AbstractIn 2007 a private firm in Seattle, Washington began to measure and publish walkability maps of cities, neighborhoods, and streets via “walkscore.com”. Although “walkscore” have been criticized for its parameters, no alternative have been provided so far. More over, such maps have been published for developed countries. No such initiative has been undertaken in developing countries. This study aims to introduce and discuss an alternative model to measure walkability on street level via Geographic Information Systems (GIS). About 6500 street segments in nine districts of Izmır, Turkey have been digitized as well as the land use. A walkability score (based on betweenness / centrality scores derived from street network and accessibility scores derived from landuse) for each street segment was measured via GIS and its extension Spatial Design Network Analysis (SDNA)

Cubukcu, Ebru

Hepguzel, Burcin

Onder, Zeynep

Tumer, Burak

Elsevier - Publisher Connector 

 Procedia - Social and Behavioral Sciences  168 ( 2015 )  229 – 237 
Available online at www.sciencedirect.com
ScienceDirect
1877-0428 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license 
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Peer-review under responsibility of Centre for Environment-Behaviour Studies (cE-Bs), Faculty of Architecture, Planning & Surveying, 
Universiti Teknologi MARA, Malaysia.
doi: 10.1016/j.sbspro.2014.10.228 
AicE-Bs2014Berlin 
(formerly AicE-Bs2014Magdeburg) 
Asia Pacific International Conference on Environment-Behaviour Studies  
Sirius Business Park Berlin-yard field, Berlin, 24-26 February 2014 
“Public Participation: Shaping a sustainable future” 
 
Active Living For Sustainable Future:  A model to measure 
“walk scores” via Geographic Information Systems 
Ebru Cubukcua *, Burcin Hepguzela, Zeynep Onderb, Burak Tumera  
aCity and Regional Planning, Faculty of Architecture,  
bGeographic Information Systems, The Graduate School of Natural and Applied Sciences,  
Dokuz Eylul University, Turkey 
Abstract 
In 2007 a private firm in Seattle, Washington began to measure and publish walkability maps of cities, 
neighborhoods, and streets  via “walkscore.com”. Although “walkscore” have been criticized for its parameters, no 
alternative have been provided so far. More over, such maps have been published for developed countries. No such 
initiative has been undertaken in developing countries. This study aims to introduce and discuss an alternative model 
to measure walkability on street level via Geographic Information Systems (GIS). About  6500 street segments in 
nine districts of Izmır, Turkey have been digitized as well as the land use. A walkability score (based on betweenness 
/ centrality scores derived from street network and accessibility scores derived from landuse) for each street segment 
was measured via GIS and its extension Spatial Design Network Analysis (SDNA).  
 
© 2014 Published by Elsevier Ltd. Selection and peer-review under responsibility of the Centre for Environment-
Behaviour Studies (cE-Bs), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. 
 
Keywords: Active living; environment and behavior studies; walkability; geographic information systems 
1. Introduction 
According to World Health Organization (WHO)  Report in 2012, about 1.4 billion adults were 
overweighed and about 500 million were obese (http://www.who.int/mediacentre/factsheets/fs311/en/; 
last accessed 21.02.2013). WHO also claimed that obesity and overweight could be prevented by 
encouraging physical activity  and active living among populations. In other words, active living is a 
 
* Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 .  
E-mail address: ebru.cubukcu@gmail.com.  
© 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license 
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Pe r-re ew under r ponsibility of Cen re for Environment-Beh viour Studies (cE-Bs), Faculty of Architecture, Planning & Surve ing, 
Universiti Teknologi MARA, Malaysia.
230   Ebru Cubukcu et al. /  Procedia - Social and Behavioral Sciences  168 ( 2015 )  229 – 237 
necessity for a sustainable future. General and scientific knowledge provides evidence for this claim. 
Research also showed that active living projects could achieve success when public participation in such 
projects could be satisfied (and vice a versa) (Hamamoto, Derauf & Yoshimura, 2009).  Awareness for 
such projects could be increased through effective communication strategies (like walkability maps of 
neighborhoods).  
Active living  is described as “a way of life that integrates physical activity into everyday routines, 
such as walking for transportation”. However, in the last decade people tend to do less exercise in their 
daily routine and rely on automobiles for transportation (rather than walking for transportation). This 
decline in mode share of walking is a global problem (Cubukcu, 2013). Leather, Fabian, Gota and Mejia 
(2011) reported the following striking findings for walking mode share changes in some Asian cities 
(Table 1):  
Table 1. Walking mode share declines in Asian Cities (Leather, Fabian, Gota and Mejia, 2011, page 4) 
City Year Before (%) Year After (%) (%) Mode with Greatest Gain (Motorized) 
Bangalore  1984 44.00 2007 8.33 Two-wheeler and car 
Changzhou 1986  38.24  2006  21.54  Two-wheeler and car 
Chennai  2002  47.00  2008  22.00  Two-wheeler 
Delhi  2002  39.00  2008  21.00  Two-wheeler and car 
Xi'an  2002  22.94  2006 15.78  Bus 
Nanchang  2001  44.99  2005  39.11  Car 
Shanghai  1986  38.00  2004  10.40  Two-wheeler and bus 
 
Given that, encouraging walkability through design is a major concern in Asian cities as well as 
developed countries.  Although the majority of research on walkability of various physical settings have 
been held in developed countries, studies in Asian cities could not be underestimated (Azmi & Karim, 
2012; Azmi et al. 2013; Azmi, Karim & Amin 2012; Abd-Latif et al. 2011; Sapawi & Said, 2013; 
Cubukcu, 2013).  
In developed countries, encouraging active living (and walking for transportation) has been declared as 
the main goal of municipalities. To that end, several municipalities have been trying to measure 
walkability of various settings and collect standardized data to understand the current condition to 
investigate the ways to improve and encourage walkability in the neighbourhood. As William Thomson 
put it out in his writings in 1883: 
“…when you can measure what you are speaking about, and express it in numbers, you know 
something about it; but when you cannot measure it, when you cannot express it in numbers, your 
knowledge is of a meagre and unsatisfactory kind...”  (http://en.wikiquote.org/wiki/William_Thomson; 
last access 15.01.2014) 
 In 2007 a private firm in Seattle, Washington  began to measure walkability score of cities, 
neighborhoods, streets, and  any address. Since then, they have been publishing maps via 
“walkscore.com”. With that publicly viewable open data, municipalities and public institutions found 
themselves in a kind of race to improve walkability in their area. In fact some municipalities claim that 
those walkability maps have been effecting the real estate market and leading the planning process. 
Although the maps produced via “walkscore” have been criticized for the parameters they used, no 
alternative have been provided so far. More over, such scores have been published for cities in United 
States, Australia, and England. However, such maps for Asian cities or developing countries have not 
been produced yet. This study aims to describe an alternative model to measure and understand the 
231 Ebru Cubukcu et al. /  Procedia - Social and Behavioral Sciences  168 ( 2015 )  229 – 237 
walkability at street level. The study was held in accordance with Izmir, Karsiyaka  Municipality. The 
decision makers in Karşıyaka Municipality commit to the project and plan to use the outcomes in 
planning in general and urban design and urban transformation projects 
(http://yerel.iha.com.tr/karsiyakanin-yuruyus-haritasi-cikariliyor-izmir-20140111AW000127) in 
particular. In other words, the study is still an ungoing project. Preliminary finding would be shared with 
this manuscript.  
2. Methodology 
2.1. Site 
The study was held in Izmir Turkey, third largest city on the west coast of Turkey (Figure 1). 
About  6500 street segments in nine districts of Karsiyaka, Izmır were digitized. The districts involved 
planned and unplanned areas for upper middle, middle and lower middle social class residents.  
The location of commercial activities, green areas, schools, bus stops, ferry, light rail stops have been 
obtained from the archival data of Karsiyaka Municipality and updated via Google Earth, Yandex and on 
site visits. Using Geographic Information Systems and its extension Spatial Design Network Analysis 
(which was developed by Cardiff School of Planning & Geography and the Sustainable Places Research 
Institute) walkability score of each street was measured.  
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig 1. The study was held in Izmir, Turkey 
 
 
 
232   Ebru Cubukcu et al. /  Procedia - Social and Behavioral Sciences  168 ( 2015 )  229 – 237 
 
 
 
 
 
 
 
 
 
 
Figure 2 About 6500 street segments and landuse in nine districts were digitized.  
2.2. Measuring Walkability Scores 
A voluminous number of research listed a voluminous number of parameters as related to active living. 
An extensive review of literature on those parameters grouped them  under seven headings: (1) landuse, 
(2) accessibility, (3) traffic safety, (4) crime safety, (5) walking and cycling comfort, (6) environmental 
aesthetics & upkeep,  (7) others (e.g. social relations within the neighborhood) (Cubukcu & Hepguzel, 
2013). In order to calculate walkability of streets and neighbourhoods related to landuse and accessibility, 
researchers measured density, non residential diversity, presence of recreational, commercial areas and 
schools, connectivity and interaction scores per street based on Space Sytntax analyses, network distance 
to non residential areas, commercial areas, public transportation.  On the other hand, “Walk Score”, 
which is a patent-pending system, measures walkability of regions, neighbourhoods and streets  based on 
distances to amenities (such as green areas and commercial areas). This study attempts to develop a 
similar model for a different geography and measure walkability per street segment. The measure 
developed in here is based on two main parameters: (1) network design (centrality and betweenness 
scores), (2) accessibility to amenities (network distance to shopping malls, market place,  mixed use 
areas, schools, green areas, bus stops, ferry, and light rail stops).  
Network design was evaluated via betweenness and centrality scores for each street segment using 
“Spatial Design Network Analysis” extension for ArcGIS. The software offers two main analyses types: 
euclidian and angular. Euclidian analyses minimizes the distance (based on shortest path) and angular 
analyses minimizes the number of turns (minimum turn). The measures used in this study are based on 
Euclidean measures. Betweenness score measures the number of times the link (street segment) lies on 
the shortest paths between other pairs of street segments. In other words, it measures the number of paths 
that pass a street segment.  Centrality score measures the difficulty (on average) of navigating to all 
possible destinations (links street segments) from each link (street segment).  Figure 3 shows the 
betweenness and centrality ratings for each street segment. Scores increase from dark green, light green, 
yellow, orange to red. Centrality scores ranged between 2.02 and 7.56. Betweenness scores ranged 
between 0.99 and 1214.29. Higher betweenness sores (orange and red lines) indicated that the street 
233 Ebru Cubukcu et al. /  Procedia - Social and Behavioral Sciences  168 ( 2015 )  229 – 237 
segment lies between the shortest path of more links. Higher centrality scores (orange and red lines) 
indicated that it is easier to reach to all other links in the network (higher in centrality).  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig 3. (a)Left: Centrality Scores; (b) Right: Betweenness scores 
Accessibility to amenities was evaluated via “Network Analysis” extension for ArcGIS. The closest 
facility analysis tool was used to measure the shortest network distance from each street segment (center 
point) to each amenity (center point). Three types of commercial areas (large scale shopping malls, 
market places, and small scale commercial activity in mixed used areas) were specified. Building layer 
was classified for each commercial type. Figure 4 shows the accessibility from each street segment to 
closest amenity. Scores increase from dark green, light green, yellow, orange to red. Network distances to 
closest large scale shopping mall ranged between 4.95 and 5873.45 meters. Network distances to closest 
commercial building in mixed use areas ranged between 0 and 1443.87 meters. Network distances to 
closest market area ranged between 17.73 and 3429.16 meters. The scores were normalized for each 
commercial activity and added up to measure a composite score for accessibility to commercial areas. For 
the composite measure, the scores ranged between 0 and 1. Higher sores (orange and red lines) indicated 
higher network distances, lower accessibility.  
For accessibility to public transportation three measures were used: network distance to closest bus 
stop, ferry, and light rail. The location of stop was specified as point. Figure 5 shows the accessibility 
from each street segment to closest public transportation stop. Scores increase from dark green, light 
green, yellow, orange to red. Network distances to closest bus stop ranged between 0 and 1433,71 meters. 
Network distances to closest ferry ranged between 7 and 5369,96 meters. Network distances to closest 
light rail transit stop ranged between 4.99 and 3562.23 meters. The scores were normalized for each 
public transportation stop and added up to measure a composite score for accessibility to public 
transportation. For the composite measure, the scores ranged between 0 and 1. Higher sores (orange and 
red lines) indicated higher network distances, lower accessibility.  
CENTRALITY SCORES  
(Spatial Design Network Analyses) 
BETWEENNESS SCORES 
(Spatial Design Network Analyses) 
234   Ebru Cubukcu et al. /  Procedia - Social and Behavioral Sciences  168 ( 2015 )  229 – 237 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 4 Upper Left: Network distance to shopping malls, Upper Right: Network distance to market areas, Lower Left: Network  
distance to commercial areas in mixed landuse, Lower Right: Composite score for accessibility to commercial areas. The black dots 
in each map shows the location of each commercial activity for each type.   
Figure 6 shows the accessibility from each street segment to closest school and green area. Scores 
increase from dark green, light green, yellow, orange to red. Network distance to closest school ranged 
between 0 and 1429.24 meters. Network distance to closest green area ranged between 0 and 1912.68 
meters. The scores were normalized and ranged between 0 and 1. Higher sores (orange and red lines) 
indicated higher network distances, lower accessibility.  
 
235 Ebru Cubukcu et al. /  Procedia - Social and Behavioral Sciences  168 ( 2015 )  229 – 237 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
Fig. 5 Upper Left: Network distance to ferry, Upper Right: Network distance to light rail transit stops, Lower Left: Network  
distance to bus stops, Lower Right: Composite score for accessibility to public transportation. The black dots in each map shows the 
location of each public transportation stop for each type.   
 
 
 
 
 
 
236   Ebru Cubukcu et al. /  Procedia - Social and Behavioral Sciences  168 ( 2015 )  229 – 237 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 6.  Left: Network distance to green areas, Right: Network distance to schools. 
3. Findings 
A composite walkabililty score was measured based on above criterias. Each score was normalized 
and the sum of normalized network design scores were subtracted from the sum of normalized 
accessibility scores to various amenities score as higher scores for  betweenness and centrality scores 
have positive meaning for walkability and higher scores for accessibility have negative meaning for 
walkability. Figure 7 shows the walkability map for nine districts in Karsiyaka, Izmir. Higher scores 
(orange and red lines) indicated lower walkability, lower scores (green lines) indicated higher walkability. 
4. Conclusion 
This study aims to describe a model to measure walkability of street segments and develop walkability 
maps.  The methodology described in here is computation intensive, yet it is possible with available 
software. We used two macro scale parameters to measure walkability of street segments: network design 
and accessibility to various amenities. The model is open to improvement; data collected via street audits 
(presence of sidewalks, traffic safety, environmental aesthetics etc.)  could be integrated into the model. 
Moreover, in the present model, the parameters used to evaluate walkability of each street segment was 
evaluated equally. With this model it is possible to weight the importance of each parameter and achieve 
alternative maps by changing the weight of each parameter in the model.     
More research is on call to investigate the relation between actual walkability in the area and 
walkability maps based on specific parameters. In other words, next step is to test the accuracy of these 
maps. A good direction for future research is to examine to what extend these maps shows the actual 
situation to discuss the strengths and limitations of this model.  
 
237 Ebru Cubukcu et al. /  Procedia - Social and Behavioral Sciences  168 ( 2015 )  229 – 237 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 7. Walkability scores based on  betweenness, centrality and accessibility to various areas.  
Acknowledgements 
       This present research is supported by The Scientific and Technological Research Council of Turkey. Project 
No: 111K383 - 1001 Research Project.  We also would like to thank to Karsıyaka Municipality, Izmir, Turkey for 
sharing the digital data.   
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Active Living for Sustainable Future: A Model to Measure “Walk Scores” via Geographic Information Systems 

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Active Living for Sustainable Future: A Model to Measure “Walk Scores” via Geographic Information Systems

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