development of urban sustainability index using 3-d spatial metrics

8/13/2019 Development of Urban Sustainability Index Using 3-D Spatial Metrics

1/8

Development of Urban Sustainability Index Using 3-D Spatial Metrics

Sara Shirowzhan1and Samsung Lim

2

1PhD Candidate, School of Surveying and Spatial Information Systems, UNSW,

Sydney, NSW 2052, Australia, PH +61 47909 5314; email:

[email protected]

2Associated Professor, School of Surveying and Spatial Information Systems,

UNSW, Sydney, NSW 2052, Australia; email: [email protected]

ABSTRACT

Advanced spatial technologies such as photogrammetry and lidar haveimproved the quality of spatial information and enable data processing for more

accurate estimation of urban environment parameters. This study aims to develop

a quantification method for urban sustainability indexes by using spatial metrics

such as compactness, complexity and density. Although building height

information is an important element of urban morphology, it has been neglected in

previous studies. Hence, height information obtained by lidar is incorporated into

the spatial metrics in this study.

The spatial metrics are applied to four study cases. We have examined the

metrics and concluded that the developed metrics can quantify the sustainable

urban form concept more effectively. The main finding of this study confirms that

the 3-dimensional spatial metrics differentiate the complexity of urban areassignificantly. Another significance of this study is the high capability of spatial

metrics for the quantification of sustainable urban forms in terms of complexity,

compactness and density. The developed indexes can be used for the

determination of the spatio-temporal changes of sustainable urban forms or the

comparison of the cities in terms of a sustainable urban form using remotely

sensed data.

Keywords:Urban form; Sustainability; Spatial metrics; Built environment

1. Introduction

The increasing growth of human settlement has resulted in the complexityof an urban fabric. Spatial metrics have been used for the illustration, management

and quantification of the characteristics of landscape and land use. For example,

Frohn and Hao (2006) reported the use of landscape pattern metrics for the

quantification of landscape structure/form on a map or remotely sensed imagery.

Spatial metrics have the potential of an improved quantification of urban form

characteristics. The use of spatial metrics has been the increasing trend of built

238ICSDEC 2012 ASCE 2013

ICSDEC 2012


2/8

environment studies (Murayama and Thapa, (2011).However, the current spatial

metrics can not characterize the morphology of an urban form in a three

dimensional manner.

Recently, remote sensing technologies (e.g. photogrammetry and lidar) provide

dense elevation information of urban forms. That is, high-resolution stereographic

aerial photos and lidar data can be used to estimate building elevation. These high-

resolution data acquisition technologies provide the potential of enhanced spatial

metrics to the third dimension. Lidar data and high-resolution images can be

utilized to define and examine novel 3-D urban form metrics. However, existing

urban studies that examine the similarities and differences in terms of a

sustainable development show that there are no universally accepted variables,

which are backed by theory as well as rigorous data collection and analysis (Parris

and Kates, 2003).

The current technologies of satellite and aerial data acquisition with more recent

data processing methods have been contributing to the most adequate

measurement procedures. However, the studies using lidar data in urban areas

further focus on building density (Yu et al.,2010), building height (Cheng et al.,

2011) and pattern of height (Zhang et al., 2011, Alobeid A, 2009 ). That is, onlyfew studies have been conducted using lidar for the improvement of spatial

metrics.

A recently developed theory of sustainable urban forms that suggests four types of

urban forms (Jabareen, 2006) has an arguable issue of descriptive methods for the

determination of the urban sustainability. The theory classifies the sustainability

concept to low, medium and high. This type of classification can be confusing

especially when fuzziness of the classes is evident. Thus the major challenge in

the sustainability concept has been identified as the transformation of the verbal

indicators of the sustainability into quantifiable measures (Zhang and Guidon,

2006).

Compactness and density are critical typologies for the sustainable urban forms(Jabareen, 2006). Our study aims to overcome the problem with the quantification

of the physical aspect of sustainable urban forms, including compactness,

complexity and density that constitute the sustainability indexes using spatial

metrics. The main argument is that the current qualitative methods for the

definition of urban forms are very limited in order to assist the planners for the

recognition of the degree of the urban sustainability. In contrast, the quantitative

methods can be more efficiently used for the recognition of the pattern of

sustainable urban forms.

The improved spatial metrics will be examined on the study area of University of

New South Wales (Figures 1 and 2). For this objective, a robust quantitative

method will be presented by improving the current spatial metrics in a 3-dimensional manner. In doing so, the theory of sustainable urban forms and the

investigation of compactness or sprawl will improve the empirical study. This

means that it will be possible to assign a degree of the sustainability to a given city

and also the comparison between cities in terms of the sustainability will be

possible.


ICSDEC 2012


3/8

Figure 1. Lidar of UNSW Campus Figure 2. Aerial image from UNSW

2. Methodology

Firstly, a model of Sustainable Urban Form Indexes (SUFI) has been presented in

the following section in order to achieve the quantification of the sustainable

urban forms. The model will clarify the direction of the study. Secondly, spatial

metrics of compactness, complexity and density have been developed. The unit of

the measurements is building as the unit of building block has previously been

used by Yoshida and Omae (2005) for the urban morphology analysis.

The lidar data has been used for the extraction of footprints and elevation

information. The extracted footprints have been categorized into four study cases,where the shape of first case is simple (Case 1) and the second is complex (Case

2). The other two cases are clusters of buildings with respective boundaries as

their land lots. Case 3 includes buildings and boundaries smaller than Case 4.

The concept of sustainable urban forms has been examined over Cases 1 to 4

(Figures 3-6). After the analysis of the results, the elevation information is used to

develop the metrics to 3-D sustainable urban form indexes.

3. Modelling Measurement of Urban Form Sustainability

The relationship between urban morphology and sustainability of urban forms has

been confirmed by several studies (Smith and Levermore, 2008); Banister et al.,

Figure 3; Case 1. Footprint of the simplebuilding

Figure 4; Case 2. Footprint of the complexbuilding

Figure 5; Case 3. Footprints of the smaller urban area Figure 6; Case 4. Footprints of the larger urban area


ICSDEC 2012
http://ascelibrary.org/action/showImage?doi=10.1061/9780784412688.028&iName=master.img-005.jpg&w=190&h=112http://ascelibrary.org/action/showImage?doi=10.1061/9780784412688.028&iName=master.img-004.jpg&w=107&h=82http://ascelibrary.org/action/showImage?doi=10.1061/9780784412688.028&iName=master.img-003.jpg&w=161&h=82http://ascelibrary.org/action/showImage?doi=10.1061/9780784412688.028&iName=master.img-002.jpg&w=157&h=82http://ascelibrary.org/action/showImage?doi=10.1061/9780784412688.028&iName=master.img-001.jpg&w=154&h=90http://ascelibrary.org/action/showImage?doi=10.1061/9780784412688.028&iName=master.img-000.jpg&w=212&h=90


4/8

1997). The sustainable urban form concepts characterizing physical urban form

are density, complexity and compactness (Figure 7). Spatial metrics could

measure the physical aspect of sustainable urban forms. Figure 7 illustrates the

model of sustainable urban form Indexes (SUFI).

Figure 7. A model of Sustainable Urban Form Index (SUFI).

4. Development of Spatial Metrics

4.1Spatial Metrics and Sustainable Urban Form Quantification

Density: The first indicator for the definition of urban forms is density, which hasbeen defined as the number of buildings per square kilometer. A similar metric for

the patch density has been defined as the number of patches per 100 hectares

(Herold M, 2002). The area unit in this study is changed to a square meter due to

the size of the study area being much smaller than that of the data in the research

carried out by previous researchers e.g. Herold (2002).

Complexity: The shape of a building influences the level of energy required for

the provision of the thermal comfort. Therefore a more complex shape of

buildings has been found to result in higher energy consumption than that of a

simpler building (Watson, (1992).

The complexity of land cover and land use has been characterized by the spatial

metrics of Area Weighted Mean Shape Index (AWMSI) and Fractal Dimension(AWMPFD) (Frohn and Hao, 2006, Herold et al., 2005). Herold et al. (2005) used

IKONOS data to characterise and describe the land cover heterogeneity of urban

areas with spatial metrics and classify the buildings based on the complexity and

compactness of the small versus large buildings. This study does not use the

spatial metrics for the determination of the heterogenity of the urban areas but

uses the metrics for the determination of the complexity of individual buildings.

Once the capability of these metrics for the demonstration of the level of

complexity for the individual buildings is confirmed, the complexity metric will

be applied to urban areas.

As for the complexity metric, a correction to the equations of AWMSI and

AWMPFD has been carried out as the building unit is changed. From this point,the replaced equation with the new building unit will be called as Area Weighted

Mean Building Shape Index (AWMBS; Equation 1) and Area Weighted Mean

Building Fractal Dimension (AWMBFD; Equation 3; Table 1).

] (1)

FDB= (2)


ICSDEC 2012


5/8

(3)

whereArepresents the Area of the building lot,pis the perimeter of the footprint

and aillustrates the area of the footprint.

Compactness Index: The third index of the SUFI model is compactness.

Compactness has been defined against sprawl in the sustainable urban formliterature. The equation of this indicator has been presented as follows (Huang et

al.,2007):

(4)

where in Equations 1 to 4, ai and pi are the area and perimeter of building i,

respectively. Pi is also the perimeter of a circle with the area of ai and N is the

total number of buildings (Huang et al.,2007).

4.2Definition of 3-D Spatial Metrics

In this section, three main indexes of the SUFI model are calculated by

incorporating the elevation information using the 3D perimeter of the buildings.

Complexity: the 3D perimeter of the buildings is calculated automatically by

using a commercial lidar data processing software. Equation 5 is used for the

determination of the 3-D index of complexity:

] (5)

Compactness: Likewise the calculation of the 3-D perimeter of the buildings, the

3-D index of compactness is calculated as follows:

(6)

whereEp represents the 3-D perimeter of the building i.

5 Analysis

5.1. Before Addition of Elevation Information

This study has examined two types of building shapes; simple (Figure 3) and

complex (Figure 4), as to evaluate the two equations: AWMBS and AWMBFD.

For the complexity index of AWMBS, the results were as same as expected i.e.

high values for more complex buildings. However, the result for AWMBFD

(Equation 3) is not adequate as it shows a higher value for the simpler footprint.Therefore, Equation 3 was ignored for the rest of this study.

Both AWMBS and FDB have been calculated for two buildings. The resulting

figures calculated from AWMBS and FDB are demonstrated in Table 1. It is clear

that these two complexity measurements are higher for Case 2 and it verifies that

Case 2 is more complex (Table 1).


ICSDEC 2012


6/8

After the verification of Equation 1 for the individual buildings, this equation was

examined for two different sizes of urban areas; Case 3 with 219245 square

meters and Case 4 with 292004 square meters (Figures 5 and 6).

One of the issues raised during the data processing which can be seen in Figure 8

is that, with the current complexity measures, the complexity of the urban area of

Case 3 will be demonstrated lower than Cases 1 and 2. The same problemoccurred for Case 4 with a similar value (Table 1; Figure 8). To overcome this

problem, 3-D indexes are proposed in order to differentiate the complexity of

indivitual and grouped buildings.

Table 1. Results of Sustainable Urban Form Indexes

Buildingfootprint ID

FDB Density CompactnessComplexity

AWMBS

Case 1 (simplebuilding)

1.4018 - ----------- 0.6709

Case 2(complexbuilding)

1.5197 - ---------- 0.8611

Case 3 (18buildings)

-------- 82 0.0072 0.2272

Case 4 ( 41buildings)

------- 62 0.0040 0.7733

5.2. After Addition of Elevation Information: 3-D indexes

The results for the complexity index differentiate the complexity of

individual buildings largely from the groups of buildings in Cases 3 and 4 (Table

2; Figure 7). It probably occurs due to the incorporation of elevation to Equation 5

(Figure 8).

Table 2. 3-D SUF results

Data samplecount

St.dev ofelevation

Mean ofelevation

3Dperimeter

3DCompactness

3DComplexity(AWMBS)

Case1 Elevation:14.22 336.16 ------- 0.94

Case 2 Elevation:26.51 1407.06 ------- 1.58

Case 3 12.45 12.22 5000.41 0.0076 4.43

Case 4 14.63 19392.50 0.0050 16.18


ICSDEC 2012


7/8

Figure 7. Complexity index versus 3-D complexity index. Figure 8. Incorporating elevation information using 3-D

perimeter.

The compactness index was only applied to Cases 3 and 4. The two indexes of

compactness and 3-D compactness have the same behavior before and after

addition of elevation (Figure 9).

Figure 9. Compactness index versus 3-D compactness index

6. Concluding Remarks

This study investigated spatial metrics to quantify the physical aspect of the

sustainable urban forms. The study found that it is possible to improve the spatial

metrics and quantify sustainable urban forms to a third dimension. The main

finding of the study is that 3-D indexes of the proposed sustainable urban form

model including elevation information can explain the degree of the sustainability

more clearly.

References

ALOBEIDA,J.K.,HEIPKEC2009Buildingheightestimationinurbanareasfromveryhigh

resolutionsatellitestereoimages.ISPRSJournalofPhotogrammetry&Remote

Sensing,XXXVIII.

BANISTER,D.,WATSON,S.&WOOD,C.1997.Sustainablecities:Transport,energy,and

urbanform.EnvironmentandPlanningB:PlanningandDesign,24,125143.

CHENG,F.,WANG,C.,WANG,J.,TANG,F.&XI,X.2011.Trendanalysisofbuildingheight

andtotalfloorspaceinBeijing,ChinausingICESat/GLASdata.International

JournalofRemoteSensing,32,88238835.


ICSDEC 2012
http://ascelibrary.org/action/showImage?doi=10.1061/9780784412688.028&iName=master.img-054.jpg&w=165&h=108http://ascelibrary.org/action/showImage?doi=10.1061/9780784412688.028&iName=master.img-053.jpg&w=202&h=111http://ascelibrary.org/action/showImage?doi=10.1061/9780784412688.028&iName=master.img-051.jpg&w=217&h=128


8/8

FROHN,R.C.&HAO,Y.2006.Landscapemetricperformanceinanalyzingtwodecades

ofdeforestationintheAmazonBasinofRondonia,Brazil.RemoteSensingof

Environment,100,237251.

HEROLD,M.,COUCLELIS,H.&CLARKE,K.C.2005.Theroleofspatialmetricsinthe

analysisandmodelingofurbanlandusechange.Computers,Environmentand

UrbanSystems,29,369399.

HEROLDM,

S.

J.,

CLARKE

C.K.

2002.

The

use

of

remote

sensing

and

landscape

metrics

to

describe

structuresandchangesinurbanlanduses.EnvironmentandPlanning,34,14431458.

HUANG,J.,LU,X.X.&SELLERS,J.M.2007.Aglobalcomparativeanalysisofurbanform:

Applyingspatialmetricsandremotesensing.LandscapeandUrbanPlanning,82,

184197.

JABAREEN,Y.R.2006.SustainableUrbanForms.JournalofPlanningEducationand

Research,26,3852.

MURAYAMAY,T.R.B.2011.Spatialanalysisandmodellingingeographic

transformationprocess,LondonNewyork,Springer.

PARRIS,T.M.&KATES,R.W.2003.CHARACTERIZINGANDMEASURINGSUSTAINABLE

DEVELOPMENT.AnnualReviewofEnvironmentandResources,28,559586.

SMITH,

C.

&

LEVERMORE,

G.

2008.

Designing

urban

spaces

and

buildings

to

improve

sustainabilityandqualityoflifeinawarmerworld.EnergyPolicy,36,45584562.

WATSON,D.&LABS,K.1992.Climaticdesign:energyefficientbuildingprinciplesand

practices,NewYork,NewYork:McGrawHill

YOSHIDA,H.&OMAE,M.2005.Anapproachforanalysisofurbanmorphology:methods

toderivemorphologicalpropertiesofcityblocksbyusinganurbanlandscape

modelandtheirinterpretations.Computers,EnvironmentandUrbanSystems,

29,223247.

YU,B.,LIU,H.,WU,J.,HU,Y.&ZHANG,L.2010.Automatedderivationofurbanbuilding

densityinformationusingairborneLiDARdataandobjectbasedmethod.

LandscapeandUrbanPlanning,98,210219.

ZHANG,P.,HU,Y.&HE,H.Year.ThespatialpatternofbuildingheightinTiexiDistrict.In:

ElectricTechnologyandCivilEngineering(ICETCE),2011International

Conferenceon,2224April20112011.17741777.

ZHANG,Y.&GUINDON,B.2006.Usingsatelliteremotesensingtosurveytransport

relatedurbansustainability:Part1:Methodologiesforindicatorquantification.

InternationalJournalofAppliedEarthObservationandGeoinformation,8,149

164.


development of urban sustainability index using 3-d spatial metrics

Documents