between directions and thus supports slippage

between more structured navigations of the same system.

The simulation of the bundling and gathering effects of wet threads was built as a

simulation in Processing using the physics library of Jeff Traer. Its initial state takes

off from where the force directed layout program leaves; a text file produced by

that program (the latter’s) is parsed and its lines used as arguments as new fixed

Particles are instantiated at each exhibit’s centre. These Particles are assembled into

two arrays, one of a new class called Human, the other a new class called Animal.

The Human class is constructed simply by using the x, y, and z-coordinates and a

string identifier read from the file while the Animal class takes these attributes as

well as their strata, diet, temperature and humidity requirements.

Once these objects are in place, the initialization phase moves on to the construc-

tion of Hair objects. Nested iterative sequences cycle through each Human object

calculating distances between it and every one of the Animal objects and stor-

ing this in a float field built into the Animal class. On each Human cycle, this list

is sorted using a Comparator class that will compare each distance, sorting from

smallest to largest. Once sorted, the closest Animals are used as arguments along

with the Human to construct each Hair.

A similar process of assigning distances between each Animal, and every other one

in the array accompanies the construction of Hairs between Animal objects. After

sorting, each Animal in the list must pass through a series of conditional gateways

in order to be constructed. A counting function - comprised of an integer field and

a corresponding method for its incremental addition - is responsible for keeping

track of how connected each object is, triggered by the construction of each Hair.

The Animal to Animal Hairs are also distinguished by a character used to denote

three different types of connections. Each connection refers to a type of relation-

THE BUNDLiNG SiMULATiON

99 viSiTOR PATHWAYS

FIG 2.50 AxONOMETRiC DiAGRAM OF 3 STRATA & CONNECTiONS BETWEEN ExHiBiTS


FIG 2.51 DiAGRAMS iiLLUSTRATiNG SiMULATiON iN DEvELOPMENT POLAR ARRAY OF FixED PARTiCLES CONNECTED TO EACH OTHER BY STRiNGS OF FREE PARTiCLES; ATTRACTiONS BETWEEN THESE PARTiCLES GENERATE BUNDLiNG BEHAviOURS; REPELLENT PARTiCLES (RED) CAN BE iNSERTED WiTHiN THE SYSTEM AS OBSTACLES

ship, the first of which stands out as it and its constituent Particles are forced to

maintain constant height relative to grade along their length; this to ensure that

there are pathways that don’t insist on ascents or descents. This first type denotes

connections between Animals that belong to the same stratum and within certain

proximity, the second, equality in climate as well as proximity, and the third equal-

ity in diet as well as proximity. The Hairs are distinguished in this way, and with a

visual correspondence of colour differentiation, in order to ensure that exhibits are

accessible to their neighbours across the multi-dimensional data set. With each test

for equality and proximity, gate connections are also being tested to ensure that the

Animal objects do not become overcrowded.

The Hairs themselves are in principle an array of free moving Particles attached to a

fixed Particle at each end. The size of this array is governed by the distance between

these ends which are given in the construction process. The initial positions of the

free moving Particles are equidistant between fixed extremities and a Spring is

constructed between each and its neighbour in the array. The draw loop enacted on

each time step in the simulation, handles the appearance of the Hairs as smoothly-

curved linear elements. Bezier curves are drawn between vectors midway between

each set of neighbours and the midway between the consecutive set. The intermedi-

ate Particle is used as a position or the Bezier handles to pass through. Though this

information appears solely to drive the display on screen, the coordinates of the

Bezier ends and handles will get recorded to a text file to be processed into a three

dimensional form in the modelling software.

Still in the initialization phase, attraction forces are added between Particles in

the system to produce the clinging action. Cycling through each Particle and every

other one, an Attraction is constructed, its strength and minimum distance being

conditioned by the initial distances between them. If the Particles are beyond a

certain distance, they will be turned off and will have no effect.

The recurring portion of the simulation then, apart from advancing the time step of

the physics system and rendering of the geometry in the display window, is in the

interaction of the attractive forces which propel the Particles towards each other

101 viSiTOR PATHWAYS

FIG 2.51 DiAGRAMS iiLLUSTRATiNG SiMULATiON iN DEvELOPMENT POLAR ARRAY OF FixED PARTiCLES CONNECTED TO EACH OTHER BY STRiNGS OF FREE PARTiCLES; ATTRACTiONS BETWEEN THESE PARTiCLES GENERATE BUNDLiNG BEHAviOURS; REPELLENT PARTiCLES (RED) CAN BE iNSERTED WiTHiN THE SYSTEM AS OBSTACLES


FIG 2.52 PATH OPTiMiZATiON SiMULATiON EACH COLOUR REFERS TO A DiFFERENT TYPE OF CONNECTiON AS THEY RELATE TO ONE OF THREE POSSiBLE CRiTERiA: STRATA, CLiMATE, AND DiET,

and continuously re-inscribe their associated position Vector. Upon export each

array of Beziers and end lines representing each Hair are written each to a separate

line in a text file. In the parametric model, these lines are reassembled and finally

joined into continuous curves to be used as rails, which can be lofted with the sec-

tional geometry of the visitor pathway.


FIG 2.52 PATH OPTiMiZATiON SiMULATiON EACH COLOUR REFERS TO A DiFFERENT TYPE OF CONNECTiON AS THEY RELATE TO ONE OF THREE POSSiBLE CRiTERiA: STRATA, CLiMATE, AND DiET,

BEFORE SWELLING

AFTER SWELLING

E

EE

FIG 2.53 •SWELLiNG DiAGRAMOvERALL PATH SYSTEM BEFORE & AFTER “SWELLiNG" ACTiON; ExHiBiTS TRiGGERiNG SWELLS ARE MARKED

FIG 2.54 •AxONOMETRiC SWELLS RENDERiNGS iLLUSTRATiNG THREE TYPES OF viSiTOR ACTiviTY AT SWELL CONDiTiONS ALONG THE PATHS


BEFORE SWELLiNG

AFTER SWELLiNG

PERFORATiONS ALLOW FOR ExCHANGE BETWEEN ANiMALS, ZOO STAFF, AND viSiTORS

SURFACE DEFORMED TO BRiNG viSiTORS CLOSER TO HABiTAT SURFACE AND DEFiNE GATHERiNG SPACE

GRADATiON iN THE DENSiTY OF METAL GRATiNG ALLOW LANDSCAPE THROUGH SURFACE

FIG 2.53 •SWELLiNG DiAGRAMOvERALL PATH SYSTEM BEFORE & AFTER “SWELLiNG" ACTiON; ExHiBiTS TRiGGERiNG SWELLS ARE MARKED

FIG 2.54 •AxONOMETRiC SWELLS RENDERiNGS iLLUSTRATiNG THREE TYPES OF viSiTOR ACTiviTY AT SWELL CONDiTiONS ALONG THE PATHS

The counterparts parts to the Human program dedicated to motion and flow are

restive moments for reflection, dining and recreation. These parts are imagined as

swells or engorgements within the path system of linear elements. Key rest areas

are given explicitly in the force directed layout simulation and they are incorporated

into a voronoi partitioning process. The generative process to be dealt with here is

the vantage points along the paths themselves. At these Animal viewing spots, the

path widens to accommodate benches and viewing platforms as well as signage and

other interpretive features.

The strategy for the deployment of these swells departs from the intuition that

these points would be better suited to long lengths of pathways. Along with provid-

ing respite from lengthy walks between exhibit centres, they are better suited for

viewing over longer ranges and are ill-suited to shorter lengths which are typically

congested with other paths.

In the parametric model, paths below a given length are culled to produce a refined

list to receive the swells. For this list, a corresponding one is generated finding the

closest of the exhibit centres to it. From this exhibit the parameter along this curve

locates the apex of the swell. A Gaussian curve function - a bell shaped curve eas-

ing in and out of its limits - describes the transition from normal path to swell. It is

driven by three primary variables – position of top limit along the curve, the height

or amount of swelling at this point, and the length along the curve over which this

transition occurs. Positions of construction planes along the curve are fed to this

function which generates values to be used for scaling. These scaling values operate

non-uniformly on the section geometry of the path prior to sweeping. Once these

sections are in place, each list of them along each path are swept to produce three

dimensional geometry.

SWELLS



FIG 2.55 PROCESSUAL FLOWSENvELOPE

ENvELOPE

ENviRONMENTAL MEDiATiON

107 ENvELOPE

FIG 2.55 PROCESSUAL FLOWSENvELOPE

FIG 2.56 AxONOMETRiC OvERviEW OF ENvELOPE

In so much as it imposes material limits on space, the envelope required to house

the flora and fauna of habitats foreign to that of the site, always threatened to

undo the project’s aspirations to boundlessness and fluctuation. Two morphological

strategies aim to counteract both the size and fixity of its body; on the one hand

its depth is reconceived as a multi-layered membrane, paper thin at its edges and

swelling to contain an interior environment and its attendant mechanical installa-

tions. On the other its planimetric edges are soft and irregular undoing the object-

ness that accompanies orientation with its sense of fronts and backs. The entirety is

conceived as a moment of suspended animation, just as a cloud’s body, composed

of individual water molecules condenses momentarily to take on material embodi-

ment.

Its surface is broken down by means of a process of recursive subdivision, the mon-

otony of its expanse being counteracted by the fluctuations in scale. The panels,

once divided, register the field of information in their subsequent specialization and

differentiation.

OvERviEW

109 ENvELOPE

FIG 2.56 AxONOMETRiC OvERviEW OF ENvELOPE

The envelope embodies its material conditioning by registration of its milieu and

the animals it encloses.

The geometry of the climatically separated portion of the zoo is generated by an

attractor pattern assembled in a parametric model. The list of animals in the source

animal database provides this design component with a list of Boolean values indi-

cating which exhibits require enclosure as well as Doubles indicating humidity and

temperature requirements. The scheme generates a soft undulating form conceived

of as a 2 ply membrane swelling in areas requiring larger buffers from the local

environment. Tapering in towards its outer edges, approaching its minima, the two

layers dissolve into one another, leaving the ground open to the sky above.

The attractor pattern, implemented through a custom visualbasic.Net (vb.Net)

script, in principle operates by assigning values to each in a regular field of points

according to their proximity to sample points. The sample points in this case come

from a list of the exhibit centres culled to exclude those exhibits which do not re-

quire the envelope. In this particular implementation, the strength of each attract-

or’s influence on the field is conditioned by the humidity and temperature values.

The greater sectional depth allows for a buffer of air separation as well as room for

mechanical duct-work. The added depth simultaneously provides for a secondary

means of servicing exhibits from above which coincides with the hot and humid

climatic portions supporting higher proportions of tree dwelling species.

The mechanism is constructed to register the field which is more than a simple one

to one correlation of the underlying data. As every grid point is processed, a gate

condition ensures that the distance to each attractor point is under a given value.

The values will accumulate intensity from all attractors within this range so that

areas of the zoo with neighbouring species requiring the envelope will be driven

higher than areas containing sole attractors. A Gaussian function produces the

values to ensure smooth transitions both in and out of the zones of intensity. The

intent is to describe a soft flowing form, a barely perceptible thickness at its edges,

swelling inconspicuously towards its centre; reducing the visual weight and thus

presence of the roof.

THE GLOBAL SURFACE

111 ENvELOPE

FIG 2.57 iLLUSTRATiON DESCRiBiNG THE PROCESS BEHiND THE GENERATiON OF THE SURFACES

Lines are drawn starting from the grid of points in the z-direction, their length given

by the values processed thus far. By connecting the end points of these lines, a

topographical surface is generated. In order to amplify the idea of an eroded edge, a

figure receding into a ground, a section plane cut parallel with the horizontal plane

above the bottom of the surface intersects with the surface to produce a curve. The

curve bounds only those areas requiring enclosure; trimming the surface with the

curve leaves those that do not open to the sky.

Subsequent steps involve using the same basic sectional depths but scaled as

required. First, this surface is mirrored about the horizontal plane to complete the

other half of the swell. A second mirroring scaled non-uniformly along the z-axis,

intersects the ground plane and thus provide for the transfer of roof loads to the

ground. Interior arches mirroring the minima, but unsheathed break up larger

interior spans.

113 ENvELOPE

The panelization of the surface begins with a process of recursive subdivision. An

initial population of surfaces generated by a regular diagrid are tested against the

underlying one. The difference between the two drive successive loops through a

subdivision routine, each producing smaller panels more closely approximating the

base surface. The movement between scales is a registration of surface curvature,

itself a registration of difference between species.

Once the scale of panels are in place, each one is identified and differentiated. Un-

like much experimentation with parametric modelling involving the population of

scaffolds with components, the intent of this portion of the design was to shift not

only in degree but in kind as well. Material behaviour shifts from the conventional

spectrum of continuous gradation towards one with subsets that can trigger alto-

gether different behaviours. The process begins by breaking the base surface down

into isoparametric subsets of it surface using UV domain intervals. From these

surfaces, the midpoints of its edges are sorted and shifted to produce a diagonal

grid (or diagrid) of points. Through these points new diamond shaped surfaces are

created.

These new panels are fed along with their centroids, the exhibits centroids, and

other exhibit attributes to a custom vb.Net component. This script goes through

each of the panels and finds the index of the closest of the list of exhibits and using

this same integer places its associated attributes into new variables. These variables

are checked against a series of conditional gates to assess whether the exhibit the

panel encloses belongs to a nocturnal animal, one capable of flying of climbing, or

one that requires high humidity and heat levels. Each original panel is subsequently

funneled through to a different output channel.

Each panel is composed of two triangular fiberglass panels. They range in opacity

depending on the light levels required by the habitat they enclose. Likewise, de-

pending on the need for controlling humidity and heat levels, the two portions swell

in opposing directions. Operating like gills, in this swollen and opposite configura-

tion, they channel the movement of air in and out through ventilation grills. In

scenarios that require sealing the habitat off from the site’s local climate, the two

SKiN COMPONENTS

115 ENvELOPE

FIG 2.58 OvERviEW SHOWiNG DiSTRiBUTiON AND ALLOCATiON OF PANELS

halves meet at a colinear edge and become conjoined. These attributes, which com-

bine to produce a diverse and varied population of panels, are applied in varying

intensities, driven by proximity to loci within the field.

117 ENvELOPE

FIG 2.59 DETAiL OF FRAGMENT AND iNDiviDUAL PANELS

CHAPTER 3 EMERGENT PHENOMENATHE PROCESSUAL ARTEFACTS

119

The thesis should be read principally as a chronicle of experience in multiple di-

mensions. It is decipherable as an emergent end that must make do with - and per-

haps even thrive on - its unsettled imprecision. It is an endeavour that is ambiguous

- both by design and accident - about its identity. It is neither pure methodology,

nor resolute architectural project.

The concluding drawings are to be seen as the consequences of the methodology

enquired into through the speculative design. They should be read as an idealiza-

tion of process, whose barometer of success is measured primarily in the relations

amongst parts and the spaces between things.

Any disorientation encountered in trying to decode the drawings is fully intended -

just as with emergent systems they demand an active gaze where shifts in percep-

tion are a fundamental component in the construction of intelligible “bodies”. They

capture the image of an environment in a state of becoming - one without privi-

leged vantage points, lacking fronts, backs, or centres.

121 THE DRAWiNG SETPROCESSUAL RESiDUE

THE DRAWiNG SETPROCESSUAL RESiDUE

122EMERGENT PHENOMENA

FIG 3.60 SiTE PLAN


FIG 3.61 AERiAL PERSPECTivE


FIG 3.62 ROOF PLAN


FIG 3.63 PLAN - ABOvE ExHiBiT LEvEL


FIG 3.64 PLAN LEvEL - BELOW ExHiBiT LEvEL


FIG 3.65 SECTiON A

FIG 3.66 SECTiON B


FIG 3.67 SECTiONAL PERSPECTivE OF FRAGMENT


FIG 3.68 ExPLODED AxONOMETRiC OF FRAGMENT

FIG 3.67 SECTiONAL PERSPECTivE OF FRAGMENT

viSiTOR LAYER

ExHiBiT LAYER

SERviCE LAYER

ENCLOSURE LAYER

ROOF MEMBRANE

ROOF SPACE FRAME

vERTiCAL SUPPORT

vERTiCAL MEMBRANE

METAL GRATiNG,

GURADRAiLS,

SUPPORTiNG POSTS

LANDCOvER: WATER,

GRASSES, TREES, SOiL

CONCRETE RETAiNiNG WALLS

ANiMAL QUARTERS


FIG 3.69 PLAN FRAGMENT - OF ROOF


FIG 3.70 PLAN FRAGMENT - THROUGH ROOF


FIG 3.71 PLAN FRAGMENT - ABOvE ExHiBiT LEvEL


FIG 3.72 PLAN FRAGMENT - BELOW ExHiBiT LEvEL


FIG 3.73 viGNETTE - viEW FROM WiTHiN SERviCE CHANNEL


FIG 3.74 viGNETTE - viEW FROM GiANT ANT-EATER ExHiBiT


FIG 3.75 viGNETTE - viEW FROM MEDiTERRANEAN HORSE-SHOE BAT ExHiBiT


FIG 3.76 viGNETTE - viEW ALONG viSiTOR PATH iN TREE CANOPY

The thesis begins with questions entrenched in architecture’s intertwinement with

life. It asks how architecture might respond to the world as a continuous product

of non-linear dynamic systems that transcend all of our societal resolve to create a

distinct and coherent human milieu. It seeks to know how the practice of architec-

ture can more precisely engage in the flows that generate our material reality. Is

the built environment to remain isolated in its singular orientation - tailored around

the perceptions of the Human? Or might it be considered as just one contribution

amongst others, modelled after and inserted into the very flows in which it moves

and is moved by?

The thesis departs from an interest in the processes that give rise to matter, seeing

in the conceptual framework given by emergence a possible ground common to

both natural and cultural phenomena. The investigation seizes on what it perceives

to be dormant potential in the zoo program, one it considers to be bound by undue

quantities of artifice. The methods of its spatial construction themselves have

limited the very qualities that they have purported to make accessible. The discrete,

separated enclosures and the linear circulation systems of the traditional zoo pro-

gram are symptoms of a single problem- a reductive and over-simplified approach

to complex ecologies; one that might be alleviated by a thorough re-orientation of

design practice.

A parallel undertaking, embedded in the same investigation, speculates on a

new turn in the computational paradigm. Previous generations of research have

remained far above the fray, either sequestering their investigations in the purely

theoretical, or contentedly settling on more formal explorations. The zoo, on the

other hand, is a program of diverse inhabitation, and it allows degrees of flexibility

but warrants engagement with issues of program, environment, and ecology.

The thesis is partly a negotiation of what is seen to be an institutionalized opposi-

tion between techne and poesis - between the methods, mediums, and rules which

structure how we build, and the aspirations we have for the evocative powers of

environment. It imagines a scenario wherein they are inseparable, by way of parallel

processes where the two feed off one another. Particular to the design for a zoo, the

CONCLUDiNG REMARKSFEEDiNG FORWARD

143 CONCLUDiNG REMARKSFEEDiNG FORWARD

FIG 3.77 viGNETTE - viEW FROM CLOUDED SNOW LEOPARD ExHiBiT

thesis ponders the possibility that meaning, by way of form, might be produced by

the registration of an adaptive environment responding to the program of a diverse

range of inhabitation. The zoo is a materialization that operates simultaneously as

a pedagogical instrument and a life support system.

As an exposition of process - and with it - an undercurrent of methodology, the

thesis research demands travel across a range of disciplines and territories - from

zoology and biology to computer science and information technology - along the

way acquiring foreign languages and customs. In order to understand its limits, the

project takes theoretical principles - of unmediated developmental processes - to

their logical conclusions; in an attempt to move beyond the broad sweep of rhetoric

to locate with greater precision their breaking points - points in the process where

the contingencies of reality might make such pursuits either unfeasible and/or

untenable - through the intimacy of lived experience.

Partway between theory and practice, computation is regarded as a contemplative

craft, however disembodied it may be. It allows one to “try things on”, if only just in

a fleeting way, and thus enquire into consequence. As a cooperative element, it can

speed or heighten the acquisition of experience, a critical part in the development

of intuition.

The actual act of constructing generative design machines is resolutely grounded in

matter, energy and acts of synthesis that transfer material flows into new orga-

nizations. This assembly necessitates an intimacy with the seeds that will set the

machine into motion (inputs) as well as its acceptable ranges of behaviour and

desired (optimal) performance (or outputs). It must spell out the rules by which

these seeds become transformed into full-fledged materializations. It is a logistical

or engineering approach, which seeks out descriptions of the conditions which give

rise to diversity, limiting the amount by which this diversity is explicitly given in the

instructions. The algorithmic script operates like its theatrical double, asking of its

actors to bring to their roles something that is not quite spelled out, but in between

the lines. If the machine’s construction engaged intellectual faculties, it makes

space for play, an activity fully engrossed in entirely different issues of tuning and

SUMMARiZiNG RESEARCH

CONSTRUCTiNG THE MACHiNE


composition. This activity is about testing feedback response to stimuli. Its efficacy

lies entirely in the intuition and feeling of its operator. As we work with these in-

struments - ones we have constructed - our bodies’ reflexes are set in motion. The

heart quickens, the blood rushes. Things feel right, or they don’t, and they do so in

various shades of intensity.

What should be obvious, is that technology, in and of itself, will not produce

complexity. It needs thoughtful orchestration. The suggestion of formal complex-

ity - particularly the vast versioning processes and specificity that is generated by

scripting and other elaborate computational procedures - might be better described

in its closer resemblance to complication than to complexity. This is not to say that

the modes of operation themselves, are faulty. Nor is it to say that they do not have

the ability to cooperatively generate complexity. It is simply that their deployment

without an active and reflective understanding of underlying processes will not

necessarily produce the complex.

What technology demands, but simply can’t provide itself (not yet at least), are pro-

ductive, meaningful and relevant questions. Without them the utility of any tools

that promise the augmentation - quantitative or qualitative - of our design searches

may be doubtful. What the computer excels at, what it is built for, is solving well

defined problems. With clearly defined problems, especially those with measurable

resolution, the computer is a very capable and useful addition to the architect’s

arsenal. Part of the merits of a computationally driven line of enquiry is in its inher-

ent capacity to recursively return to the question, each time refining it as more is

learned, and consequently generating answers of greater authority and precision. At

the very least, while technology may not itself be capable of asking important ques-

tions, it is certainly more than capable of provoking new ones.

Imagining the cyborgian subsuming of body and machine is accompanied by a

number of problems. Particularly worrisome to the logic of distributed intelligence

is that of the oversight of a project - of responsibility. By separating thinking and

action - manifested in the compounding of atomic decisions into singular resolute

wholes - we risk blissful ignorance, We somehow convince ourselves, that we are


not responsible for what we don’t know, or what we can’t see. The more opacity and

depth our machines acquire in attempting to contend with the vicissitude of life,

the more we risk delivery into, not out of, a troubled reality. Working with machines

must foreground responsibility, and not its retreat.

Computationally generated architecture can, at times, be mired in slavery to the

mistakes it employs. Its simulation of natural processes relate particularly to mis-

appropriations of scale, whether of time or space, but rarely are these conceived

in terms of programmatic events and inhabitation. Genetic algorithms, likewise,

presume that processes of natural selection - which typically can only be perceived

from the perspective of time-lines of millions of years - can be simulated by syn-

thetic models over the course of minutes or hours. The shapes of perceptible bodies

are always a product of aggregates of smaller building blocks, and thus, there is an

inextricable interdependence between the two scales. The same inter-component

behaviour that might generate particular phenomena at a certain scale in nature,

when manipulated to be perceptible at a different one, will generate different

phenomena. In translation the performative is lost; the process that generated the

pattern or shape is no longer necessary or relevant.

As long as the aforementioned mistakes are partitioned within a project, they are

limited to bearing - for better or worse - on just that part. This thesis enquired into

the consequence of a process operating autonomously, from start to finish, across

all scales, and not surprisingly, it became extremely problematic at times. Many of

its breaking points are weakened specifically by the imprecision of the model being

used to represent the scenario. Others stem from the impracticality of searching for

a coherent system to deal with the full breadth of reality’s vicissitude; typically this

latter scenario is really the same as the former, but in the context of an architec-

tural practice (and not a scientific one) it may warrant distinction. The utility and

applicability of the systems approach in architecture - either through parametric

models or algorithmic scripts - should be weighed against the time it might take to

develop it, as a model that will hold true for a wider range of conditions. Even then,

after all is said and done, unless these processes yield something truly remarkable,

or that might not be possible by other means, one has to wonder about the merits of

process-heavy pursuits in practice.


Returning to the particular experience of the project’s investigations, problems arise

with the evolution of its inputs, the system by which the data was being classified.

At the most practical level, machines are geared to processing data formatted to

very particular specifications and this precision makes the “machines” prone to

breaking down. The largest obstacle continued to be the time required to tend to

these issues, and the lack of immediacy that one might get from their counterparts

in manual drawing. These issues relating to lag times come to bear on our abil-

ity to assess consequence. We might attribute many of these problems not to the

methods themselves, but to the impracticality of deploying them in parallel across

all scales. As stated previously, when confined to clearly stated problems - with well

defined start and end points - they would not be an issue.

Notable lapses in the smooth processual flow occurred at both the beginning and

end of the zoo design- on entry and exit into the abstractions of the virtual. Near

the start, antithetical leaps or assumptions had to be taken in order to give the

project some leverage. Several programmatic elements were given explicit and

fixed positions on the site, notably those that interfaced with the site, such as entry

and service programs. Human programmatic elements were not conducive to the

logic of the animal properties, and these were dealt with independently, in far more

determinate ways. The immediacy of intuition in these cases, trumped the idealized

process of self-determination. Since it was deemed too cumbersome to allow these

elements to be “discovered” by process, so-called “autonomy” had to be discarded

temporarily.

There is trouble, as well, at the end of the processes, on exiting the system, and

returning from the abstractions of the virtual environment back into an architec-

tural one. It is at this point that the untenability of attempting to develop autono-

mous mechanical systems across all scales becomes evident again. At some point,

the play that these processes allow require fixing in order to be read architecturally.

Moving between scales, from site plan, to architectural plan and on to the detail,

the development of mechanical systems for this orchestration becomes extremely

cumbersome.

PROBLEMS: ENTERiNG

PROBLEMS: ExiTiNG


Getting swept up in an approach that allows one to continuously test options, one

which might help to define problems as well as generate its possible solutions,

proves to be extremely problematic. Towards the end of the project, details such as

the ones relating to swells within the visitor pathways and their possible inhabita-

tion made more sense as manually designed articulations of the machine-generated

diagram. This may be partly a function of available time and resources, but it also

attests to the limitation of being bound to purity in process. Though it may be self-

evident, processes that preserve real-time interdependency and take nothing for

granted, make for difficult paths.

If the project were to continue, it might spend considerable time investigating the

performance - and thus, consequence - of its building systems. These might include

various assessments ranging from the size of its environmental impact, its ecologi-

cal footprint, to its capacity to passively regulate its climate to the performance of

structural elements measured against their material consumption, and even cost.

The degree to which the management of living forces as a human endeavour can

and should approximate them in their process are central questions to the thesis,

though ones to which it cannot provide any resolute answers. They ask us to con-

tend with what distinguishes man from animal, and architecture from life. Within

those narratives within architectural discourse that intimate faithfulness to “na-

ture” there is a concealed struggle for control, a concern with domination between

master and subject. The perils of being “seized” or “captivated” by the increasing

persuasiveness of our media, looms overs us like a charmer over its snake. The

danger of becoming enslaved to any so-called reality is one that demands ongoing

attention. An orientation to reality, rather, that considers the simulacra to be a con-

tinuous regenerative and cooperative force, might better address our perceptions

as a fundamental part of its construction. Lest we allow ourselves to drift, reality

threatens to masquerade as if it is unadulterated.

The will to mastery becomes all the more urgent the more technology threatens to slip from human control.41

iF WE WERE TO GO FURTHER

STRUGGLE FOR CONTROL

41. MARTiN HEiDEGGER, THE QUESTION CONCERNING TECHNOLOGY, AND OTHER ESSAYS (TORONTO: FiTZHENRY & WHiTESiDE, 1977)


Addressing more pragmatically the problems that accompany new modes of prac-

tice, architecture will have to develop new strategies, and even entire new posi-

tions within the architectural office to manage the inconceivable quantities of data

produced by generative machines. If these endeavours are intended to engender

transparency, and in so doing move towards greater precision and authority - link-

ing form and performance in building and moving outside the limited realm of the

metaphor - the architect will have to become more comfortable with processing

quantitative information. Though rarely discussed, the need to develop strategies

and methods for dealing with this engineering data is a very real one.

Questions still attend how one might form closure on a loop which aspires to oper-

ate like the open-ended searches of living systems. How do we know when to stop

our synthetic evolutionary systems, declaring them to be “final”? How will we know

when we have “arrived”? What is being discussed here is more than just optimiza-

tion, a pursuit of single and fixed ideals. It might be more accurate to explain the

search itself in terms of event; a moving target which is conditioned by, just as it

conditions, the project’s situation. The response, therefore, necessitates not fixed

forms, but flexible approaches, those capable of adaptive response. For every move

or decision we make, our environment responds; it makes us just as we make it.

We should continue to ask how complexity theories can be made to operate in prac-

tice beyond the metaphorical - specifically the material realm - and what impact

this has on our architecture. Likewise, we must continue to ponder the distinctions

one might make between emergence, always operating on the world regardless of

its willed involvement, and its conceptual inclusion, played out in a controlled syn-

thetic laboratory-like setting. These practices, more than simply shaping our built

environment, are changing who we are.

NEW DEMANDS ON PRACTiCE

THE FUTURE


APPENDiCES

157

APPENDiCES 158

Processing is an open source programming language. Developed be approachable

to non-traditional users including artists, designers, researches and hobbyists, the

project was initiated by Ben Fry and Casey Reas in 2001 whilst studying at the MIT

Media Lab. Their goal was to allow “ideas to be ‘sketched’ in code”. It began with an

interest in teaching the fundamentals of scripting and computer control logics, but

has since blossomed into a thriving professional tool that has been used for major

music videos, publications, and large scale installation works. It is sustained by an

active community-driven forum and regular contributions of new libraries from

various developers.

Processing is written in a dialect of Java that makes regularly used functions

more convenient to access. Along with its syntax, the project includes several key

libraries, as well as the Processing Development Environment, an IDE (Integrated

Development Environment) with a simple set of features. It differs from Java in its

simplification of common procedures into more compact forms. Noted as being

more forgiving than C/C+, Processing's proximity eases the transition to such ad-

vanced languages.

The Processing project encourages a style of work that builds code quickly, understanding that either the code will be used as a quick sketch, or ideas are being tested before developing a final project. This could be misconstrued as software engineering heresy. Perhaps we’re not far from “hacking,” but this is more appropriate for the roles in which Processing is used.42

Libraries continue to be contributed by users to create simple and direct means to

implement regularly used code. Processing's IDE is very accessible; it strips away

that which might be required by heavier application development and adds built-in

capacity to export to the web. Their programs can be viewed online as applets or as

stand-alone applications for use on computers with a standard Java installation.

Processing lends itself to being incorporated into design strategies by non-special-

ists. Its built-in libraries ease working with 3-D geometry and the exchange of data

in other media. Alongside other emerging computational technologies, Processing is

being used in design studios and schools all over the world.

PROCESSiNG iNTRO

42. HTTP://PROCESSiNG.ORG/LEARNiNG/GETTiNGSTARTED/

PROCESSiNG iNTRO 159

FIG 4.1 SCREENGRAB OF THE PROCESSiNG iDE iN USE

APPENDiCES 160

Parametric software, like the explicitness of programming, is geared towards under-

standing and interrogating the processes behind the development of form. Though

it tends not to be made explicit to its users, parametric features perform just as

algorithmic codes do- by manipulating time and processing by serial procedures.

What distinguishes parametric software from the typical algorithm is primar-

ily in its interface with users - instead of running an algorithm once with a clear

temporal beginning and end it is run continuously. One builds a parametric rig in

incremental pieces picking up data somewhere in the process and delivering it else-

where after first undergoing some kind of manipulation. Visual feedback on screen

indicates to users the consequences of each operation as it is deployed.

Grasshopper - a plugin for Rhinoceros 3D used extensively in the thesis work - is

implemented primarily through a graphic user interface - parallel to a 3 dimen-

sional space represented on screen - described as a series of operations which can

be plugged together to create processual flows. In addition to regular geometric

features and functions, users have access to "blank" components that read scripted

code, in either vB.net or C.

The parametric rig is independent from the data which it processes. Its construction

is driven by two distinct procedures or motivations. While it is constructed typically

to search for new design options for a specific set of conditions, implicit, as well, is

an intention to understand, in each design problem, a more generic condition which

might borrow from the investigation, either through the user's experience, or liter-

ally, by re-purposing the parts of its mechanisms. This might be conceived along the

lines of an "abstract machine" which can drive different kinds of forms in the world.

Its processual development, distinct from the form it produces, re-orients emphasis

in the design process from concerns typological or morphological to the mecha-

nisms that give rise to them; towards producing novel and divergent combinations.

Parametric software might be seen partly as a response to the rise of custom fabri-

cation and its attendant endless variation. Designers, not confined to standardized

materials, have shifted their attention to the rules or parameters that govern these

forms

PARAMETRiC MODELiNG iNTRO

PARAMETRiC MODELiNG iNTRO 161

FIG 4.2 SCREENGRAB OF GRASSHOPPER iN USE

APPENDiCES 162APPENDiCES

1 Oryx 440 62.5 4000 Oryx dammah Asia Desert Artiodactyla Mammalia fur diurnal ground no no no no herbivore hot dry soil 0 0 0 100 0 0 102 Naked Mole Rat 0.09 3.5 400 Heterocephalus glaber Africa Desert Rodentia Mammalia skin diurnal underground no no no yes herbivore hot dry soil 0 0 0 100 0 0 63 Zebra 887.5 110 3600 Equus grevyi Africa Grassland Perissodactyla Mammalia fur diurnal ground no no no no herbivore hot mixed grass 5 75 10 0 0 10 104 Antelope 385 90 6000 Hippotragus niger niger Africa Grassland Artiodactyla Mammalia fur diurnal ground no no no no herbivore hot mixed grass 5 75 10 0 0 10 105 Warthog 220 47 2500 Phacochoerus aethiopicus Africa Grassland Artiodactyla Mammalia fur diurnal ground no no no no herbivore hot mixed grass 5 75 10 0 0 10 106 Caracal 30 32.5 1200 Caracal caracal Africa Grassland Carnivora Mammalia fur nocturnal ground no no yes no carnivore hot mixed grass 5 75 10 0 0 10 157 Serval 29.5 34 1200 Leptailurus serval Africa Grassland Carnivora Mammalia fur crepuscular ground no no yes no carnivore hot mixed grass 5 75 10 0 0 10 158 Spotted Hyena 85 42 2500 Crocuta crocuta Africa Grassland Carnivora Mammalia fur diurnal ground no no yes no carnivore hot mixed grass 5 75 10 0 0 10 159 Meerkat 2 12 1000 Suricata suricatta suricatta Africa Grassland Carnivora Mammalia fur diurnal ground no no no no carnivore hot mixed grass 5 75 10 0 0 10 6

10 White-nosed Coati 8.8 21 800 Nasua narica North America Grassland Carnivora Mammalia fur diurnal trees yes no no yes omnivore temperate mixed grass 5 75 10 0 0 10 611 Southern Three-banded Armadillo 3.25 12 500 Tolypeutes matacus South America Grassland Cingulata Mammalia carapace nocturnal underground no no yes no insectivore hot mixed grass 5 75 10 0 0 10 612 Koala 20 30 600 Phascolarctos cinereus Australia Grassland Diprotodontia Mammalia fur diurnal ground no no yes yes herbivore hot mixed grass 5 75 10 0 0 10 1013 Southern Hairynosed Wombat 56 33.6 750 Lasiorhinus latifrons Australia Grassland Diprotodontia Mammalia fur crepuscular underground no no no no herbivore hot mixed grass 5 75 10 0 0 10 1014 White-headed Vulture 9.5 34 600 Trigonoceps occipitalis Africa Grassland Falconiformes Aves feathers diurnal air no no yes no saprovore temperate mixed grass 5 75 10 0 0 10 5015 Tammar Wallaby 17.5 18 1500 Macropus eugenii Australia Grassland Marsupialis Mammalia fur nocturnal ground no no yes yes herbivore hot mixed grass 5 75 10 0 0 10 1016 Red-billed Oxpecker 0.11 8 400 Buphagus erythrorhynchus Africa Grassland Passeriformes Aves feathers diurnal air no no yes no sanguinivores temperate mixed grass 5 75 10 0 0 10 5017 Przewalski’s Horse 770 83 2400 Equus caballus przewalskii Asia Grassland Perissodactyla Mammalia fur diurnal ground no no no no herbivore temperate mixed grass 5 75 10 0 0 10 1018 Ground Pangolin 27.5 21.7 800 Manis temminckii Africa Grassland Pholidota Mammalia scales nocturnal underground yes yes yes yes insectivore temperate mixed grass 5 75 10 0 0 10 1019 Mandrill 82.5 32 2500 Mandrillus sphinx Africa Grassland Primates Mammalia fur diurnal trees yes no no yes omnivore hot mixed grass 5 75 10 0 0 10 1520 Komodo Dragon 175 90 500 Varanus komodensis Asia Grassland Sauria Reptilia rough skin diurnal ground yes no yes yes carnivore hot mixed grass 5 75 10 0 0 10 621 Oustalet’s Chameleon 5 23 250 Furcifer oustaleti Africa Grassland Squamata Reptilia scales diurnal trees yes no yes yes insectivore hot mixed grass 5 75 10 0 0 10 622 Ostrich 243 88 1600 Struthio camelus australis Africa Grassland Struthioniformes Aves feathers diurnal ground no no no no omnivore hot mixed grass 5 75 10 0 0 10 623 Emu 93.5 67.5 1200 Dromaiusnovaehollandiae Australia Grassland Struthioniformes Aves feathers diurnal ground no yes yes no omnivore hot mixed grass 5 75 10 0 0 10 624 Galapagos Tortoise 660 48 600 Geochelone nigra South America Grassland Testudines Reptilia carapace diurnal ground no yes no no herbivore hot mixed grass 5 75 10 0 0 10 625 North Island Brown Kiwi 6.93 19.49 300 Apteryx australis Australia Temperate Forest Apterygiformes Aves feathers nocturnal ground no no yes yes carnivore hot mixed trees 75 15 0 0 0 10 626 Canada Lynx 39 27 2400 Lynx canadensis Europe Temperate Forest Carnivora Mammalia fur nocturnal ground no no yes yes carnivore temperate mixed trees 75 15 0 0 0 10 1527 Mountain Lion 150 80 4000 Puma concolor North America Temperate Forest Carnivora Mammalia fur nocturnal ground no no yes yes carnivore temperate mixed trees 75 15 0 0 0 10 1528 Giant Panda 275 60 5000 Ailuropoda melanoleuca Asia Temperate Forest Carnivora Mammalia fur crepuscular trees yes no yes no herbivore temperate mixed trees 75 15 0 0 0 10 1529 Siberian Tiger 440 90 1200 Panthera tigris altaica Asia Temperate Forest Carnivora Mammalia fur crepuscular ground no yes yes yes carnivore temperate mixed trees 75 15 0 0 0 10 1530 Mediterranean Horseshoe Bat 0.03 3 500 Rhinolophus euryale Europe Temperate Forest Chiroptera Mammalia fur nocturnal air no no no no insectivore temperate mixed trees 75 15 0 0 0 10 1531 Tasmanian Devil 15.5 41.5 500 Sarcophilus harrisii Australia Temperate Forest Dasyuromorphia Mammalia fur nocturnal ground no no yes yes carnivore temperate mixed trees 75 15 0 0 0 10 632 Andean Condor 25 44 600 Vultur gryphus South America Temperate Forest Falconiformes Aves feathers diurnal air no no yes no saprovore temperate mixed trees 75 15 0 0 0 10 1533 Giant Anteater 63 30 750 Myrmecophaga tridactyla South America Temperate Forest Pilosa Mammalia fur diurnal ground no yes yes no insectivore temperate mixed trees 75 15 0 0 0 10 1034 Lion-tailed Macaque 24 22 600 Macaca silenus Asia Temperate Forest Primates Mammalia long hair diurnal trees yes no no yes omnivore temperate mixed trees 75 15 0 0 0 10 1535 North American Porcupine 39.75 25.5 600 Erethizon dorsatum North America Temperate Forest Rodentia Mammalia quills nocturnal trees yes no yes yes herbivore temperate mixed trees 75 15 0 0 0 10 1536 Okapi 550 98 3000 Okapia johnstoni Africa Tropical Rainforest Artiodactyla Mammalia fur diurnal ground no no yes no herbivore hot wet trees 75 15 0 0 0 10 1037 Pygmy Hippopotamus 475 62 1000 Hexaprotodon liberiensis Africa Tropical Rainforest Artiodactyla Mammalia skin nocturnal water no yes no no herbivore hot wet trees 75 15 0 0 0 10 1038 Binturong 45 30 1200 Arctictis binturong Asia Tropical Rainforest Carnivora Mammalia fur crepuscular trees yes no yes yes omnivore hot wet trees 75 15 0 0 0 10 639 Clouded Leopard 42.5 36 2500 Neofelis nebulosa Asia Tropical Rainforest Carnivora Mammalia fur nocturnal trees yes no yes no carnivore hot wet trees 75 15 0 0 0 10 1540 Kinkajou 5.5 19 500 Potos flavus South America Tropical Rainforest Carnivora Mammalia fur nocturnal trees yes no no yes nectarivore hot wet trees 75 15 0 0 0 10 1541 Sun Bear 102.5 48 2500 Helarctos malayanus Asia Tropical Rainforest Carnivora Mammalia fur nocturnal trees yes no yes yes omnivore hot wet trees 75 15 0 0 0 10 1542 Ocelot 20 30 1200 Leopardus pardalis South America Tropical Rainforest Carnivora Mammalia fur nocturnal ground no no yes yes carnivore hot wet trees 75 15 0 0 0 10 1543 Sugar Glider 0.24 6 500 Petaurus breviceps Australia Tropical Rainforest Marsupialis Mammalia fur nocturnal trees yes no no no omnivore hot wet trees 75 15 0 0 0 10 1544 Birds of Paradise 0.53 24.5 1200 Paradisaeidae Australia Tropical Rainforest Passeriformes Aves feathers diurnal air yes no yes yes omnivore hot wet trees 75 15 0 0 0 10 5045 Brazillian Tapir 440 72 2000 Tapirus terrestris South America Tropical Rainforest Perissodactyla Mammalia fur diurnal water no yes yes no herbivore hot wet trees 75 15 0 0 0 10 1046 Southern Two-Toed Sloth 13 25 800 Choloepus didactylus South America Tropical Rainforest Pilosa Mammalia fur nocturnal trees yes no yes no herbivore hot wet trees 75 15 0 0 0 10 1047 Bonobo (Pygmy Chimp) 95 32 2400 Pan paniscus Africa Tropical Rainforest Primates Mammalia fur diurnal trees yes no no yes herbivore hot wet trees 75 15 0 0 0 10 1548 Chimpanzee 95 36 2400 Pan troglodytes Africa Tropical Rainforest Primates Mammalia fur diurnal trees yes no no yes omnivore hot wet trees 75 15 0 0 0 10 1549 Golden Lion Tamarin 0.9 10.5 1200 Leontopithecus rosalia Africa Tropical Rainforest Primates Mammalia long hair diurnal trees yes no yes no omnivore hot wet trees 75 15 0 0 0 10 1550 Mangabey 16.1 22.1 1200 Lophocebus albigena Africa Tropical Rainforest Primates Mammalia fur diurnal trees yes no no yes omnivore hot wet trees 75 15 0 0 0 10 1551 Siamang 23 32 2500 Symphalangus syndactylus Asia Tropical Rainforest Primates Mammalia fur diurnal trees yes no yes yes herbivore hot wet trees 75 15 0 0 0 10 1552 Sumatran Orangutan 132.5 54 3000 Pongo pygmaeus abelii Asia Tropical Rainforest Primates Mammalia long hair diurnal trees yes no yes no omnivore hot wet trees 75 15 0 0 0 10 1553 Western Lowland Gorilla 370 60 4000 Gorilla gorilla Africa Tropical Rainforest Primates Mammalia fur diurnal ground no no no yes omnivore hot wet trees 75 15 0 0 0 10 1554 White-tufted-ear Marmoset 0.75 9 600 Callithrix jacchus South America Tropical Rainforest Primates Mammalia fur diurnal trees yes no no no herbivore hot wet trees 75 15 0 0 0 10 1555 Ring Tailed Lemur 6.25 17.8 2400 Lemur catta Africa Tropical Rainforest Primates Mammalia fur diurnal trees yes no no yes herbivore hot wet trees 75 15 0 0 0 10 1556 Brazilian Agouti 6 20 500 Dasyprocta leporina South America Tropical Rainforest Rodentia Mammalia fur crepuscular ground no no yes yes herbivore hot wet trees 75 15 0 0 0 10 657 Arctic Fox 11.75 22.4 1000 Vulpes lagopus North America Tundra Carnivora Mammalia fur nocturnal ground no no no no carnivore cold dry ice 0 10 15 0 75 0 1058 Gray Wolf 80 67 2400 Canis lupus North America Tundra Carnivora Mammalia fur nocturnal ground no no no no carnivore cold dry ice 0 10 15 0 75 0 1559 Polar Bear 1065 100 7000 Ursus maritimus North America Tundra Carnivora Mammalia fur diurnal water no yes yes no carnivore cold dry ice 0 0 0 0 50 50 1560 Norway Lemming 0.2 4.9 1000 Lemmus lemmus Europe Tundra Rodentia Mammalia fur nocturnal underground no no no no herbivore cold dry ice 0 10 15 0 75 0 661 Little Penguin 2 16 1200 Eudyptula minor Australia Tundra Sphenisciformes Aves feathers diurnal water no yes yes no piscivores cold dry ice 0 0 0 0 50 50 662 Goliath Frog 6.6 10.25 400 Conraua goliath Africa Wetlands Anura Amphibia skin nocturnal water no yes yes yes omnivore hot mixed water 0 15 15 0 0 70 663 Japanese Giant Salamander 55 60 600 Andrias japonicus Asia Wetlands Caudata Amphibia skin nocturnal water no yes yes yes carnivore hot mixed water 0 15 15 0 0 70 664 Sacred Ibis 3 30 500 Threskiornis aethiopicus Africa Wetlands Ciconiiformes Aves feathers diurnal water no yes yes no insectivore hot mixed water 0 15 15 0 0 70 665 American Alligator 330 147.5 800 Alligator mississippiensis North America Wetlands Crocodilia Reptilia scales diurnal water no yes no no carnivore hot mixed water 0 15 15 0 0 70 666 Johnston’s Crocodile 30 102 1000 Crocodylus johnstoni Australia Wetlands Crocodilia Reptilia scales diurnal water no yes yes yes carnivore hot mixed water 0 15 15 0 0 70 667 Green Peafowl 7.2 81 600 Pavo muticus Asia Wetlands Galliformes Aves feathers diurnal ground no no yes no omnivore hot mixed water 0 15 15 0 0 70 668 Andean Flamingo 6.16 47.5 600 Phoenicoparrus andinus South America Wetlands Phoenicopteriformes Aves feathers diurnal water no yes no no herbivore hot mixed water 0 15 15 0 0 70 1069 Capybara 100 48 800 Capybara South America Wetlands Rodentia Mammalia fur crepuscular water no yes no yes herbivore hot mixed water 0 15 15 0 0 70 670 sea otter 64.9 49 2000 Enhydra lutris Asia, N. America coastal Carnivora Mammalia fur diurnal water no yes yes no piscivore temperate wet water 0 0 0 10 0 90 71 bush baby 0.5 8 500 Galago senegalensis Africa woodlland Primates Mammalia fur nocturnal trees yes omnivore hot mixed trees 50 25 25 0 0 0 72 zebra duiker 38.5 3200 Cephalophus zebra Africa forest Artiodactyla Mammalia fur diurnal ground no no yes no herbivore hot mixed trees 75 15 10 0 0 0 73 gray-headed flying fox 1.5 10 2400 Pteropus poliocephalus Australia wetlands Chiroptera Mammalia fur nocturnal air yes no no no herbivoe temperate mixed trees 80 20 0 0 0 0 74 mountain gazelle 51.15 4000 Gazella gazella Africa scrub forest, mountain Artiodactyla Mammalia fur diurnal ground no no yes no herbivore hot dry soil 0 25 25 50 0 0 75 fennec fox 3.3 2400 Vulpes zerda Africa desert Carni vora Mammalia fur nocturnal underground no no no no omnivore hot dry soil 0 15 15 70 0 0 76 virginia opossum 11 18 1200 Didelphis virginiana North America woodland Didelphimorphia Mammalia fur nocturnal trees yes no yes omnivore temperate mixed trees 65 20 10 5 0 0 77 southern tamandua 9.9 28 1600 Tamandua tetradactyla South America Rainforest Pilosa Mammalia fur nocturnal trees yes no insectivore hot wet trees 75 0 15 0 0 10 78 giraffe 3421 204 8000 Giraffa camelopardalis Africa Grassland Artiodactyla Mammalia fur diurnal ground no no no no herbivore hot dry grass 15 65 10 10 0 0 79 Sumatran rhinoceros 3080 110 4000 Dicerorhinus sumatrensis Asia Rainforest Perissodactyla Mammalia skin diurnal water no yes yes no herbivore hot wet trees 50 0 15 10 0 25 80 aardvark 154 2600 Orycteropus afer Africa grassland Tubulidentata Mammalia skin, hair nocturnal underground no no no insectivore hot mixed grass 10 50 25 15 0 0 81 rock hyrax 8 21 1200 Procavia capensis Africa grassland Hyracoidea Mammalia fur diurnal underground no no no no herbivore hot dry soil, rock 0 15 15 70 0 0 82 mouse deer 14 30 500 Tragulus napu Asia Rainforest Artiodactyla Mammalia fur nocturnal ground no no yes yes, no herbivore hot humid trees 65 15 0 5 0 15 83 eastern barred bandicoot 1.4 10 1200 Perameles gunnii Australia grassland Peramelemorphia Mammalia fur nocturnal ground no no yes no insectivore temperate mixed grass 10 75 0 10 0 0 84 long-eared jerboa 0.06 3.15 700 Euchoreutes naso Asia desert Rodentia Mammalia fur nocturnal underground insectivore cold dry soil 0 0 0 100 0 0 85 northern viscacha 2.75 15 600 Lagidium peruanum South America mountains Rodentia Mammalia fur crepuscular ground no no no no herbivore temperate dry soil 0 10 10 75 0 5 86 rosy-faced lovebird 0.12 5.91 1000 Agapornis roseicollis Africa desert Psittaciformes Aves feathers diurnal air yes no no no herbivore hot dry soil 10 0 10 80 0 0 87 California legless lizard 0.007 6.68 300 Anniella pulchra North America desert Squamata Reptilia scales nocturnal underground no no yes yes, no insectivore hot dry soil 0 0 25 75 0 0 88 demoiselle crane 5.181 35.43 1200 Anthropoides virgo Asia grassland Gruiformes Aves feathers diurnal air no no “yes, no” no omnivore temperate dry soil 0 15 15 50 0 20 89 burrowing owl 0.3125 10 1200 Athene cunicularia South America desert Strigiformes Aves feathers diurnal underground no no no no carnivore temperate dry soil 5 15 10 70 0 0 90 spot-billed pelican 10.78 126 1200 Pelecanus philippensis Asia wetlands Pelecaniformes Aves feathers diurnal air no yes no no carnivore hot humid water 10 10 10 10 0 60 91 short-beaked echidna 10 15 1200 Tachyglossus aculeatus Australia Monotremata Mammalia fur diurnal underground no no yes no insectivore temperate mixed soil 15 25 25 35 0 0

AvG. WEiG

HT (LB)

AvG. LENGTH (iN

)

AREASCiE

NTiFiC NAM

E

CONTiNENT

HABiTAT

ORDER

CLASS

SKiNACTiv

E

SPECiALiZ

ATiON

COMM

ON NAM

E

PARAMETRiC MODELiNG iNTRO 163

THE DATABASE1 Oryx 440 62.5 4000 Oryx dammah Asia Desert Artiodactyla Mammalia fur diurnal ground no no no no herbivore hot dry soil 0 0 0 100 0 0 102 Naked Mole Rat 0.09 3.5 400 Heterocephalus glaber Africa Desert Rodentia Mammalia skin diurnal underground no no no yes herbivore hot dry soil 0 0 0 100 0 0 63 Zebra 887.5 110 3600 Equus grevyi Africa Grassland Perissodactyla Mammalia fur diurnal ground no no no no herbivore hot mixed grass 5 75 10 0 0 10 104 Antelope 385 90 6000 Hippotragus niger niger Africa Grassland Artiodactyla Mammalia fur diurnal ground no no no no herbivore hot mixed grass 5 75 10 0 0 10 105 Warthog 220 47 2500 Phacochoerus aethiopicus Africa Grassland Artiodactyla Mammalia fur diurnal ground no no no no herbivore hot mixed grass 5 75 10 0 0 10 106 Caracal 30 32.5 1200 Caracal caracal Africa Grassland Carnivora Mammalia fur nocturnal ground no no yes no carnivore hot mixed grass 5 75 10 0 0 10 157 Serval 29.5 34 1200 Leptailurus serval Africa Grassland Carnivora Mammalia fur crepuscular ground no no yes no carnivore hot mixed grass 5 75 10 0 0 10 158 Spotted Hyena 85 42 2500 Crocuta crocuta Africa Grassland Carnivora Mammalia fur diurnal ground no no yes no carnivore hot mixed grass 5 75 10 0 0 10 159 Meerkat 2 12 1000 Suricata suricatta suricatta Africa Grassland Carnivora Mammalia fur diurnal ground no no no no carnivore hot mixed grass 5 75 10 0 0 10 6

10 White-nosed Coati 8.8 21 800 Nasua narica North America Grassland Carnivora Mammalia fur diurnal trees yes no no yes omnivore temperate mixed grass 5 75 10 0 0 10 611 Southern Three-banded Armadillo 3.25 12 500 Tolypeutes matacus South America Grassland Cingulata Mammalia carapace nocturnal underground no no yes no insectivore hot mixed grass 5 75 10 0 0 10 612 Koala 20 30 600 Phascolarctos cinereus Australia Grassland Diprotodontia Mammalia fur diurnal ground no no yes yes herbivore hot mixed grass 5 75 10 0 0 10 1013 Southern Hairynosed Wombat 56 33.6 750 Lasiorhinus latifrons Australia Grassland Diprotodontia Mammalia fur crepuscular underground no no no no herbivore hot mixed grass 5 75 10 0 0 10 1014 White-headed Vulture 9.5 34 600 Trigonoceps occipitalis Africa Grassland Falconiformes Aves feathers diurnal air no no yes no saprovore temperate mixed grass 5 75 10 0 0 10 5015 Tammar Wallaby 17.5 18 1500 Macropus eugenii Australia Grassland Marsupialis Mammalia fur nocturnal ground no no yes yes herbivore hot mixed grass 5 75 10 0 0 10 1016 Red-billed Oxpecker 0.11 8 400 Buphagus erythrorhynchus Africa Grassland Passeriformes Aves feathers diurnal air no no yes no sanguinivores temperate mixed grass 5 75 10 0 0 10 5017 Przewalski’s Horse 770 83 2400 Equus caballus przewalskii Asia Grassland Perissodactyla Mammalia fur diurnal ground no no no no herbivore temperate mixed grass 5 75 10 0 0 10 1018 Ground Pangolin 27.5 21.7 800 Manis temminckii Africa Grassland Pholidota Mammalia scales nocturnal underground yes yes yes yes insectivore temperate mixed grass 5 75 10 0 0 10 1019 Mandrill 82.5 32 2500 Mandrillus sphinx Africa Grassland Primates Mammalia fur diurnal trees yes no no yes omnivore hot mixed grass 5 75 10 0 0 10 1520 Komodo Dragon 175 90 500 Varanus komodensis Asia Grassland Sauria Reptilia rough skin diurnal ground yes no yes yes carnivore hot mixed grass 5 75 10 0 0 10 621 Oustalet’s Chameleon 5 23 250 Furcifer oustaleti Africa Grassland Squamata Reptilia scales diurnal trees yes no yes yes insectivore hot mixed grass 5 75 10 0 0 10 622 Ostrich 243 88 1600 Struthio camelus australis Africa Grassland Struthioniformes Aves feathers diurnal ground no no no no omnivore hot mixed grass 5 75 10 0 0 10 623 Emu 93.5 67.5 1200 Dromaiusnovaehollandiae Australia Grassland Struthioniformes Aves feathers diurnal ground no yes yes no omnivore hot mixed grass 5 75 10 0 0 10 624 Galapagos Tortoise 660 48 600 Geochelone nigra South America Grassland Testudines Reptilia carapace diurnal ground no yes no no herbivore hot mixed grass 5 75 10 0 0 10 625 North Island Brown Kiwi 6.93 19.49 300 Apteryx australis Australia Temperate Forest Apterygiformes Aves feathers nocturnal ground no no yes yes carnivore hot mixed trees 75 15 0 0 0 10 626 Canada Lynx 39 27 2400 Lynx canadensis Europe Temperate Forest Carnivora Mammalia fur nocturnal ground no no yes yes carnivore temperate mixed trees 75 15 0 0 0 10 1527 Mountain Lion 150 80 4000 Puma concolor North America Temperate Forest Carnivora Mammalia fur nocturnal ground no no yes yes carnivore temperate mixed trees 75 15 0 0 0 10 1528 Giant Panda 275 60 5000 Ailuropoda melanoleuca Asia Temperate Forest Carnivora Mammalia fur crepuscular trees yes no yes no herbivore temperate mixed trees 75 15 0 0 0 10 1529 Siberian Tiger 440 90 1200 Panthera tigris altaica Asia Temperate Forest Carnivora Mammalia fur crepuscular ground no yes yes yes carnivore temperate mixed trees 75 15 0 0 0 10 1530 Mediterranean Horseshoe Bat 0.03 3 500 Rhinolophus euryale Europe Temperate Forest Chiroptera Mammalia fur nocturnal air no no no no insectivore temperate mixed trees 75 15 0 0 0 10 1531 Tasmanian Devil 15.5 41.5 500 Sarcophilus harrisii Australia Temperate Forest Dasyuromorphia Mammalia fur nocturnal ground no no yes yes carnivore temperate mixed trees 75 15 0 0 0 10 632 Andean Condor 25 44 600 Vultur gryphus South America Temperate Forest Falconiformes Aves feathers diurnal air no no yes no saprovore temperate mixed trees 75 15 0 0 0 10 1533 Giant Anteater 63 30 750 Myrmecophaga tridactyla South America Temperate Forest Pilosa Mammalia fur diurnal ground no yes yes no insectivore temperate mixed trees 75 15 0 0 0 10 1034 Lion-tailed Macaque 24 22 600 Macaca silenus Asia Temperate Forest Primates Mammalia long hair diurnal trees yes no no yes omnivore temperate mixed trees 75 15 0 0 0 10 1535 North American Porcupine 39.75 25.5 600 Erethizon dorsatum North America Temperate Forest Rodentia Mammalia quills nocturnal trees yes no yes yes herbivore temperate mixed trees 75 15 0 0 0 10 1536 Okapi 550 98 3000 Okapia johnstoni Africa Tropical Rainforest Artiodactyla Mammalia fur diurnal ground no no yes no herbivore hot wet trees 75 15 0 0 0 10 1037 Pygmy Hippopotamus 475 62 1000 Hexaprotodon liberiensis Africa Tropical Rainforest Artiodactyla Mammalia skin nocturnal water no yes no no herbivore hot wet trees 75 15 0 0 0 10 1038 Binturong 45 30 1200 Arctictis binturong Asia Tropical Rainforest Carnivora Mammalia fur crepuscular trees yes no yes yes omnivore hot wet trees 75 15 0 0 0 10 639 Clouded Leopard 42.5 36 2500 Neofelis nebulosa Asia Tropical Rainforest Carnivora Mammalia fur nocturnal trees yes no yes no carnivore hot wet trees 75 15 0 0 0 10 1540 Kinkajou 5.5 19 500 Potos flavus South America Tropical Rainforest Carnivora Mammalia fur nocturnal trees yes no no yes nectarivore hot wet trees 75 15 0 0 0 10 1541 Sun Bear 102.5 48 2500 Helarctos malayanus Asia Tropical Rainforest Carnivora Mammalia fur nocturnal trees yes no yes yes omnivore hot wet trees 75 15 0 0 0 10 1542 Ocelot 20 30 1200 Leopardus pardalis South America Tropical Rainforest Carnivora Mammalia fur nocturnal ground no no yes yes carnivore hot wet trees 75 15 0 0 0 10 1543 Sugar Glider 0.24 6 500 Petaurus breviceps Australia Tropical Rainforest Marsupialis Mammalia fur nocturnal trees yes no no no omnivore hot wet trees 75 15 0 0 0 10 1544 Birds of Paradise 0.53 24.5 1200 Paradisaeidae Australia Tropical Rainforest Passeriformes Aves feathers diurnal air yes no yes yes omnivore hot wet trees 75 15 0 0 0 10 5045 Brazillian Tapir 440 72 2000 Tapirus terrestris South America Tropical Rainforest Perissodactyla Mammalia fur diurnal water no yes yes no herbivore hot wet trees 75 15 0 0 0 10 1046 Southern Two-Toed Sloth 13 25 800 Choloepus didactylus South America Tropical Rainforest Pilosa Mammalia fur nocturnal trees yes no yes no herbivore hot wet trees 75 15 0 0 0 10 1047 Bonobo (Pygmy Chimp) 95 32 2400 Pan paniscus Africa Tropical Rainforest Primates Mammalia fur diurnal trees yes no no yes herbivore hot wet trees 75 15 0 0 0 10 1548 Chimpanzee 95 36 2400 Pan troglodytes Africa Tropical Rainforest Primates Mammalia fur diurnal trees yes no no yes omnivore hot wet trees 75 15 0 0 0 10 1549 Golden Lion Tamarin 0.9 10.5 1200 Leontopithecus rosalia Africa Tropical Rainforest Primates Mammalia long hair diurnal trees yes no yes no omnivore hot wet trees 75 15 0 0 0 10 1550 Mangabey 16.1 22.1 1200 Lophocebus albigena Africa Tropical Rainforest Primates Mammalia fur diurnal trees yes no no yes omnivore hot wet trees 75 15 0 0 0 10 1551 Siamang 23 32 2500 Symphalangus syndactylus Asia Tropical Rainforest Primates Mammalia fur diurnal trees yes no yes yes herbivore hot wet trees 75 15 0 0 0 10 1552 Sumatran Orangutan 132.5 54 3000 Pongo pygmaeus abelii Asia Tropical Rainforest Primates Mammalia long hair diurnal trees yes no yes no omnivore hot wet trees 75 15 0 0 0 10 1553 Western Lowland Gorilla 370 60 4000 Gorilla gorilla Africa Tropical Rainforest Primates Mammalia fur diurnal ground no no no yes omnivore hot wet trees 75 15 0 0 0 10 1554 White-tufted-ear Marmoset 0.75 9 600 Callithrix jacchus South America Tropical Rainforest Primates Mammalia fur diurnal trees yes no no no herbivore hot wet trees 75 15 0 0 0 10 1555 Ring Tailed Lemur 6.25 17.8 2400 Lemur catta Africa Tropical Rainforest Primates Mammalia fur diurnal trees yes no no yes herbivore hot wet trees 75 15 0 0 0 10 1556 Brazilian Agouti 6 20 500 Dasyprocta leporina South America Tropical Rainforest Rodentia Mammalia fur crepuscular ground no no yes yes herbivore hot wet trees 75 15 0 0 0 10 657 Arctic Fox 11.75 22.4 1000 Vulpes lagopus North America Tundra Carnivora Mammalia fur nocturnal ground no no no no carnivore cold dry ice 0 10 15 0 75 0 1058 Gray Wolf 80 67 2400 Canis lupus North America Tundra Carnivora Mammalia fur nocturnal ground no no no no carnivore cold dry ice 0 10 15 0 75 0 1559 Polar Bear 1065 100 7000 Ursus maritimus North America Tundra Carnivora Mammalia fur diurnal water no yes yes no carnivore cold dry ice 0 0 0 0 50 50 1560 Norway Lemming 0.2 4.9 1000 Lemmus lemmus Europe Tundra Rodentia Mammalia fur nocturnal underground no no no no herbivore cold dry ice 0 10 15 0 75 0 661 Little Penguin 2 16 1200 Eudyptula minor Australia Tundra Sphenisciformes Aves feathers diurnal water no yes yes no piscivores cold dry ice 0 0 0 0 50 50 662 Goliath Frog 6.6 10.25 400 Conraua goliath Africa Wetlands Anura Amphibia skin nocturnal water no yes yes yes omnivore hot mixed water 0 15 15 0 0 70 663 Japanese Giant Salamander 55 60 600 Andrias japonicus Asia Wetlands Caudata Amphibia skin nocturnal water no yes yes yes carnivore hot mixed water 0 15 15 0 0 70 664 Sacred Ibis 3 30 500 Threskiornis aethiopicus Africa Wetlands Ciconiiformes Aves feathers diurnal water no yes yes no insectivore hot mixed water 0 15 15 0 0 70 665 American Alligator 330 147.5 800 Alligator mississippiensis North America Wetlands Crocodilia Reptilia scales diurnal water no yes no no carnivore hot mixed water 0 15 15 0 0 70 666 Johnston’s Crocodile 30 102 1000 Crocodylus johnstoni Australia Wetlands Crocodilia Reptilia scales diurnal water no yes yes yes carnivore hot mixed water 0 15 15 0 0 70 667 Green Peafowl 7.2 81 600 Pavo muticus Asia Wetlands Galliformes Aves feathers diurnal ground no no yes no omnivore hot mixed water 0 15 15 0 0 70 668 Andean Flamingo 6.16 47.5 600 Phoenicoparrus andinus South America Wetlands Phoenicopteriformes Aves feathers diurnal water no yes no no herbivore hot mixed water 0 15 15 0 0 70 1069 Capybara 100 48 800 Capybara South America Wetlands Rodentia Mammalia fur crepuscular water no yes no yes herbivore hot mixed water 0 15 15 0 0 70 670 sea otter 64.9 49 2000 Enhydra lutris Asia, N. America coastal Carnivora Mammalia fur diurnal water no yes yes no piscivore temperate wet water 0 0 0 10 0 90 71 bush baby 0.5 8 500 Galago senegalensis Africa woodlland Primates Mammalia fur nocturnal trees yes omnivore hot mixed trees 50 25 25 0 0 0 72 zebra duiker 38.5 3200 Cephalophus zebra Africa forest Artiodactyla Mammalia fur diurnal ground no no yes no herbivore hot mixed trees 75 15 10 0 0 0 73 gray-headed flying fox 1.5 10 2400 Pteropus poliocephalus Australia wetlands Chiroptera Mammalia fur nocturnal air yes no no no herbivoe temperate mixed trees 80 20 0 0 0 0 74 mountain gazelle 51.15 4000 Gazella gazella Africa scrub forest, mountain Artiodactyla Mammalia fur diurnal ground no no yes no herbivore hot dry soil 0 25 25 50 0 0 75 fennec fox 3.3 2400 Vulpes zerda Africa desert Carni vora Mammalia fur nocturnal underground no no no no omnivore hot dry soil 0 15 15 70 0 0 76 virginia opossum 11 18 1200 Didelphis virginiana North America woodland Didelphimorphia Mammalia fur nocturnal trees yes no yes omnivore temperate mixed trees 65 20 10 5 0 0 77 southern tamandua 9.9 28 1600 Tamandua tetradactyla South America Rainforest Pilosa Mammalia fur nocturnal trees yes no insectivore hot wet trees 75 0 15 0 0 10 78 giraffe 3421 204 8000 Giraffa camelopardalis Africa Grassland Artiodactyla Mammalia fur diurnal ground no no no no herbivore hot dry grass 15 65 10 10 0 0 79 Sumatran rhinoceros 3080 110 4000 Dicerorhinus sumatrensis Asia Rainforest Perissodactyla Mammalia skin diurnal water no yes yes no herbivore hot wet trees 50 0 15 10 0 25 80 aardvark 154 2600 Orycteropus afer Africa grassland Tubulidentata Mammalia skin, hair nocturnal underground no no no insectivore hot mixed grass 10 50 25 15 0 0 81 rock hyrax 8 21 1200 Procavia capensis Africa grassland Hyracoidea Mammalia fur diurnal underground no no no no herbivore hot dry soil, rock 0 15 15 70 0 0 82 mouse deer 14 30 500 Tragulus napu Asia Rainforest Artiodactyla Mammalia fur nocturnal ground no no yes yes, no herbivore hot humid trees 65 15 0 5 0 15 83 eastern barred bandicoot 1.4 10 1200 Perameles gunnii Australia grassland Peramelemorphia Mammalia fur nocturnal ground no no yes no insectivore temperate mixed grass 10 75 0 10 0 0 84 long-eared jerboa 0.06 3.15 700 Euchoreutes naso Asia desert Rodentia Mammalia fur nocturnal underground insectivore cold dry soil 0 0 0 100 0 0 85 northern viscacha 2.75 15 600 Lagidium peruanum South America mountains Rodentia Mammalia fur crepuscular ground no no no no herbivore temperate dry soil 0 10 10 75 0 5 86 rosy-faced lovebird 0.12 5.91 1000 Agapornis roseicollis Africa desert Psittaciformes Aves feathers diurnal air yes no no no herbivore hot dry soil 10 0 10 80 0 0 87 California legless lizard 0.007 6.68 300 Anniella pulchra North America desert Squamata Reptilia scales nocturnal underground no no yes yes, no insectivore hot dry soil 0 0 25 75 0 0 88 demoiselle crane 5.181 35.43 1200 Anthropoides virgo Asia grassland Gruiformes Aves feathers diurnal air no no “yes, no” no omnivore temperate dry soil 0 15 15 50 0 20 89 burrowing owl 0.3125 10 1200 Athene cunicularia South America desert Strigiformes Aves feathers diurnal underground no no no no carnivore temperate dry soil 5 15 10 70 0 0 90 spot-billed pelican 10.78 126 1200 Pelecanus philippensis Asia wetlands Pelecaniformes Aves feathers diurnal air no yes no no carnivore hot humid water 10 10 10 10 0 60 91 short-beaked echidna 10 15 1200 Tachyglossus aculeatus Australia Monotremata Mammalia fur diurnal underground no no yes no insectivore temperate mixed soil 15 25 25 35 0 0

ARBOREAL

NATATORiAL

SOLiTARY

SEDENTARY

DiET

TEMPERATURE

HUMiD

iTY

COvER

TREES

GRASS

BUSHES

SOiLiCE W

ATERHEiG

HT OF

TRENCH (FT)

1 //This Processing simulation was developed to augment a process of laying out program. It consists of a

physics simulation of particles connected by links. Its primary function is one of sorting animals

into a series of lists according to different criteria and allocating each with different rules and

behaviours.

2 import upsdnPhysics.*;

3 import controlP5.*;

4 import superCAD.*;

5 import damkjer.ocd.*;

6 import toxi.math.noise.*;

7 import processing.opengl.*;

8 import processing.pdf.*;

9

10 PImage aerialPhoto;

11 PFont font;

12 Camera camera1;

13

14 //initialization of variables

15 boolean dispEdges, dispServiceGrid, record = false;

16 boolean export = false;

17 boolean dispTriangulation = false;

18 boolean getConnected = false;

19 boolean stillTicking = true;

20 int tickCount = 0,

21 maxTicks = 1000;

22

23 float drawingScale = 1.0/5.0 ;

24 float transX = 0;

25 float transY = 0;

26

27 ControlP5 controlP5;

28 int sliderValue = 32;

29

30 int gateDist = sliderValue;

31 boolean debugging = false;

32

33 float attDist =1;

34 float attStrength = 3200;

35 float aqd, aqs;

36 float lLength, lStrength, lDamping =10 ;

37 float clStrength, clDamping = 10, clLength;

38 float lcStrength, lcDamping = 10, lcLength;

39 float ahlStrength, ahlDamping = 10, ahlLength;

40 float allSep = 3, dietSep = 3, strataSep = 3;

41 float gateFactor = 1;

42 float[][] AHregionCoordinates;

43

44 Zoo z = new Zoo();

45 Vector3D animalsCentroid;

46

47 int count = 0;

48

49 void setup() {

50 size(1026,830);

51

52 controlP5 = new ControlP5(this);

53 controlP5.setAutoInitialization(true);

54

55 Slider s1 = controlP5.addSlider(“gateDist”,0,100,128,25,25,10,100);

56

57 //Anchor Links controls

58 Slider s2 = controlP5.addSlider(“lLength”,

59 0, 500, 1, 75,25,10,100);

60 Slider s3 = controlP5.addSlider(“lStrength”,

61 0, 10, 1, 125,25,10,100);

62 Slider s4 = controlP5.addSlider(“clLength”,

63 0, 500, 1, 175, 25, 10, 100);

64 Slider s5 = controlP5.addSlider(“clStrength”,

65 0, 10, 1, 225, 25, 10, 100);

66

67 Slider s6 = controlP5.addSlider(“lcLength”,

68 0, 500, 1, 275, 25, 10, 100);

69 Slider s7 = controlP5.addSlider(“lcStrength”,

70 0, 10, 1, 325, 25, 10, 100);

71 Slider s8 = controlP5.addSlider(“allSep”,

72 0, 10, 10, 375, 25, 10, 100);

73 Slider s9 = controlP5.addSlider(“dietSep”,

74 0, 10, 10, 425, 25, 10, 100);

75 Slider s10 = controlP5.addSlider(“strataSep”,

76 0, 10, 10, 475, 25, 10, 100);

77 Slider s11 = controlP5.addSlider(“gateFactor”,

78 0, 5, 5, 525, 25, 10, 100);

79 Slider s12 = controlP5.addSlider(“ahlLength”,

80 0, 500, 1, 575, 25, 10, 100);

81 Slider s13 = controlP5.addSlider(“ahlStrength”,

82 0, 10, 1, 625, 25, 10, 100);

83

84 frameRate(24);

85 aerialPhoto = loadImage(“lgp-mcp-04-1pxEQ1ft-cropped-1-5b.png”);

86 setupSpecials();

87

88 font = createFont(“Silkscreen-9”, 9);

89 textFont(font, 9);

90

91 ps = new ParticleSystem( 0, 0.25 );

92

93 initAnchors();

94 initAnimals();

95 initHumans();

96 smooth();

97 }

98

99 void draw() {

100 if (record) {

101 // Note that #### will be replaced with the frame number. Fancy!

102 beginRecord(PDF, “frame-####.pdf”);

103 saveFrame(“animal-FDL-####.png”);

104 }

105 if(count==200 & !getConnected){

106 getConnected= true;

107 }

108

109 if(getConnected){

110 println(“Get connected!”);

111 addMoreLinks();

112 getConnected=!getConnected;

113 }

114

115

116 adjustLinks();

117 image(aerialPhoto,0,0);

118 fill(0,75);

119 rect(10,10,width-20, 130);

120 float[][] points = new float[z.animals.size()][2];

121 for (int i = 0; i < z.animals.size(); i++) {

122 Animal a = (Animal)z.animals.get(i);

123 points[i][0] = a.p.position.x;

THE FORCE DiRECTED LAYOUT SiMULATiON 165

THE FORCE DiRECTED LAYOUT SiMULATiON

REFER TO "ENviRONMENTAL CALiBRATiON"iN

CHAPTER 2: CONSTRUCTiNG THE ZOO MACHiNE

124 points[i][1] = a.p.position.y;

125 }

126 Animal y = (Animal)z.animals.get(1);

127

128 Delaunay mesh = new Delaunay(points);

129 Hull myHull = new Hull( points );

130

131 MPolygon myRegion = myHull.getRegion();

132 AHregionCoordinates = myRegion.getCoords();

133

134 tickCount++;

135 if(tickCount > maxTicks){

136 stillTicking = false;

137 }

138

139 animalsCentroid = centroid(z.animals);

140

141 centroid.p.position().set(animalsCentroid.x, animalsCentroid.y, 0.0);

142

143 if (mousePressed && mouseY>140){

144 fixedP.position().set(mouseX, mouseY, 0);

145 fixedP.velocity.clear();

146 }

147

148 ps.tick( 0.1 );

149 z.draw();

150

151 if (dispServiceGrid){

152 drawVisitorGrid();

153 }

154 if(dispEdges){

155 drawEdges();

156 }

157

158 if(dispTriangulation){

159 // draw the trianguation.

160 float[][] edges = mesh.getEdges();

161 for(int i=0; i<edges.length; i++){

162 float startX = edges[i][0];

163 float startY = edges[i][1];

164 float endX = edges[i][2];

165 float endY = edges[i][3];

166 float d = distance(startX, startY, endX, endY);

167 strokeWeight(1.4);

168 if(d <= gateDist){

169 stroke(255,0,222)

170 }

171 else{


173 stroke(50);

174 }

175 line( startX, startY, endX, endY );

176 }

177 }

178

179 //screengrab

180 if (keyPressed && key == ‘o’){

181 saveFrame(“animal-FDL-####.png”);

182 }

183 //export geometry to text filep

184 if (export) {

185 clusterAnalysis(mesh);

186

187 exportToTxt(mesh);

188 export=!export;

189 }

190 if (record) {

191 endRecord();

192 record = false;

193 }

194

195 for (int i=0; i<z.animals.size(); i++) {


197 handleBoundaryCollisions( a.p );

198 }

199 count++;

200 }

201

202 void drawEdges(){

203 fill(255,0);

204 strokeWeight(1);

205 stroke(0, 50);

206

207 beginShape( );

208 for (int i = 0; i < anchoredLinks.size(); i++) {

209 Link l = (Link)anchoredLinks.get(i);

210 Particle a = l.s.getOneEnd();

211 Particle b = l.s.getTheOtherEnd();

212 line(a.position.x, a.position.y, b.position.x, b.position.y);

213 }

214 endShape();

215 stroke(255, 0,0,50);

216 beginShape( );

217 for (int i = 0; i < centroidLinks.size(); i++) {

218 Link l = (Link)centroidLinks.get(i);

219 Particle a = l.s.getOneEnd();

220 Particle b = l.s.getTheOtherEnd();


222 }

223 endShape();

224 }

225

226 // really basic collision strategy:

227 // sides of the window are walls

228 // if it hits a wall pull it outside the wall and flip the direction of the velocity

229 // the collisions aren’t perfect so we take them down a notch too

230 void handleBoundaryCollisions( Particle p ){

231 if ( p.position.x < 350 || p.position.x > width-200 ){

232 p.velocity.set( -0.9*p.velocity.x, p.velocity.y, 0 );

233 p.position().set( constrain( p.position.x, 350, width-200 ), constrain( p.position.y, 350,

height-175 ), 0 );

234 }

235

236 if ( p.position.y < 350 || p.position.y > height-175 ){

237 p.velocity.set( p.velocity.x, -0.9*p.velocity.y, 0 );

238 p.position().set( constrain( p.position.x, 300, width-150 ), constrain( p.position.y, 350,

height-175 ), 0 );

239 }

240

241 if ( p.position.x >= 660 && p.position.y <= 500){

242 p.velocity.set( -0.9*p.velocity.x, -0.9*p.velocity.y, 0 );

243 }

244 }

1 //Section devoted to the zoo class. Zoo is a parent to all Spaces, animal and human as well as all Links.

2 Particle carnivore, p;

3 ParticleSystem ps;

4 Particle fixedP;

5 Spring s;

6 Anchor[] specials;

7 Anchor center, hot, cold, wet, dry, temperate, mixed, nocturnal, centroid, humanCounter,

humanCounter2, supportAnchor, north, south, east, west, landAnchor, waterAnchor,

treesAnchor, mountainAnchor;

APPENDiCES 166

8

9 Space selectedSpace = null;

10 Space dragSpace = null;

11 Space hoverSpace = null;

12

13 class Zoo {

14 ArrayList anchors;

15 ArrayList animals;

16 ArrayList friends, foes;

17 ArrayList humans;

18 ArrayList visitors;

19 ArrayList dietLinks, stratumLinks;

20 ArrayList wetLinks;

21 ArrayList eating, sitting;

22 ArrayList north, south, east, west;

23 ArrayList support;

24 ArrayList aquarters, aquartersLinks;

25

26 //attraction groups

27 ArrayList aqAtt, aeAtt, allAtt, dietAtt, lcAtt;

28

29 //spring separation test

30 ArrayList supportLinks;

31 ArrayList visitorLinks;

32 ArrayList lcLinks;

33

34 ArrayList animalHumanLinks;

35

36 ArrayList spaces;

37 HashMap spacesById, animalsById;

38 ArrayList edges;

39 HashMap edgesFrom;

40 HashMap edgesTo;

41

42 Zoo() {

43 wetLinks = new ArrayList();

44

45 anchors = new ArrayList();

46 spaces = new ArrayList();

47 animals = new ArrayList();

48 friends = new ArrayList();

49 foes = new ArrayList();

50 humans = new ArrayList();

51

52 dietLinks = new ArrayList();

53 stratumLinks = new ArrayList();

54 lcLinks = new ArrayList();

55

56 eating = new ArrayList();

57 sitting = new ArrayList();

58 north = new ArrayList();

59 south = new ArrayList();

60 west = new ArrayList();

61 east = new ArrayList();

62

63 visitors = new ArrayList();

64 visitorLinks = new ArrayList();

65

66 support = new ArrayList();

67 supportLinks = new ArrayList();

68

69 aquarters = new ArrayList();

70 aquartersLinks = new ArrayList();

71

72 animalHumanLinks = new ArrayList();

73

74 aqAtt = new ArrayList();

75 aeAtt = new ArrayList();

76 allAtt = new ArrayList();

77 dietAtt = new ArrayList();

78 lcAtt = new ArrayList();

79

80 animalsById = new HashMap();

81 spacesById = new HashMap();

82

83 edges = new ArrayList();

84 edgesFrom = new HashMap();

85 edgesTo = new HashMap();

86 }

87

88 void setSelectedSpace(Space n) {

89 selectedSpace = n;

90 }

91

92 Space getSelectedSpace() {

93 return selectedSpace;

94 }

95

96 void setHoverSpace(Space n) {

97 hoverSpace = n;

98 }

99

100 Space getHoverSpace() {

101 return hoverSpace;

102 }

103

104 void setDragSpace(Space n) {

105 dragSpace = n;

106 }

107

108 Space getDragSpace() {

109 return dragSpace;

110 }

111

112 void addSpace(Space n, ArrayList listname) {

113 spaces.add(n);

114 listname.add(n);

115 spacesById.put(n.getId(), n);

116 n.setGraph(this);

117 }

118

119 Space getSpace(String id) {

120 return (Space)spacesById.get(id);

121 }

122

123 void draw() {

124

125 ////just the human program

126 for (int i=0; i<humans.size(); i++) {

127 Space a = (Space)humans.get(i);

128 a.draw();

129 }

130

131

132 for (int i=0; i<animals.size(); i++) {

133 Space a = (Space)animals.get(i);

134 a.draw();

135 }

136 // for (int i=0; i<support.size(); i++) {

137 // Space a = (Space)support.get(i);

138 // a.draw();

139 // }

140 // for (int i=0; i<visitors.size(); i++) {

141 // Space a = (Space)visitors.get(i);

142 // a.draw();

143 // }


144 //

145 // for (int i=0; i<aquarters.size(); i++) {

146 // Space a = (Space)aquarters.get(i);

147 // a.draw();

148 // }

149 for (int i = 0; i < z.anchors.size(); i++) {

150 Anchor a = (Anchor)z.anchors.get(i);

151 a.draw();

152 }

153 }

154 }

1 //The anchor class is a type of object with a fixed particle for registering and fixing qualities.

2 class Anchor {


4 Particle p;

5 String type;

6 float[] myScreenPos = { 0,0,2};

7

8 Anchor(ParticleSystem ps, float xPos, float yPos){

9 this.ps = ps;

10 this.p = ps.makeParticle(.1,width/2, height/2, 0);

11 this.p.makeFixed();

12 }

13

14 Anchor(ParticleSystem ps, float xPos, float yPos, String type){

15 this.type = type;

16 this.ps = ps;

17 this.p = ps.makeParticle(.1,xPos, yPos, 0);


19 }

20

21 void draw(){

22 if(this.p.isFree()){

23 fill(255,0,0);

24 }

25 else if(this.p.isFixed()){

26 fill(255);

27 }

28

29 strokeWeight(5);

30 stroke(0);

31 line(this.p.position.x-5, this.p.position.y, this.p.position.x+5, this.p.position.y);

32 line(this.p.position.x, this.p.position.y-5, this.p.position.x, this.p.position.y+5);

33

34 fill(0);

35 float[] myScreenPos = {

36 0,0,2 };

37


39 text(this.type, this.p.position.x+10, this.p.position.y+10);

40

41 if (this.p == fixedP){

42 stroke(228,255,31,128);

43 strokeWeight(5);

44

45 for ( int i = 0; i < ps.numberOfSprings(); ++i ){

46 Spring e = ps.getSpring( i );

47 if(e.isOn()){

48 if ( this.p == e.getOneEnd() || this.p == e.getTheOtherEnd()){

49 Particle a = e.getOneEnd();

50 Particle b = e.getTheOtherEnd();

51 stroke(0);

52 strokeWeight(1);


54 }

55 }

56 }

57 }

58 }

59 }

60

61 void initAnchors(){

62 hot = new Anchor(ps, 150, 600, “hot”);

63 cold = new Anchor(ps, 1000, 600, “cold”);

64 wet = new Anchor(ps, 1000, 800, “wet”);

65 dry = new Anchor(ps, 150, 225, “dry”);

66 landAnchor = new Anchor(ps, 525, 225, “land”);

67 waterAnchor = new Anchor(ps, 150, 800, “water”);

68 treesAnchor = new Anchor(ps, 1000, 225, “trees”);

69 mountainAnchor = new Anchor(ps, 625, 375, “mountain”);

70

71 z.anchors.add(landAnchor);

72 z.anchors.add(waterAnchor);

73 z.anchors.add(treesAnchor);

74 z.anchors.add(mountainAnchor);

75

76 north = new Anchor(ps, width/2, 3*height/7, “north”);

77 south = new Anchor(ps, width/2, 6*height/7, “south”);

78

79 west = new Anchor(ps, 2*width/7, height/2, “west”);

80 east = new Anchor(ps, 5*width/7, height/2, “east”);

81

82 supportAnchor = new Anchor(ps, 6*width/7, 5*height/7, “supportAnchor”);

83

84 nocturnal = new Anchor(ps, 400, 500, “nocturnal”);

85

86 z.anchors.add(nocturnal);

87 z.anchors.add(hot);

88 z.anchors.add(cold);

89 z.anchors.add(wet);

90 z.anchors.add(dry);

91

92 z.anchors.add(north);

93 z.anchors.add(south);

94 z.anchors.add(west);

95 z.anchors.add(east);

96 z.anchors.add(supportAnchor);

97

98 Vector3D animalsCentroid = centroid(z.animals);

99

100 centroid = new Anchor(ps, animalsCentroid.x, animalsCentroid.y, “centroid”);

101 centroid.p.makeFixed();

102 z.anchors.add(centroid);

103

104 float landYPos = lerp (dry.p.position.y, wet.p.position.y, .3);

105 float waterYPos = lerp (dry.p.position.y, wet.p.position.y, .9);

106 }

1 //The Space class parents animal and human objects. Every space has a particle within the particle

system, identification, type, size and initial positions.

2 class Space {

3 Zoo z;

4 Vector3D position;

5 Particle p;


7 Spring s;

8

9 float[] myScreenPos = { 0,0,2 };

10 String label = “”;

APPENDiCES 168

11 String type = “”;

12 String id = “”;

13

14 float diameter, radius, weight, area, elevation, depth;

15

16 color colCoding;

17 boolean visited;

18

19 int[] neighbors; // associations with the neighboring animals in the mesh.

20 int particleNum; // associate this animal with the particle system

21

22 void draw(){

23 if (diet.equals(“carnivore”)){

24 stroke(229,10,70);

25 strokeWeight(2);

26 }

27 else{

28 stroke(0);

29 strokeWeight(1);

30 }

31

32 fill(colCoding);

33 ellipse(this.p.position.x,this.p.position.y, diameter , diameter);

34

35 stroke(0);

36 strokeWeight(2);

37 point(this.p.position.x,this.p.position.y);

38


40 stroke(228,255,31,128);

41 strokeWeight(5);

42 println(label);

43 fill(0);


45 text(label, this.p.position.x+this.diameter/2+5,this.p.position.y+this.diameter/2+5);


47 stroke(255);

48 strokeWeight(2);


50 }

51

52 if (record) {

53 fill(0);



56 }

57 }

58

59 String getId() {

60 return id;

61 }

62 void setId(String s) {

63 id = s;

64 }

65 void setGraph(Zoo h) {

66 z = h;

67 }

68 }

1 //this class was started as a means to start typifying relationships - as each “Link” is created, a field

called type could be filled as per a series of conditional gates, could placed in separate lists or

whatever

2 class Link {

3 Space space, a, b;

4 Anchor c;

5 Spring s;

6

7 Link(Space a, Anchor b ){

8 s = ps.makeSpring(a.p, b.p, 1, 1, 1 );

9 }

10

11 Link(Space a, Space b){

12 s = ps.makeSpring(a.p, b.p, 1, 1, 1 );

13 this.a = a;

14 this.b = b;

15 }

16

17 Link(Space a, Anchor b, float strength, float damping, float restLength ){

18 s = ps.makeSpring(a.p, b.p, strength, damping, restLength );

19 println(strength);

20 this.a = a;

21 this.c = b;

22 }

23

24 Link(Space a, Space b, float strength, float damping, float restLength){


26 this.a = a;

27 this.b = b;

28 }

29

30 Link( Animal a, Anchor b, float strength, float damping, float restLength ){


32 this.a = a;

33 this.c = b;

34 }

35

36 Link (float x1, float y1, float x2, float y2){

37 }

38 }

1 //The Animal class has fields for many divergent criteria pertaining to each species, some which are

used to make distinctions as to how to treat each exhibit. As a child of the Space class, each

has its own particle, area, as well as other spatial information but this is augmented here with

fields to make finer distinctions between species.

2 String[] specialTypes = { “water”, “underground”, “ground”, “trees”, “air”};

3

4 ArrayList anchors, wetAnimals, dryAnimals, mixedAnimals, hotAnimals, coldAnimals,

temperateAnimals,

5 waterAnimals, earthAnimals, landAnimals, treeAnimals, skyAnimals, friends, foes,

6 undergroundAnimals, abovegroundAnimals ;

7 ArrayList anchoredLinks = new ArrayList();

8 ArrayList centroidLinks = new ArrayList();

9 ArrayList landcoverLinks = new ArrayList();

10 ArrayList landcovers = new ArrayList();

11

12 boolean dispWaterAnimals = false, dispEarthAnimals = false, dispLandAnimals =

false, dispTreeAnimals = false, dispSkyAnimals = false, dispugAnimals = true,

dispagAnimals = true;

13 float weight, area;

14 String diet, climate, common, special, temperature, humidity, active;

15 int opacity = 90;

16 color waterColor = color(0,200,255,opacity), skyColor = color(150,255,255,opacity),

earthColor = color(50,50,0,opacity), landColor = color(255,255,0,opacity), treesColor =

color(150,255,0,opacity);

17

18 HashMap specialIndices;

19

20 void setupSpecials() {

21 specialIndices = new HashMap();

22 for (int i = 0; i < specialTypes.length; i++) {


87 abovegroundAnimals.add(this);

88 Link tempLink = new Link(this, treesAnchor,10,1,1);

89 landcoverLinks.add(tempLink);

90 }

91 if (this.specialType.equals(“ground”)){

92 this.elevation = 0.0;

93 this.stratum = “ground”;

94 this.stratumID = “B”;

95 colCoding = landColor;

96 landAnimals.add(this);

97 Link tempLink = new Link(this, landAnchor,10,1,1);


99 }

100 if (this.specialType.equals(“underground”)){

101 this.elevation = -10.0;

102 this.stratum = “belowground”;

103 this.stratumID = “A”;

104 colCoding = earthColor;

105 earthAnimals.add(this);

106 undergroundAnimals.add(this);

107 Link tempLink = new Link(this, landAnchor,10,1,1);


109 }

110 if (this.specialType.equals(“water”)){

111 this.elevation = -10.0;

112 this.stratum = “belowground”;

113 this.stratumID = “A”;

114 colCoding = waterColor;

115 waterAnimals.add(this);

116 undergroundAnimals.add(this);

117 Link tempLink = new Link(this, waterAnchor,10,1,1);


119 }

120 if (this.specialType.equals(“air”)){


122 this.stratum = “aboveground”;

123 this.stratumID = “C”;

124 colCoding = skyColor;

125 skyAnimals.add(this);


127 }

128 if(this.mountain.equals(“TRUE”)){




132 colCoding = earthColor;

133 Link tempLink = new Link(this, mountainAnchor,100,1,1);


135 }

136 }

137 void setDiet(){

138 if(this.diet.equals(“carnivore”) || this.diet.equals(“saprovore”)){

139 foe = true;

140 foes.add(this);

141 }

142 else if(this.diet.equals(“omnivore”)){

143 if(this.weight < 25){

144 foe = false;

145 friends.add(this);

146 }

147 else{

148 foe = true;

149 foes.add(this);

150 }

151 }

152 else{

153 foe = false;

154 friends.add(this);

23 specialIndices.put(specialTypes[i], new Integer(i));

24 }

25 }

26

27 class Animal extends Space {

28 float trees, grass, soil, ice, water, bushes;

29 String specialType, temp, humidity, diet, active, stratum, stratumID;

30 boolean foe, nocturnalBool = false;

31 Vector3D position;

32 PVector aqPos, lcPos, treePos, waterPos, icePos;

33 float aqRadius, lcRadius, treeRadius, waterRadius, iceRadius = 0;

34 float randomAngle =0;

35 int[] neighbors;

36 float sepHeight, sepWidth;

37

38 String sepType, roof, mountain;

39

40 ArrayList neighbours;

41

42 Animal(int index, ParticleSystem ps, String label, float weight, float area, String specialType,

String temperature, String humidity, String diet, String active, float trees, float grass,

float bushes, float soil, float ice, float water, String sepType, float sepHeight, float

sepWidth, String roof, String mountain ) {

43 particleNum = index;

44

45 this.temp = temperature;

46 this.humidity = humidity;

47 this.label = label;

48 this.weight = weight;

49 this.area = area;

50 this.specialType = specialType;

51 this.diet = diet;

52 this.active = active;

53 this.radius = sqrt(area/PI)*drawingScale;

54 this.diameter = radius*2;

55 this.trees = trees;

56 this.grass = grass;

57 this.bushes = bushes;

58 this.soil = soil;

59 this.ice = ice;

60 this.water = water;

61 this.ps = ps;

62

63 this.roof = roof;

64 this.sepType = sepType;

65 this.sepHeight = sepHeight;

66 this.sepWidth = sepWidth;

67 this.mountain = mountain;

68 this.p = ps.makeParticle(10, random((width/2)-150,(width/2)+150), random((height/2)-50,

(height/2)+300), 0);

69

70 position= new Vector3D(this.p.position.x,this.p.position.y,this.p.position.z());

71 aqPos = new PVector(0,this.radius + this.aqRadius, 0 );

72

73 setStratum();

74 setDiet();

75 anchorAnimal();

76 provideAQuarters();

77 setLandCover();

78 }

79

80 void setStratum(){

81 if (this.specialType.equals(“trees”)){




85 colCoding = treesColor;

86 treeAnimals.add(this);

APPENDiCES 170

155 }

156 }

157 void anchorAnimal(){

158 Link centroidLink = new Link(this, centroid);

159 centroidLinks.add(centroidLink);

160

161 if (this.temp.equals(“hot”)){

162 Link tempLink = new Link(this, hot,10,1,1);

163 hotAnimals.add(this);

164 anchoredLinks.add(tempLink);

165 }

166 else if (this.temp.equals(“cold”)){

167 Link tempLink = new Link(this, cold,10,1,1);

168 coldAnimals.add(this);


170 }

171 else if (this.temp.equals(“temperate”)){

172 Link tempLink = new Link(this, hot,10,1,1);

173 Link tempLink2 = new Link(this, cold,10,1,1);

174 temperateAnimals.add(this);


176 anchoredLinks.add(tempLink2);

177 }

178 if (this.humidity.equals(“wet”)){

179 Link tempLink = new Link(this, wet,10,1,1);

180 wetAnimals.add(this);


182 }

183 if (this.humidity.equals(“dry”)){

184 Link tempLink = new Link(this, dry,10,1,1);

185 dryAnimals.add(this);


187 }

188 if (this.humidity.equals(“mixed”)){

189 Link tempLink = new Link(this, wet,10,1,1);

190 Link tempLink2 = new Link(this, dry,10,1,1);

191 mixedAnimals.add(this);


193 anchoredLinks.add(tempLink2);

194 }

195

196 if (active.equals(“nocturnal”)){

197 this.nocturnalBool = true;

198 ps.makeSpring(this.p, nocturnal.p,10, 1, 1 );

199 }

200 }

201

202 void provideAQuarters(){

203 float tempArea;

204 if (area/5 > 500){

205 tempArea = 500;

206 }

207 else{

208 tempArea = area/5;

209 }

210 aqRadius = sqrt(tempArea/PI)*drawingScale;

211 }

212

213 void setLandCover(){

214 treeRadius = sqrt((this.trees/100)*area/PI)*drawingScale;

215 waterRadius = sqrt((this.water/100)*area/PI)*drawingScale;

216 iceRadius = sqrt((this.ice/100)*area/PI)*drawingScale;

217 treePos = new PVector(0, this.radius-treeRadius, 0);

218 waterPos = new PVector(0, this.radius-waterRadius, 0);

219 icePos = new PVector(0, this.radius-iceRadius, 0);

220 }

221

222 void draw(){

223 if (this.foe){

224 stroke(150,0,00);


226 }

227 else if(!this.foe){

228 stroke(0);


230 }

231

232 fill(colCoding);


234 stroke(0);



237


239 stroke(228,255,31,128);


241 fill(0);




245 stroke(255);



248

249 for ( int i = 0; i < ps.numberOfSprings(); ++i ){

250 Spring e = ps.getSpring( i );

251 if(e.isOn()){

252 if ( this.p == e.getOneEnd() || this.p == e.getTheOtherEnd()){

253 Particle a = e.getOneEnd();

254 Particle b = e.getTheOtherEnd();

255 stroke(0, 90);



258 }

259 }

260 }

261 }

262

263 if (record) {

264 fill(0);



267 }

268 updateNeighbours();

269

270 noStroke();

271 fill(0,200,0);

272 ellipse(treePos.x+p.position.x, treePos.y+p.position.y, treeRadius*2, treeRadius*2);

273 fill(200,200,0);

274 ellipse(aqPos.x+p.position.x, aqPos.y+p.position.y, aqRadius*2, aqRadius*2);

275 }

276

277 void updateNeighbours(){

278 int count = 0;



281 if(a != this){

282 if(this.diet.equals(a.diet)){

283 float distance = distance(this.p.position.x, this.p.position.y, a.p.position.x,

a.p.position.y);

284 if(distance<60){

285 aqPos.add(a.p.position.x-this.p.position.x, a.p.position.y-this.p.position.y, 0);

286 }

287 }

288 aqPos.normalize();

289 aqPos.mult(this.radius+aqRadius);


354 String toString(){

355 return “Node ID: “+id+”, label: “+label+”, position: “+position;

356 }

357 }

358

359 void initAnimals(){

360 Table animalValuesTable = new Table(“animalValues-10.txt”);

361 int rowCountAVT = animalValuesTable.getRowCount();

362 Animal[] animals = new Animal[rowCountAVT-1];

363

364 wetAnimals= new ArrayList();

365 dryAnimals = new ArrayList();

366 mixedAnimals = new ArrayList();

367 hotAnimals = new ArrayList();

368 coldAnimals= new ArrayList();

369 temperateAnimals = new ArrayList();

370 waterAnimals = new ArrayList();

371 landAnimals = new ArrayList();

372 treeAnimals = new ArrayList();

373 skyAnimals = new ArrayList();

374 earthAnimals = new ArrayList();

375 undergroundAnimals = new ArrayList();

376 abovegroundAnimals = new ArrayList();

377 friends = new ArrayList();

378 foes = new ArrayList();

379

380 for (int j=1; j<rowCountAVT; j++){

381 area = animalValuesTable.getFloat(j,3);

382 diet = animalValuesTable.getString(j,16);

383 common = animalValuesTable.getString(j,0);

384 weight = animalValuesTable.getFloat(j,1);

385 special = animalValuesTable.getString(j,11);

386 temperature = animalValuesTable.getString(j,17);

387 humidity = animalValuesTable.getString(j,18);

388 active = animalValuesTable.getString(j,10);

389

390 float trees = animalValuesTable.getFloat(j,20);

391 float grass = animalValuesTable.getFloat(j,21);

392 float bushes = animalValuesTable.getFloat(j,22);

393 float soil = animalValuesTable.getFloat(j,23);

394 float ice = animalValuesTable.getFloat(j,24);

395 float water = animalValuesTable.getFloat(j,25);

396

397 String sepType = animalValuesTable.getString(j,26);

398 String roof = animalValuesTable.getString(j,29);

399 float sepWidth = animalValuesTable.getFloat(j,28);

400 float sepHeight = animalValuesTable.getFloat(j,27);

401

402 String mountain = animalValuesTable.getString(j,30);

403 animals[j-1] = new Animal(j-1, ps, common, weight, area, special, temperature, humidity,

diet, active, trees, grass, bushes, soil, ice, water, sepType, sepWidth, sepHeight, roof,

mountain);

404 animals[j-1].setId(common);

405 z.addSpace(animals[j-1], z.animals);

406 }

407 }

1 //the following iterative sequences manage and orchestrate the seprations and links between spaces on

every time sliced loop.

2 ArrayList separations = new ArrayList();

3

4 /*use this to addmore connetions, particularly to neighbours*/

5 void addMoreLinks(){



290 if(a.trees>0 && this.trees>0){


a.p.position.y);


293 treePos.add(a.p.position.x-this.p.position.x, a.p.position.y-this.p.position.y, 0);

294 }

295 treePos.normalize();

296 treePos.mult(this.radius-treeRadius);

297 }

298 if(a.water>0 && this.water>0){


a.p.position.y);


301 waterPos.add(a.p.position.x-this.p.position.x, a.p.position.y-this.p.position.y,

0);

302 }

303 waterPos.normalize();

304 waterPos.mult(this.radius-waterRadius);

305 }

306 if(a.ice>0 && this.ice>0){


a.p.position.y);


309 icePos.add(a.p.position.x-this.p.position.x, a.p.position.y-this.p.position.y, 0);

310 }

311 icePos.normalize();

312 icePos.mult(this.radius-iceRadius);

313 }

314 }

315 }

316 }

317

318 String getLabel() {

319 return this.label;

320 }

321

322 boolean containsPoint(float x, float y) {

323 float dx = p.position.x-x;

324 float dy = p.position.y-y;

325 return (abs(dx) < diameter/2 && abs(dy)<diameter/2);

326 }

327

328 Animal (Vector3D v) {

329 position = v;

330 }

331

332 void setPosition(Vector3D v) {

333 position = v;

334 }

335

336 float getZ() {

337 return position.getZ();

338 }

339

340 String getId() {

341 return id;

342 }

343 void setId(String s) {

344 id = s;

345 }

346 void setGraph(Zoo h) {

347 z = h;

348 }

349

350 void addToCurrentPosition(float x, float y, float z){

351 p.position().set(x,y,z);

352 }

353

APPENDiCES 172

8

9 for (int j = 0; j < z.visitors.size(); j++) {

10 Human b = (Human)z.visitors.get(j);

11

12 Link tempLinkA = new Link(a, b, 1, 1.0, 1);

13 tempLinkA.s.turnOff();

14 z.animalHumanLinks.add(tempLinkA);

15 }

16

17 for (int j = i+1; j < z.animals.size(); j++) {

18 Animal b = (Animal)z.animals.get(j);

19

20 Link sepLink = new Link(a,b);

21 sepLink.s.turnOff();

22 separations.add(sepLink);

23

24 //Animals with the same dietary needs should be associated with one another

25 if(a.diet.equals(b.diet)){

26 float d = distance(a.p.position.x,a.p.position.y,b.p.position.x,b.p.position.y);

27 Link tempLink = new Link(a, b, 50.0, 1.0, d);

28 tempLink.s.turnOff();

29 z.dietLinks.add(tempLink);

30 }

31 //Animals with the same habitat needs should be associated with one another

32 if(a.stratum.equals(b.stratum)){

33 float d = distance(a.p.position.x,a.p.position.y,b.p.position.x,b.p.position.y);

34 Link tempLink = new Link(a, b, 50.0, 1.0, d);

35 tempLink.s.turnOff();

36 z.stratumLinks.add(tempLink);

37 }

38 }

39 }

40 }

41

42 void adjustLinks(){

43 for (int i = 0; i < z.animalHumanLinks.size(); i++) {

44 Link l = (Link)z.animalHumanLinks.get(i);

45 float minDist = l.a.radius+l.b.radius;

46

47 if(l.s.currentLength()>gateFactor*minDist && l.s.isOn()){

48 l.s.turnOff();

49 }

50 if(l.s.currentLength()<gateFactor*minDist && l.s.isOff()){

51 l.s.turnOn();

52 l.s.setRestLength(ahlLength);

53 l.s.setDamping(10);

54 l.s.setStrength(ahlStrength);

55 }

56 }

57

58 for (int i = 0; i < centroidLinks.size(); i++) {

59 Link l = (Link)centroidLinks.get(i);

60 l.s.setRestLength(clLength);


62 l.s.setStrength(clStrength);

63 }

64

65 for (int i = 0; i < separations.size(); i++) {

66 Link l = (Link)separations.get(i);


68 l.s.setRestLength(minDist*allSep);


70 l.s.turnOff();

71 }


73 l.s.turnOn();


75 }

76 }

77

78 for (int i = 0; i < z.dietLinks.size(); i++) {

79 Link l = (Link)z.dietLinks.get(i);

80


82 l.s.setRestLength(minDist*dietSep);


84 l.s.turnOff();

85 }


87 l.s.turnOn();


89 }

90 }

91

92 for (int i = 0; i < z.stratumLinks.size(); i++) {

93 Link l = (Link)z.stratumLinks.get(i);


95 l.s.setRestLength(minDist*strataSep);


97 l.s.turnOff();

98 }


100 l.s.turnOn();


102 }

103 }

104 }

105 void addSpacersToNodes(){



108

109 for (int j = i+1; j < z.animals.size(); j++) {

110 Animal b = (Animal)z.animals.get(j);

111 float attDist = a.radius+b.radius;

112

113 //make room for carnivores

114 if (a != b && a.diet.equals(“carnivore”) || b.diet.equals(“carnivore”) ){

115 Attraction tempAtt = ps.makeAttraction( a.p, b.p, attStrength*5, attDist*attDist/5 );

116 z.dietAtt.add(tempAtt);

117 }

118 }

119 }

120 }

121

122 void adjustSpacers(){

123 for (int i = 0; i < z.dietAtt.size(); i++) {

124 Attraction a = (Attraction)z.dietAtt.get(i);

125 a.setMinimumDistance(attDist/5);

126 a.setStrength(attStrength*5);

127

128 Particle one = a.getOneEnd();

129 Particle two = a.getTheOtherEnd();

130

131 float d = distance(one.position.x,one.position.y,two.position.x,two.position.y);

132 if(a.isOn()){

133 if (d>100){

134 a.turnOff();

135 }

136 }

137 if(a.isOff()){

138 if (d<100){

139 a.turnOn();

140 }

141 }

142 }

143 }


FIG 4.3 LANDCOvER SCHEMA

FIG 4.4 TERRiTORiAL PARTiTiONiNG

CELLULAR ORGANiSATiON 175

CELLULAR ORGANiSATiON

REFER TO "iNTERNAL DiFFERENTiATiON" iNCHAPTER 2: CONSTRUCTiNG THE ZOO MACHiNE

30 float radius = 350;

31 int divisions = 16;

32

33 boolean displayNodes = false;

34 boolean toggle = false;

35 boolean record = false;

36 boolean xPhysics = true;

37 boolean optimize = false;

38 boolean adjust = false;

39

40 char x;

41 float neighbourhood = 100;

42 int particlesNo, attractionsNo;

43 int bFactor=2;

44

45 PeasyCam camera;

46

47 void setup() {

48 size(1026,830, OPENGL);

49 camera = new PeasyCam(this, 500, 500, 0, 450);

50 ps = new ParticleSystem( 0, 0.1 );

51 initAnimals();

52 ps.addCustomForce(new Turbulence());

53 }

54

55 void draw(){

56 if (record) {

57 beginRecord(PDF, “frame-####.pdf”);

58 }

59 if(xPhysics){

60 ps.tick(0.1);

61 }

62 background(0);

63 for(int i =0; i<animals.length; i++){

64 animals[i].draw();

65 }

66 for(int i =0; i<humans.length; i++){

67 humans[i].draw();

68 }

69

70 for (int i = 0; i < hairs.length; i++) {

71 hairs[i].draw();

72 }

73

74 if (record) {

75 exportGeometry();

76 }

77

78 if(optimize){

79 if(xPhysics){

80 xPhysics = false;

81 }

82 optimizePaths();

83 }

84 if(adjust){

85 adjustForces();

86 }

87 }

88

89 //initialization of Animal objects and "hairs" connecting them

90 void initAnimals(){

91 Table hairPlayInit = new Table(“hairPlayInit.txt”);

92 int rowCount = hairPlayInit.getRowCount();

93 animals = new Animal[rowCount];

94

95 Table hairPlayInitHumans = new Table(“humanPositions.txt”);

96 int rowCountHumans = hairPlayInitHumans.getRowCount();

97 humans = new Human[rowCountHumans-8];

1 //A Processing simulation to orchestrate the visitor's pathways between exhibits. It proceeds from the

FDL simulation, taking each space's vectors and species specific properties and translating them

into a system composed of fixed particles connected by springs of free moving particles which

are allowed to gather together

2 import peasy.org.apache.commons.math.*;

3 import peasy.*;

4 import peasy.org.apache.commons.math.geometry.*;

5 import processing.opengl.*;

6 import upsdnPhysics.*;

7 import processing.pdf.*;

8


10 ArrayList free = new ArrayList();

11 ArrayList attractions = new ArrayList();

12 Particle fixedP;

13

14 Animal[] animals;

15 Hair[] hairs;

16 Human[] humans;

17

18 //attraction properties

19 float aDist = 1000; //8

20 float aStrength = 5000; //50

21

22 //spring properties

23 float sLength = 1;

24 float sStrength = 1;

25 float sDampening = 1;

26

27 float gateDist = 70;

28 float crossSize = 3;

29

WET THREADS SiMULATiON 177

WET THREADS SiMULATiON

REFER TO "THE BUNDLiNG SiMULATiON" iN CHAPTER 2: CONSTRUCTiNG THE ZOO MACHiNE

98

99 Particle[] fixed = new Particle[rowCount+rowCountHumans-8];

100 hairs= new Hair[0];

101

102 for (int j=0; j<rowCount; j++){

103 float x = hairPlayInit.getFloat(j,0);

104 float y = hairPlayInit.getFloat(j,1);

105 float z = hairPlayInit.getFloat(j,2);

106 String label = hairPlayInit.getString(j,3);

107 String specialType = hairPlayInit.getString(j,4);

108 String stratum = hairPlayInit.getString(j,5);

109 String temp = hairPlayInit.getString(j,6);

110 String humidity = hairPlayInit.getString(j,7);

111 String diet = hairPlayInit.getString(j,8);

112 animals[j] = new Animal(x,y,z,label, specialType, stratum, diet, temp, humidity);

113 fixed[j] = animals[j].p;

114 }

115

116 for (int j=0; j<rowCountHumans-8; j++){

117 float x = hairPlayInitHumans.getFloat(j+8,1);

118 float y = hairPlayInitHumans.getFloat(j+8,2);

119 float z = hairPlayInitHumans.getFloat(j+8,3);

120 String label = hairPlayInitHumans.getString(j+8,0);

121 humans[j] = new Human(x/5,y/5,z/5,label);

122 fixed[j+rowCount] = humans[j].p;

123 }

124

125 for (int j = 0; j < humans.length; j++) {

126 for (int i = 0; i < animals.length; i++) {

127 PVector ptDifference = PVector.sub(humans[j].position, animals[i].position);

128 animals[i].setProximity(ptDifference.mag());

129 }

130

131 Arrays.sort(animals, new AComparator02());

132 for(int i =0; i<9; i++){

133 Hair tempStrand = new Hair(animals[i].p, humans[j].p, x);

134 hairs = (Hair[])append(hairs, tempStrand);

135 animals[i].countConnections();

136 humans[j].countConnections();

137 }

138 }

139

140 for (int i = 0; i < animals.length; i++) {

141 Animal focusAnimal = animals[i];

142

143 for (int k = 0; k < animals.length; k++) {

144 PVector ptDifference = PVector.sub(focusAnimal.position, animals[k].position);

145 if (i!=k){

146 animals[k].setProximity(100000);

147 }

148 else{

149 animals[k].setProximity(ptDifference.mag());

150 }

151 }

152

153 Arrays.sort(animals, new AComparator02());

154

155 for (int j = 0; j < animals.length-1; j++) {

156 if(!neighbours(focusAnimal, animals[j])){

157 if(!focusAnimal.label.equals(animals[j].label)){

158 if(focusAnimal.connected<4){

159 float distance = PVector.dist(focusAnimal.position, animals[j].position);

160 if(distance<65 && focusAnimal.stratum.equals(animals[j].stratum)){

161 x= ‘b’;

162 Hair tempStrand = new Hair(focusAnimal.p, animals[j].p, x);


164 focusAnimal.countConnections();

165 focusAnimal.neighbours = (Animal[])append(focusAnimal.neighbours, animals[j]);

166 animals[j].neighbours = (Animal[])append(animals[j].neighbours, focusAnimal);

167 }

168

169 else if( focusAnimal.connected<5 && animals[j].connected<5 && distance<100 && foc

usAnimal.temp.equals(animals[j].temp) && focusAnimal.humidity.equals(animals[j].

humidity) && !neighbours(focusAnimal, animals[j])){

170 x= ‘c’;






176 }

177

178 else if( focusAnimal.connected<5 && animals[j].connected<5 && distance<100 &&

animals[i].diet.equals(animals[j].diet) && !neighbours(focusAnimal, animals[j])){

179 x = ‘a’;






185 }

186 }

187 }

188 }

189 }

190 }

191

192 particlesNo = ps.numberOfParticles();

193

194 Particle a, b;

195 for(int i = 0; i<particlesNo; i++){

196 a = (Particle)ps.getParticle(i);

197 PVector vA = new PVector(a.position.x, a.position.y, a.position.z);

198

199 for(int j = i+1; j<particlesNo; j++){

200 b = (Particle)ps.getParticle(j);

201 PVector vB = new PVector(b.position.x,b.position.y, a.position.z);

202

203 float distance = PVector.dist(vA, vB);

204 Attraction tempAtt = ps.makeAttraction(a, b, distance/3, distance/12);

205 attractions.add(tempAtt);

206 if(distance>25){

207 tempAtt.turnOff();

208 }

209 }

210 }

211 attractionsNo = ps.numberOfAttractions();

212 }

213

214 //sequence for adjusting the forces between free moving particles

215 void adjustForces(){

216 Attraction a;

217 Particle b, c;

218 for(int i = 0; i<attractionsNo; i++){

219

220 a = (Attraction)ps.getAttraction(i);

221 b = a.getOneEnd();

222 c = a.getTheOtherEnd();

223

224 PVector vA = new PVector(b.position.x, b.position.y, b.position.z);

225 PVector vB = new PVector(c.position.x, c.position.y, c.position.z);

226

227 float distance = PVector.dist(vA, vB);

228 if (distance<25 && a.isOff()){

229 a.turnOn();

230 }

APPENDiCES 178

231 if (distance<25 && a.isOn()){

232 a.setStrength(distance/2);

233 a.setMinimumDistance(distance/2);

234 }

235 else if(distance>25 && a.isOn()){

236 a.turnOff();

237 }

238 }

239 }

240

241 class AComparator implements Comparator {

242 int compare(Object o1, Object o2) {

243 float d1 = ((PVector) o1).mag();

244 float d2 = ((PVector) o2).mag();

245 return (d1<d2) ? -1 : (d1==d2) ? 0 : 1;

246 }

247 }

248 class AComparator02 implements Comparator {

249 int compare(Object o1, Object o2) {

250 float d1 = ((Animal)o1).proximity;

251 float d2 = ((Animal)o2).proximity;

252 return (d1<d2) ? -1 : (d1==d2) ? 0 : 1;

253 }

254 }

255

256 void optimizePaths(){

257 for(int i = 0; i<hairs.length; i++){

258 for(int j=1; j<hairs[i].cPts.length-1; j++){

259

260 PVector ptA = new PVector(hairs[i].cPts[j].position.x, hairs[i].cPts[j].position.y, hairs[i].

cPts[j].position.z);

261 PVector[] closestPts = new PVector[0];

262

263 for(int k = 0; k<hairs.length; k++){

264 PVector[] kPts = new PVector[0];

265 PVector kClosestPt = new PVector();

266

267 if(k!=i){

268 for(int l=1; l<hairs[k].cPts.length-1; l++){

269 PVector ptB = new PVector(hairs[k].cPts[l].position.x, hairs[k].cPts[l].position.y,

hairs[k].cPts[l].position.z);

270 PVector ptDifference = PVector.sub(ptA, ptB);

271 kPts = (PVector[]) append(kPts, ptDifference);

272 }

273 }

274

275 if(kPts.length>0){

276 Arrays.sort(kPts, new AComparator());

277 kClosestPt = kPts[0];

278 closestPts = (PVector [])append(closestPts, kClosestPt);

279 }

280 }

281

282 Arrays.sort(closestPts, new AComparator());

283 float distance = ptA.dist(closestPts[0]);

284


286 PVector vec = closestPts[0];

287 vec.mult(-0.1);

288 PVector newPos = PVector.add(ptA, vec);

289 line(ptA.x, ptA.y, ptA.z, newPos.x, newPos.y, newPos.z);

290 hairs[i].cPts[j].position().set(newPos.x, newPos.y, newPos.z);

291 hairs[i].cPts[j].makeFixed();

292 }

293 }

294 }

295 }

296

297 void exportGeometry(){

298 saveFrame(“frame-####.png”);

299 endRecord();

300 record = false;

301

302 PrintWriter output1 = createWriter(“beziers.txt”);

303 PrintWriter output2 = createWriter(“lines.txt”);

304

305 for (int i = 0; i < hairs.length; i++) {

306 if(hairs[i].cPts.length>2){

307 output2.print(hairs[i].cPts[0].position.x +”,”+ hairs[i].cPts[0].position.y +”,”+ hairs[i].

cPts[0].position.z +”,” +(hairs[i].cPts[1].position.x + hairs[i].cPts[0].position.x)/2 +”,”+

(hairs[i].cPts[0].position.y + hairs[i].cPts[1].position.y) /2 +”,”+ (hairs[i].cPts[0].position.z

+ hairs[i].cPts[1].position.z) /2);

308

309 int numCPts = hairs[i].cPts.length;

310

311 for (int j = 1; j < hairs[i].cPts.length-2; j++) {

312 output1.print((hairs[i].cPts[j-1].position.x+hairs[i].cPts[j].position.x)/2 +”,”+ (hairs[i].

cPts[j-1].position.y+hairs[i].cPts[j].position.y) /2 +”,”+ (hairs[i].cPts[j-1].position.

z+hairs[i].cPts[j].position.z) /2 +”,”+hairs[i].cPts[j].position.x +”,”+hairs[i].cPts[j].

position.y +”,” +hairs[i].cPts[j].position.z +”,” +hairs[i].cPts[j].position.x +”,”+ hairs[i].

cPts[j].position.y +”,” +hairs[i].cPts[j].position.z +”,”+ (hairs[i].cPts[j+1].position.x +

hairs[i].cPts[j].position.x)/2 +”,”+ (hairs[i].cPts[j+1].position.y + hairs[i].cPts[j].position.y)

/ 2 +”,”+ (hairs[i].cPts[j+1].position.z + hairs[i].cPts[j].position.z) / 2+”,”);

313 }

314 output1.println((hairs[i].cPts[numCPts-3].position.x+hairs[i].cPts[numCPts-2].position.x)/2

+”,”+ (hairs[i].cPts[numCPts-3].position.y+hairs[i].cPts[numCPts-2].position.y) /2

+”,”+ (hairs[i].cPts[numCPts-3].position.z+hairs[i].cPts[numCPts-2].position.z) /2

+”,”+hairs[i].cPts[numCPts-2].position.x +”,”+hairs[i].cPts[numCPts-2].position.y +”,”

+hairs[i].cPts[numCPts-2].position.z +”,” +hairs[i].cPts[numCPts-2].position.x +”,”+

hairs[i].cPts[numCPts-2].position.y +”,” +hairs[i].cPts[numCPts-2].position.z +”,”+

(hairs[i].cPts[numCPts-2].position.x + hairs[i].cPts[numCPts-1].position.x)/2 +”,”+

(hairs[i].cPts[numCPts-2].position.y + hairs[i].cPts[numCPts-1].position.y) / 2 +”,”+

(hairs[i].cPts[numCPts-2].position.z + hairs[i].cPts[numCPts-1].position.z) / 2);

315 output2.println(“,”+ hairs[i].cPts[hairs[i].cPts.length-1].position.x +”,”+ hairs[i].cPts[hairs[i].

cPts.length-1].position.y +”,”+ hairs[i].cPts[hairs[i].cPts.length-1].position.z +”,”+

(hairs[i].cPts[hairs[i].cPts.length-2].position.x + hairs[i].cPts[hairs[i].cPts.length-1].

position.x )/2 +”,”+ (hairs[i].cPts[hairs[i].cPts.length-2].position.y + hairs[i].cPts[hairs[i].

cPts.length-1].position.y )/2 +”,”+ (hairs[i].cPts[hairs[i].cPts.length-2].position.z +

hairs[i].cPts[hairs[i].cPts.length-1].position.z )/2);

316 }

317 }

318 output1.flush();

319 output1.close();

320 output2.flush();

321 output2.close();

322 }

323 void keyPressed(){

324 if(key==’d’) displayNodes=!displayNodes;

325 if(key==’t’) toggle=!toggle;

326 if (key == ‘r’) record =! record;

327 if (key == ‘x’) xPhysics =! xPhysics;

328 if (key == ‘o’) optimize =! optimize;

329 if (key == ‘a’) adjust =! adjust;

330 if (key == ‘[‘) bFactor++;

331 println(bFactor);

332 if (key == ‘]’) bFactor—;

333 println(bFactor);

334 }

335

336 public class Turbulence implements Force {

337 int ForceX, ForceY;

338 float nScale = 1;

339 float timeScale = 1;

340 float amp = 1;

341 float sample = 0.47;

342


343 public void apply(){

344

345 for(int i = 0; i<particlesNo; i++){

346 Particle p = (Particle)ps.getParticle(i);

347

348 ForceX += ((noise(p.position.x/nScale, p.position.y/nScale, float(frameCount)/timeScale))-

sample)*amp;

349 ForceY += ((noise(p.position.x/nScale, p.position.y/nScale + 1000, float(frameCount)/

timeScale))-sample)*amp;

350

351 p.force().add( ForceX, ForceY, 0 );

352 }

353 }

354 boolean isOn(){

355 return true;

356 }

357 void turnOff(){

358 }

359 boolean isOff(){

360 return false;

361 }

362 }

363

364 boolean neighbours(Animal a, Animal b){

365 for(int i = 0; i<a.neighbours.length; i++){

366 if(a.neighbours[i] == b ){

367 println(a.label +” and “+ b.label +” are neighbours yes”);

368 return true;

369 }

370 }

371 println(“No, “+ a.label +” and “+ b.label +” are not neighbours yet”);

372 return false;

373 }

1 class Hair{

2 Particle pA, pB;

3 PVector a, b;

4 Particle[] cPts;

5 Spring[] segments;

6 char type;

7 float z;

8 int numP;

9

10 //very important to simulation...

11 int numPfactor = 14; //was 12 before

12

13 Hair(Particle pA, Particle pB, char type){

14 this.type = type;

15 this.z = pA.position.z;

16

17 PVector a = new PVector(pA.position.x, pA.position.y, pA.position.z);

18 PVector b = new PVector(pB.position.x, pB.position.y, pB.position.z);

19

20 float tempDistance = a.dist(b);

21 numP = ceil(tempDistance/numPfactor)+1;

22 cPts = new Particle[numP];

23 cPts[0] = pA;

24 cPts[cPts.length-1] = pB;

25

26 for(float i = 1; i<cPts.length-1; i++){

27 cPts[int(i)] = ps.makeParticle(1, (1-i/cPts.length)*a.x+b.x*(i/cPts.length), (1-i/cPts.length)*a.

y+b.y*(i/cPts.length), (1-i/cPts.length)*a.z + b.z*(i/cPts.length));

28 free.add(cPts[int(i)]);

29 }

30 segments = new Spring[cPts.length-1];

31 for(int i = 0; i<segments.length; i++){

32 PVector tempA = new PVector(cPts[i].position.x, cPts[i].position.y, cPts[i].position.z);

33 PVector tempB = new PVector(cPts[i+1].position.x, cPts[i+1].position.y, cPts[i+1].position.z);

34 float distance = PVector.dist(tempA, tempB);

35 segments[i] = ps.makeSpring(cPts[i], cPts[i+1], sStrength, sDampening, distance*sLength);

36 }

37 }

38

39 void draw(){

40 switch(type){

41 case ‘a’:

42 stroke(255, 0, 0);

43 break;

44 case ‘b’:

45 stroke(0, 255, 0);

46 //keeps hair on one plane

47 for (int i = 0; i<cPts.length;i++){

48 float x = cPts[i].position().x();

49 float y = cPts[i].position().y();

50 cPts[i].position().set( x, y, this.z );

51 }

52 break;

53 case ‘c’:

54 stroke(0, 0, 255);

55 break;

56 }

57


59

60 if(!toggle){

61 if(cPts.length>2){

62 line(cPts[0].position.x, cPts[0].position.y, cPts[0].position.z, (cPts[1].position.x+cPts[0].

position.x)/2, (cPts[0].position.y+cPts[1].position.y) /2, (cPts[0].position.z + cPts[1].

position.z) /2);

63 for(int i = 1; i<cPts.length-1; i++){

64 PVector A = new PVector((cPts[i-1].position.x+cPts[i].position.x)/2, (cPts[i-1].position.

y+cPts[i].position.y) /2, (cPts[i-1].position.z + cPts[i].position.z) /2);

65 PVector B = new PVector(cPts[i].position.x, cPts[i].position.y, cPts[i].position.z);

66 PVector C = new PVector((cPts[i+1].position.x+cPts[i].position.x)/2, (cPts[i+1].position.

y+cPts[i].position.y) /2, (cPts[i+1].position.z + cPts[i].position.z) /2);

67

68 PVector handle1 = new PVector(B.x-A.x, B.y-A.y, B.z-A.z);

69 float hLength1 = handle1.mag();

70 handle1.normalize();

71 handle1.mult(hLength1/bFactor); //25 is nice

72

73 PVector handle2 = new PVector(B.x-C.x, B.y-C.y, B.z-C.z);

74 float hLength2 = handle2.mag();

75 handle2.normalize();

76 handle2.mult(hLength2/bFactor);

77

78 bezier(A.x, A.y, A.z , A.x+handle1.x, A.y+ handle1.y, A.z+ handle1.z , C.x + handle2.x ,

C.y + handle2.y, C.z + handle2.z , C.x, C.y, C.z);

79 }

80 line(cPts[cPts.length-1].position.x, cPts[cPts.length-1].position.y, cPts[cPts.length-1].

position.z, (cPts[cPts.length-2].position.x+cPts[cPts.length-1].position.x )/2, (cPts[cPts.

length-2].position.y+cPts[cPts.length-1].position.y )/2, (cPts[cPts.length-2].position.

z+cPts[cPts.length-1].position.z )/2);

81 }

82 }

83

84 if(toggle){

85 beginShape();

86 curveVertex(cPts[0].position.x, cPts[0].position.y, cPts[0].position.z);

87 curveVertex(cPts[0].position.x, cPts[0].position.y, cPts[0].position.z);

88 for (int i = 1; i<cPts.length-1;i++){

89 curveVertex(cPts[i].position.x, cPts[i].position.y, cPts[i].position.z);

90 }

APPENDiCES 180

91 curveVertex(cPts[cPts.length-1].position.x, cPts[cPts.length-1].position.y, cPts[cPts.

length-1].position.z);

92 curveVertex(cPts[cPts.length-1].position.x, cPts[cPts.length-1].position.y, cPts[cPts.

length-1].position.z);

93 endShape();

94 }

95

96 if(displayNodes){

97 fill(255);


99 for(int i = 1; i<cPts.length; i++){

100 line(cPts[i].position.x-crossSize, cPts[i].position.y, cPts[i].position.z, cPts[i].position.

x+crossSize, cPts[i].position.y, cPts[i].position.z);

101 line(cPts[i].position.x, cPts[i].position.y-crossSize, cPts[i].position.z, cPts[i].position.x,

cPts[i].position.y+crossSize, cPts[i].position.z);

102 }

103 noFill();

104 }

105 }

106 }

1 class Animal{

2 float x, y, z;

3 String label, specialType, stratum ,diet, temp, humidity;

4 PVector position;

5 Particle p;

6 int connected=0;

7 float proximity;

8 Animal[] neighbours = new Animal[0];

9

10 Animal(float x, float y, float z, String label, String specialType, String stratum, String diet,

String temp, String humidity){


12 this.specialType = specialType;

13 this.diet = diet;

14 this.temp = temp;

15 this.humidity = humidity;

16 this.stratum = stratum;

17 this.p = ps.makeParticle(1, x, y, z);


19 position= new PVector( x, y , z);

20 }

21 void draw(){

22 if(connected>0){

23 stroke(255,99,0); }

24 else{

25 stroke(255,99); }


27 fill(255,50);

28 pushMatrix();

29 translate(0,0,position.z);

30 ellipse(position.x, position.y, 3, 3);

31 popMatrix();

32 noFill();

33 }

34 void countConnections(){

35 connected++;

36 }

37 void setProximity(float distance){

38 proximity = distance;

39 }

40 }

1 class Human{

2 float x, y, z;

3 String label;

4 PVector position;

5 Particle p;

6 int connected=0;

7

8 Human(float x, float y, float z, String label){


10 this.p = ps.makeParticle(1, x, y, z);


12 position= new PVector( x, y , z);

13 }

14

15 void draw(){

16 stroke(255,99);


18 fill(255);

19 pushMatrix();

20 translate(0,0,position.z);

21 ellipse(position.x, position.y, 5, 5);

22 popMatrix();

23 noFill();

24 }

25

26 void countConnections(){

27 connected++;

28 }

29 }


FIG 4.5 WET THREADS MANiPULATiON

APPENDiCES 182

WET THREAD PROCESSiNG

REFER TO "viSiTOR PATHWAYS"iN


1 Private Sub RunScript(ByVal crv As List(Of OnCurve), ByVal inPts As List(Of On3dPoint), ByRef

A As Object, ByRef B As Object)

2 Dim d1 As Double = Double.MaxValue

3 Dim min1 As Int32 = -1

4 Dim closest As New list(Of On3dPoint)

5 Dim closestVals As New List(Of Double)

6 For j As int32 = 0 To crv.Count - 1

7 For i As Int32 = 1 To inPts.Count() - 1

8 Dim t As New Double

9 crv(j).GetClosestPoint(inPts(i), t)

10 Dim cPt As New On3dPoint

11 cPt = crv(j).PointAt(t)

12 Dim d As Double = inPts(i).DistanceTo(cPt)

13 If (d < d1) Then

14 d1 = d

15 min1 = i

16 End If

17 Next

18 closest.add(inPts(min1))

19 Dim u As New Double

20 crv(j).GetClosestPoint(inPts(min1), u)

21 closestVals.Add(u)

22 Next

23

24 A = closest

25 B = closestVals

26

27 End Sub

1 Private Sub RunScript(ByVal path As String, ByRef A As Object, ByRef At As Object, ByRef B As

Object, ByRef Bt As Object)

2 If (Not IO.File.Exists(path)) Then Return

3

4 Dim lines As String() = IO.File.ReadAllLines(path)

5

6 If (lines Is Nothing) Then Return

7 Dim beziersA, beziersAt, beziersB, beziersBt As New List(Of On3dPoint)

8

9 For Each line As String In lines

10 Dim parts As String() = line.Split(",".ToCharArray())

11 Dim x1 As Double = Convert.ToDouble(parts(0))

12 Dim y1 As Double = Convert.ToDouble(parts(1))

13 Dim z1 As Double = Convert.ToDouble(parts(2))

14 beziersA.Add(New On3dPoint(5 * x1, -5 * y1, 5 * z1))




18 beziersAt.Add(New On3dPoint(5 * x2, -5 * y2, 5 * z2))




22 beziersB.Add(New On3dPoint(5 * x3, -5 * y3, 5 * z3))




26 beziersBt.Add(New On3dPoint(5 * x4, -5 * y4, 5 * z4))

27 Next

28 A = beziersA

29 At = beziersAt

30 B = beziersB

31 Bt = beziersBt

32 End Sub

33

1 Private Sub RunScript(ByVal curves As List(Of OnCurve), ByVal threshold As Double, ByVal

crvLength As List(Of Double), ByVal centres As List(Of On3dPoint), ByRef A As Object,

ByRef B As Object, ByRef C As Object)

2 Dim i, j As New Integer

3 Dim tVals As New List(Of Double)

4 Dim bools As New list(Of Boolean)

5 Dim lengths As New List(Of Double)

6 Dim closestPtsList As New List(Of On3dPointArray)

7 Dim closestPtParam As New Double

8 Dim ptArray, closestPts, applicableCentres, testCentres As New On3dPointArray

9 Dim crvPt As New On3dPoint

10 For i = 0 To curves.Count - 1 Step 1

11 If crvLength(i) > threshold Then

12 Dim p0 As New On3dPoint(curves(i).PointAtStart())

13 Dim p1 As New On3dPoint(curves(i).PointAtEnd())

14 For j = 0 To centres.Count - 1

15 If centres(j) <> p0 And centres(j) <> p1 Then

16 ptArray.Append(centres(j))

17 End If

18 Next

19 If crvLength(i) > threshold Then

20 For j = 0 To ptArray.count - 1

21 curves(i).GetClosestPoint(ptArray(j), closestPtParam)

22 'I don't want swells at the beginning or end of the curve

23 If closestPtParam <> 0 And closestPtParam <> 1

24 crvPt = curves(i).pointat(closestPtParam)

25 closestPts.Append(crvPt)

26 applicableCentres.append(ptArray(j))

27 'Else If closestPtParam <> 0 Or closestPtParam <> 1 Then

28 ' ptArray.Remove(j)

29 End If

30 Next

31 End If

32 closestPtsList.Add(closestPts)

33 End If

34 Next

35

36 A = tVals

37 B = applicableCentres

38 C = closestPts

39 End Sub

WET THREAD PROCESSiNG 183

1 //An attractor pattern which allocates values to each point in an array - in this case a grid - based on their porximity to a list of attractors. In

this version, only if the distance between attractor and point is under a given value, here simply called "distance". A gaussian or normal

function is implemented here to get an easing in and out of each attractor's zone of influence.

2 Sub RunScript(ByVal ptGrid As List(Of On3dPoint), ByVal attractors As List(Of On3dPoint), ByVal gateDistance As List(Of Double),

ByVal strength As List(Of Double), ByVal max As Double)

3

4 Dim intensityValues As New List(Of Double)

5 Dim i As Integer

6 Dim j As Integer

7

8 For i = 0 To ptGrid.Count - 1 Step 1

9

10 Dim intVal As New Double

11 intVal = 0.0

12

13 For j = 0 To attractors.Count - 1 Step 1

14 Dim distance As Double

15 distance = attractors(j).DistanceTo(ptGrid(i))

16

17 If distance < gateDistance(j) Then

18 intVal = intVal + strength(j) * exp(-((distance) ^ 2 / (2 * gateDistance(j) ^ 2))) / (gateDistance(j) * sqrt(2 * PI))

19 End If

20

21 Next

22

23 intensityValues.Add(intVal)

24 Next

25

26 VALUES = intensityValues

27

28 End Sub

ATTRACTOR PATTERN 185

ATTRACTOR PATTERN

REFER TO "ENvELOPE" iNCHAPTER 2: CONSTRUCTiNG THE ZOO MACHiNE

FIG 4.6 ENvELOPE PANEL ARTiCULATiON

FIG 4.7 ENvELOPE GEOMETRY

APPENDiCES 186

TRiANGULATiON SCHEMA

REFER TO "ENvELOPE"iN


TRiANGULATiON SCHEMA 187

6

7 Dim polyline As New OnPolyline()

8 polyline.Append(midpts(i))

9 polyline.Append(vertices(i))

10

11 If i = 2 Then

12 polyline.Append(midpts(0))

13 Else

14 polyline.Append(midpts(i + 1))

15 End If

16

17 new_polylines.Add(polyline)

18

19 Next

20

21 A = new_polylines

22

23 End Sub

1 Private Sub RunScript(ByVal vertices As List(Of On3dPoint), ByVal midpts As List(Of

On3dPoint), ByRef A As Object)

2

3 Dim new_polylines As New List(Of OnPolyline)

4

5 For i As int32 = 0 To 2

6


8 polyline.Append(midpts(i))

9 polyline.Append(vertices(i))

10

11 If i = 2 Then


13 Else

14 polyline.Append(midpts(i + 1))

15 End If

16


18

19 Next

20


22

23 End Sub



2



5





10


12


14

15 End Sub

1 Private Sub RunScript(ByVal triangles As OnCurve, ByRef A As Object)

2



5 Dim i,j, k As Int32

6

7 For i = 0 To triangles.Count - 1

8

9 Dim triangle As OnCurve

10 triangle = triangles(i)

11 Dim knots(), mids() As Double

12 triangle.GetSpanVector(knots)

13

14 For j = 0 To 2

15

16 Dim vertices, midPoints As List(Of On3dPoint)

17 Dim tempPt As On3dPoint

18 tempPt = triangle.PointAt(knots(j))

19 vertices.Add(tempPt)

20

21 If j = 0 Then

22 mids(0) = knots(0) - knots(2)

23 Else

24 mids(j) = knots(j) - knots(j - 1)

25 End If

26

27 For k = 0 To 2

28 tempPt = triangle.PointAt(mids(k))

29 midPoints.Add(tempPt)

30 Next

31

32 Next

33

34 Next

35

36 A = midPoints

37

38 End Sub



2


4

5 For i As int32 = 0 To 2

FIG 4.8 STRUCTURAL GRiD

APPENDiCES 188

BiBLiOGRAPHY

TRiANGULATiON SCHEMA 191

Allan, John, and Berthold Lubetkin. Berthold Lubetkin : Architecture and the Tradition of Progress.

London: RIBA Publications, 1992. Print.

Allen, Stan. Points + Lines : Diagrams and Projects for the City. 1st ed. New York: Princeton

Architectural Press, 1999. Print.

Altinisik, Melike, et al. Morphê : MRGD. Wien ; New York ; London: Springer, 2008. Print.

Aranda, Benjamin, and Chris Lasch. Tooling. 27 Vol. New York: Princeton Architectural Press,

2006. Print.

Balmond, Cecil, Jannuzzi Smith, and Christian Brensing. Informal. Munich ; New York: Prestel,

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