environmentalfootprintdesigntool -...

Environmental Footprint Design Tool

Exchanging GIS and CADData in Real Time

Earl Mark1, Zita Ultmann21University of Virginia, School of Architecture 2Budapest University of Technologyand Economics, Faculty of Civil Engineering1http://faculty.virginia.edu/mark2http://www.agt.bme.hu/staff_h/[email protected] [email protected]

The pairing of CAD and GIS data creates an opportunity to connect anarchitectural design process more immediately with its environmental constraints.Yet the GIS data may be too overwhelmingly complex to be fully used in CADwithout computer-assisted methods of highlighting relevant information. Thispaper reports on the implementation of an integrated environment forthree-dimensional design geometrical modeling and obtaining environmentalimpact feedback. The project focused on enhancements to the data exchange andon the development of a related set of tools. While the technologies of CAD andGIS may rely on separate representational models,in combination they canprovide a more complete view of the built and natural environment. The challengein integration is that of bridging analytical methods and database formats used inthe two technologies. Our approach is rooted in part in constraint based designmethods well established in CAD (e.g., Sketchpad, Generative Components,CATIA). Within such CAD systems geometrical transformations may beintentionally constrained to help enforce some previously made design decisions.Although this current implementation modestly relates to geometrical constraints,the use of probabilistic risk values is more central to its methodology.

Keywords: Real-time feedback, Environmental Footprint Analysis, PythonMacro Programming Language

COMPLEXITY OF THE DESIGN TOOLCoupling CAD and GIS within a constraint based an-alytical framework holds the potential to more di-rectly incorporate environmental analysis into an ar-chitect's or landscape architect's design process. Alodging/lab design project for a national park serves

as the basis of a case study. The project had alreadybeen through a constraint based design approachprior to using GIS. The current project adapts GISdata derived from landscape features. Several topo-graphic site surveys and thematic maps are availablein GIS due to the wealth of interest in the prominent

Design Tools - Evaluation - Volume 1 - eCAADe 33 | 217

national park.Note that the layer, geometry and attribute fea-

tures are handled differently in CAD than they are inGIS. An especially important distinction between thetwo systems is that the building description in CADis based primarily upon 3D geometry rather than onthe 2D polygon geometry that GIS software typicallyuses (see comparison in figure 1).

Given the kind of data exchange under consid-eration, a few simplifications were possible. Whenimporting data from GIS to CAD, polygons wereprojected onto the 2D ground plane and from theground plane in turn could be projected onto the 3Dterrain model. Conversely, when moving 3D build-ing data from CAD to GIS, its envelope and boundarywere projected onto the 2D ground plane. The dataflow is summarized in figure 2.

The project was implemented with Pythonscripts written for both Quantum GIS (abbreviatedas QGIS), an open source GIS system developed un-der the OSGeo hub, and Rhino, a proprietary nurbsmodeling CAD technology developed byMcNeel As-sociates. Alternatives, such as ArcGis and Sketchupthat are already paired in practice, could have beenchosen for this project. However, QGIS was se-lected due to it being an open source platform andRhino offered some Python programming languagelibraries of relevance to the project. Pilgrim (2004;[1]), Rhino.Python Programmer's Reference [2], Wes-tra (2013)

DATA SOURCESOne of the objectives of the footprint analysis is toprovide accurate and up to date input data. As thedata acquisition is a rather time consuming process,we were interested in a location where the data is al-ready given or can be efficiently collected. Satisfyingthis criterion, the campus of Schoodic Institute is lo-cated on Big Moose Island in Acadia National Park,Maine, USA. The campus is rendered with a GIS veg-etation height coverage map in figure 3. One of theauthors has used this area for student and researchbased design projects for a number of years.

Figure 1Catchment areaanalysis (3D CADmodel on left, 2DGIS analysis withvegetation heightrisk legend on right)Figure 2Flowchart of theanalysis

Figure 3GIS Thematic Mapderived fromcycling databetween Rhino andQGIS

The location has been extensively surveyed, andthe project had access to both primary and sec-ondary collected data. The main source was the In-tegrated Resource Management Application (abbre-viated as IRMA), which is a complex database cen-

218 | eCAADe 33 - Design Tools - Evaluation - Volume 1

ter maintained by several national parks. Consider-ing the fact that the size of our chosen building isaround 10m*10m and the resolution of IRMA-data is30m*30m, we regarded the database as minimallyaccurate enough for our investigation. The WebSoil-Survey produced by the National Cooperative SoilSurvey was used to create our soil database.

Other data centers, like eBird, iNaturalist, Na-tional Phenology Network, Earthwatch and Bird At-las were investigated as well, but published data isstill forthcoming and not yet sufficiently available insite specific detail for our project. Field Report onClimate Change in Acadia National Park, Day 4 [3] Anumber of GIS maps were completed through pass-ing data between CAD and GIS to better construct avisual overview of the site context.

DATAMANAGEMENTThe collected information was already in GIS format,either in vector or raster form. Our task was to filterthe information relevant to design. GIS data has a dis-tinct structure (.shp, .shx, .dbf, .prj), much of whichcannot be directly imported into Rhino. Althoughthere are some plug-ins for handling the data im-port, for instance Meerkat GIS, these are mainly de-veloped under a plugin called Grasshopper whichdid not seem fit our needs due in part to the perfor-mance level necessary to process a large amount ofdata. When importing the data from GIS into CAD,we assigned specific attributes to polygons andorga-nized them into an attribute based layering systemasshown in figure 4.

To import the data in a way that would be help-ful to analyzing the footprint of our building, we cus-tomized QGIS and we also created a new toolbox ofimport and analysis tools in Rhino (see figure 4 left-hand side for tool menu). The first step was to cus-tomize QGIS to convert GIS data into a simple filestructure. QGIS software provides several exchangeformats. The twomost widely used are the .dxf (DataExchange Format) and .csv (Comma Separated Val-ues).

Figure 4Rhino Toolkit fullyshown on left andanchored near viewwindow on rightused to “ImportQGIS File Polygons”

On the one hand, the .dxf format provides a smoothimport of geometry into Rhino. However, passingattributes associated with the data through dxf toRhino seemed more cumbersome. On the otherhand, the .csv format is extensible and provided astraightforward way to pair up geometry and at-tributes. In addition, the .csv file format seems betteroptimized to meet the needs of the project withoutballooning in size. This is evident in file size compar-isons where the same data stored in GIS file formatsis 4-5 times larger than in the .csv format.

Our original GIS file sources cover a large areaand range of data types for the national park. How-ever, for the footprint analysis we implemented amore focused file format and converted the differenttypes of data to it. In the first stage of this conver-sionwe projected all data into the same system (UTMNorth 19) and then converted the data from raster tovector form. We thenorganizedvectors into attributecategories. Finally, we exported the geometry un-der the attribute categories as Well-Known Text fromQGIS. We also used one of several alternative ways tovectorize and unify our data (more detail is beyondthe scope of this paper). Since Rhino cannot directlyimport the special .csv files we created, we wrote forour Python toolbox a Python script .csv file importerthat was used to produce figure 4 above.

Separate .csv files were developed for each GISthematic overlay such as wetland, tree canopy, floodzone, etc., but all follow the same structure. Thestructure includes geometry (E and N, horizontal co-ordinates), ID, Attribute values, Risk values and ashort description:


1. Geometry: The exported .csv file format con-tains point coordinates only. This leaves openthe possibility of interpreting the points withdifferent geometrical entities, such as poly-gons, spline or composite arc curves.

2. ID: For any given QGIS generated polygon theID was created as its unique identifier so asto also provide information about the datasource. For instance "acad_canopy_3" de-scribes the third polygon of the canopy coverfrom the database center of Acadia (ACADia)National Park.

3. Attribute: For each polygon an attribute col-umn refers to the value of the attribute it-self derived from the previously mentioneddatabase center.

4. Description: a description column containsa short description about the attribute datatype.

5. Risk: Risk value is key to the constraint analysisand is described below.

Figure 5GIS polygonsimported into CADand cumulative riskcalculation fordepicted buildingshown in white.

We found that risk values serve as a way to neutral-ize different data types. For example, instead of onekind of data for tree height polygons (e.g., height ofvegetation is metric) and another kind of data fortree porosity polygons (e.g., canopy cover is in per-centage), we set into place 12 numerical risk values.Eleven of these numbers represent risk values be-tween -5 and +5 in one unit increments, where -5is the worst case of environental harm and +5 is thebest possible condition regarding favorable environ-

mental impact, and 0 is irrelevant or neutral In addi-tion, a so-called "knockout" polygon has a risk valueat -50. Knockout is a term borrowed from risk man-agement. The knockout polygons signify where therisk is so destructive that any type of built construc-tionwithin the area it represents would be unaccept-able.

Within the CAD system, in parallel to how thedata is structured inside GIS, polygons are the basisfor storing information. The polygons are organizedunder amaster layer named "QGIS". A sub-layer is cre-ated for each attribute type (.e.g. wetland, canopy,slope, etc..) as shown in figure 5. Risk values arestored as a polygon attribute property. For ease of vi-sual identity, individual polygons are assigned a colorranging in values from blue to green in parallel totheir risk values of -5 to +5. Note that the color blackis assigned to the knockout polygons with the riskvalue of -50. Queries of the system, as show in figures5 and7, report back normalized values in the rangeof0 to 1 where 0 represents harmful impact, 0.5 is neu-tral, and 1 positive impact on the environment.

RISK MANAGEMENT, GEOMETRY ANDPOLYGONOVERLAYSRisk valuation of each of the polygons sets into placethe basis of our approach. The risk values used weredetermined by educated guesses and upon a reviewof background literature. Nguyen (2011), WesternElectricity Coordinating Council (2014) Real data forincidence of risk would obviously have helped tovalidate the basis of the methodology. However, itwasn't available and therefore remains a challengefor developing the project further.

Creating a common vectorized data format fromdifferent types of datasets required trying a numberof different approaches in QGIS. Also, whereas QGISsoftware can handle both multipart and overlayingpolygons directly, Rhino can display but doesn't han-dle the analysis of such data apart from the tools wecreated.

In the first step in QGIS, for a given attribute type(e.g., wetland) the original raster data was projected


onto an equal distanced grid as shown in figure 6.The size of the grid and the cells of the raster weremade the same and not overlapping. This helped toavoid losing information during later stage interpola-tion techniques. Also in QGIS we consolidated adja-cent grids that shared the sameprimary attribute andthen single parted the polygons according to risk val-ues.

Before being imported into CAD, the vector datawas checked for overlapping geometry within an at-tribute type. Any overlapping geometry was pro-jected onto a cellular grid scaled to the resolution ofthe source GIS data. We then repeated the techniqueof single parting the polygons mentioned above.This removed overlapping geometry and ensuredgreater coherence of the data.

Figure 6Stages of polygoncreation in QGIS forexport into Rhino

Figure 7Move BuildingFootprint toolresults in riskvaluation goingfrom favorable tounfavorableenvironmentalimpact DESIGN FEEDBACK AND CONSTRAINTS

Design feedback analysis in the prototype systemis based on georeferencing the geometry of a pro-posedbuildingwith the importedGISdata consistingof polygons and their risk values. The system drawsupon the imported GIS data when the user makeschanges that impact a building's footprint, such de-picted in figure 7. If the designer adjusts the build-ing envelope, in turn its footprint is adjusted, andin turn the environmental risk calculation associatedwith the related underlying layers of risk is automati-cally redone. Both the area of impact and the level of

risk are taken into account. Thus, a building site thathasonly 5%of its footprint overlapwith adetrimentalenvironmental risk polygon results in a different cu-mulative risk calculation than one that has 95% of itsfootprint overlap with such a polygon. If a knockoutpolygon is encountered, it results in a more extremenegative risk value.

The organization of the data in CAD is parallel toits preparation in GIS:

1. Layer Scheme: The data is imported throughthe .csv format described above into CAD.Polygons sharing the same type attribute(e.g., wetland) are automatically assigned toa distinct sub-layer of a QGIS Layer (e.g., the"wetland" sub-layer).

2. Risk: As noted previously risk values are as-signed as attributes to the polygons, Theircolor from blue to green is determined bytheir risk values. Also, the color of each GISsub-layer is determined by the average riskvalue of its polygons

3. Building Footprint: The 3D building footprintis projected to an analysis layer so as to be co-planar with the GIS polygons.

4. Negative and Positive Risk: Purely negativerisk areas or purely positive risk areas can beautomatically highlighted for visual referenceand automatically Boolean union if adjacentas illustrated in figure 8 below.

5. Cumulative Risk: Cumulative risk associatedwith any building footprint is quantitativelyreported and normalized to values rangingfrom 0 to 1.

Our experience indicated that the transfer of polygondata from GIS to CAD and Boolean analysis of minusand plus areas of risk raised some contextual issues.For example, a polygonA that reflects a positive envi-ronmental impactmayoverlapwith several polygonsB, C andD that represent negative environmental im-pact. To handle this condition required checkswithinthe QGIS export process to ensure that redundant in-formation was reduced and that the cumulative risk


assessmentwasn't arbitrarily biased towards thepos-itive or negative risk values.

Figure 8Cumulative positive(green) andnegative (blue) riskareas for vegetationheight –shiftedright for thisillustration

Additional features remain to be implemented. Onegoal is to add greater sophistication in terms of howthe building footprint is derived from the full 3Dbuilding envelope. That is, we have so far based ouranalysis on the vertical projection of the 3D build-ing forms onto a 2D ground construction plane. Yet,there are a number of special cases that are notaccounted for. For example, a building's shadowsproject at varied angles during the day and year. In amore complete implementation, the programwouldexport this information from CAD into GIS, and inturn, export a polygon from GIS back to CAD with arisk assessment Another challenge is to account forexceptional conditions that may arise from the juxta-position of two different type GIS polygons that miti-gate one another. For example, with respect to max-imizing solar power a 1) terrain aspect polygon de-scribes the orientation of land slope to compass di-rection and a 2) terrain slope polygon describes theangle of land surface relative to a level ground. A ter-rain aspect facing south would useful for optimizingsolar gain for our project site. Yet, a terrain slope of<= 2 % negates any advantage or disadvantage withrespect to terrain aspect and solar gain. The uniquerelationship of slope to aspect shows the need for afew special case rules for determining environmentalrisk.

RESULTS AND CAVEATSIntegrated GIS and CAD may make site informationmore apparent to a person working with 12 concur-

rent overlays of environmental information as shownin figure 5. The valuation of risk may also begin toquantify positive or negative impacts thatmaynot beself-evident. Still, not all design decisions can be eas-ily related to such a GIS framework. For example, abuilding's energy performance may depend on fea-tures regarding the design of its facades, insulation,and glazing. These features aren't necessarily charac-terized within GIS layers, and so strictly enforced GISbased geometrical constraints may not be appropri-ate.

Finally, real time performance was an aspira-tional goal of the project. In the current prototypereal-time performance was not fully achieved. Ex-ecution time ran from roughly 0.25 to 1.5 secondsfor some tools depending upon the number of GISpolygons. This was true for the tools "Calculate Risk","Project Volumes", "Project Footprint", and "MoveBuilding Footprint". The "Calculate Positive" and "Cal-culate Negative" tools shown in figure 7 could takeup to several minutes or more since they generate acumulative Boolean display of all the positive or neg-ative polygons. Boolean operations are not fully im-plemented in the underlying software libraries, andthiswas only partially addressed in the Python scriptswritten for the project.

FUTURE DIRECTIONS AND UNFINISHEDOBJECTIVESFour enhancements would help to address limita-tions of the system implemented:

1. Monte Carlo: The Monte Carlo probabilitymethod could better represent the risk for anygiven polygon. For example, currently eachpolygon has a single risk rating such as thenumber r = -4. However, the risk of a severeweather event impactinga "sealevelrise" poly-gon may be 20 % for a catastrophic event (r =-50), 60% for an event of significant impact(r= -3), and 40% for a moderate event (r = -1).This type risk informationwasunavailable andwould take time to determine.


2. Field Data Measurements: The GIS / CADproject has been developed in parallel to aseparate project of simulation structures withmicroclimate and building performance sen-sors. The structures collect data that wouldadd to theGIS information. However, this datacollection is incomplete.

3. Feedback Loops between CAD and GIS: Thedominant direction of data flow in the cur-rent prototype has been GIS to CAD. A morecomplete analysis cycle based upon export-ing proposed building data from CAD to GISmayhelp better identify site design issues andenvironmental impact conflicts.

4. Compound polygon analysis: As already de-scribed, two different type polygons may ahave distinct inter-relationship that may notbe fully accounted for in terms of their in-dependent risk values. Thus, more work isneeded to take into account these specialcase circumstances (e.g., slope and aspect de-scribed earlier).

5. Only risk assessment feedback andnot full ge-ometrical constraintswere implemented suchas would restrict a user from moving a build-ing's geometry to an areawhere very negativerisk values are encountered. Direct geomet-rical constraints would be possible along theability to turn them on or off.

SUMMARYIn this prototype we demonstrate where risk valuedpolygons exchangedbetweenGIS andCADmayhelptoprovidedynamic feedback in response to an evolv-ing 3D design model. Higher resolution data andmore complete certainty of risk values would en-hance the value of this feedback. More advancedrisk assessmentmethods (e.g., Monte Carlo)may givemoremeaning to the risk calculations. In design edu-cation, the inclusion such a system of analysis wouldinvolve seeing architecture or landscape architecturethrough a geoscientist data lens. Thismethod of see-ing for a School of Architecture would need to be in-

tegral with teaching design and the exploration ofform, materials, function and site.

ACKNOWLEDGEMENTSThe following individuals and organizations were in-strumental to this project:

Sijia Yang, Graduate Research Assistant, Schoolof Architecture, University of Virginia, , contributedsubstantially to CAD and GIS analysis maps.

Karen Anderson, GIS Specialist, Acadia NationalPark, , generously provided data used for the re-search.

Mike Soukup, PhD, Director of Science, SchoodicInstitute, Acadia National Park

Mark Berry, President and CEO, Schoodic Insti-tute, Acadia National Park

Quesada Foundation, Portland, Maine

REFERENCESWestern Electricity Coordinating Council, WECC 2014,

Environmental Data Products, User's Manual (Decem-ber 2014), Self-published, Salt Lake City

Nguyen, BK 2011, 'Comparative Review of Five Sustain-able Rating Systems', Procedia Engineering, 21, pp.376-386

Pilgrim, M 2004, Dive Into Python, ApressWestra, E 2013, Python Geospatial Development, PACKT

Publishing, Birmingham-Mumbai[1] http://www.diveintopython.net[2] http://www.rhino3d.com/5/ironpython/index.html[3] http://blog.pivotal.io/data-science-pivotal/cas

e-studies-2/from-sea-to-trees-pivotal-data-science-looks-at-climate-change-in-acadia-national-park-day-4-final-day-field-report


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