Is Walkability Equally Distributed Among Downtowners? Evaluating the Pedestrian Streetscapes of Eight Uropean Capitals Using a Micro-Scale Audit Approach

Is Walkability Equally Distributed Among Downtowners? Evaluating the Pedestrian Streetscapes of Eight Uropean Capitals Using a Micro-Scale Audit Approach

Alexandros Bartzokas-Tsiompras Eleftheria Maria Tampouraki Yorgos N. Photis

Department of Geography & Regional Planning, National Technical University of Athens, Greece

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In this paper, we evaluate different elements of the urban micro-scale environment in eight European capitals’ downtown areas (i.e. Vienna, Copenhagen, Warsaw, Madrid, Brussels, Budapest, Athens and Sofia) to provide insight into inequalities in walkability benefits due to spatial distribution. To this end, we utilize MAPS-Mini, the brief version of Microscale Audit of Pedestrian Streetscapes to record and assess, at the street level, 15 walkability related items based on the Google Street View service. Our total sample consists of about 15.736 street segments/crossings, while for reliability analysis reasons, a second rater was employed to cross assess 10% of street segments per city. Results showed that Vienna and Athens had the highest (50.4%) and lowest (32.1%) overall walkability scores, respectively. Assessments were further combined with the population estimates of the European Urban Atlas 2012 dataset to perform equity analysis by estimating the distribution of average walkability scores among the population living downtown in the examined cities. In doing so, we used the Gini (G.) index and constructed Lorenz curve graphs. Our findings reveal a landscape of high inequality in downtown walkability distribution since all Gini coefficients were higher than 0.43. However, the inequality was greatest in Brussels (G. = 0.60) and lowest in Budapest (G. = 0.43). Additionally, we used spatial statistics tests (i.e. global and local Moran’s I) to identify global and local patterns of walkability and population. The results indicated a highly clustered pattern of walkability across all downtowns and designated several clusters of uneven walkability geographies. Our approach sheds light on the application of active mobility strategies in different European cities, highlighting at the same time the need for further research to provide a clearer assessment of the spatial distribution of inequalities in social benefits and impact when designing sustainable urban neighbourhoods.


active mobility, downtown, city centre, walkability, urban planning, equality


[1] Gilderbloom, J.I., Riggs, W.W. & Meares, W.L., Does walkability matter? An examination of walkability’s impact on housing values, foreclosures and crime. Cities, 42(PA), pp. 13–24, 2015.

[2] Speck, J., Walkable City Rules: 101 Steps to Making Better Places, Island Press: Washington, DC, 2018.

[3] Knight, J., Weaver, R. & Jones, P., Walkable and resurgent for whom? The uneven geographies of walkability in Buffalo, NY. Applied Geography, 92, pp. 1–11, 2018.

[4] Bereitschaft, B., Equity in neighbourhood walkability? A comparative analysis of three large U.S. cities. Local Environment, 22(7), pp. 859–879, 2017.

[5] Bereitschaft, B., Equity in microscale urban design and walkability: A photographic survey of six Pittsburgh streetscapes. Sustainability (Switzerland), 9(7), p. art. no. 1233, 2017.

[6] Adkins, A., Makarewicz, C., Scanze, M., Ingram, M. & Luhr, G., Contextualizing walkability: Do relationships between built environments and walking vary by socioeconomic context? Journal of the American Planning Association, 83(3), pp. 296–314, 2017.

[7] Van Dyck, D., et al., Neighborhood SES and walkability are related to physical activity behavior in Belgian adults. Preventive Medicine, 50(SUPPL.), pp. S74–S79, 2010.

[8] Riggs, W., Inclusively walkable: Exploring the equity of walkable housing in the San Francisco bay area. Local Environment, 21(5), pp. 527–554, 2014.

[9] Weng, M., et al., The 15-minute walkable neighborhoods: Measurement, social inequalities and implications for building healthy communities in urban China. Journal of Transport and Health, 13, pp. 259–273, 2019.

[10] Gullón, P., et al., Intersection of neighborhood dynamics and socioeconomic status in small-area walkability: The Heart Healthy Hoods project. International Journal of Health Geographics, 16(1), p., 2017.

[11] Kenyon, A. & Pearce, J., The socio-spatial distribution of walkable environments in urban scotland: A case study from Glasgow and Edinburgh. SSM - Population Health, 9, p., 2019.

[12] Frank, L.D., et al., The development of a walkability index: Application to the neighborhood quality of life study. British Journal of Sports Medicine, 44(13), pp. 924– 933, 2010.

[13] Bartzokas-Tsiompras, A. & Photis, Y.N., What matters when it comes to “walk and the city”? Defining a weighted GIS-based walkability index. Transportation Research Procedia, 24, pp. 523–530, 2017.

[14] Neckerman, K.M., et al., Disparities in urban neighborhood conditions: Evidence from GIS measures and field observation in New York city. Journal of Public Health Policy, 30(SUPPL.1), pp. S264–S285, 2009.

[15] Koschinsky, J., Talen, E., Alfonzo, M. & Lee, S., How walkable is Walker’s paradise? Environment and Planning B: Urban Analytics and City Science, 44(2), pp. 343–363, 2017.

[16] Cain, K.L., et al., Developing and validating an abbreviated version of the Microscale Audit for Pedestrian Streetscapes (MAPS-Abbreviated). Journal of Transport and Health, 5, pp. 84–96, 2017.

[17] Sallis, J.F., et al., Income disparities in perceived neighborhood built and social environment attributes. Health and Place, 17(6), pp. 1274–1283, 2011.

[18] Cain, K.L., et al., Contribution of streetscape audits to explanation of physical activity in four age groups based on the Microscale Audit of Pedestrian Streetscapes (MAPS). Social Science and Medicine, 116, pp. 82–92, 2014.

[19] Day, K., Boarnet, M., Alfonzo, M. & Forsyth, A., The Irvine-Minnesota inventory to measure built environments: Development. American Journal of Preventive Medicine, 30(2), pp. 144–152, 2006.

[20] Bethlehem, J.R., et al., The SPOTLIGHT virtual audit tool: A valid and reliable tool to assess obesogenic characteristics of the built environment. International Journal of Health Geographics, 13(1), p. art. no. 52, 2014.

[21] Dannenberg, A.L., Cramer, T.W. & Gibson, C.J., Assessing the walkability of the workplace: A new audit tool. American Journal of Health Promotion, 20(1), pp. 39–44, 2005.

[22] Clifton, K.J., Livi Smith, A.D. & Rodriguez, D., The development and testing of an audit for the pedestrian environment. Landscape and Urban Planning, 20(1-2), pp. 95– 110, 2007.

[23] Zhu, W., et al., Reliability between online raters with varying familiarities of a region: Microscale Audit of Pedestrian Streetscapes (MAPS). Landscape and Urban Planning, 167, pp. 240–248, 2017.

[24] Geremia, C., & Cain, K., MAPS-Mini, 2015. [Online]. Available: http://sallis.ucsd. edu/Documents/Measures_documents/MAPS-Mini%20Field%20Procedures%20%20 Picture%20Guide_090815.pdf. [Accessed 10 8 2019].

[25] Sallis, J.F., et al., Is your neighborhood designed to support physical activity? A brief streetscape audit tool. Preventing Chronic Disease, 12(9), p. art. no. 150098, 2015.

[26] Cain, K.L., et al., Development and reliability of a streetscape observation instrument for international use: MAPS-global. International Journal of Behavioral Nutrition and Physical Activity, 15(1), p. art. no. 19, 2018.

[27] Braun, L.M. & Malizia, E., Downtown vibrancy influences public health and safety outcomes in urban counties. Journal of Transport and Health, 2(4), pp. 540–548, 2015.

[28] Tammaru, T., Marcińczak, S., Van Ham, M. & Musterd, S., Socio-economic segregation in European capital cities: East meets West, Taylor and Francis Inc., 2015.

[29] Inchauste, G., Karver, J., Kim, Y. S. & Jelil, M. A., Living and leaving. housing, mobility, and welfare in the European Union, World Bank Report. [Online]. Available: http:// [Accessed 2019 8 5].

[30] Chaplain, C., The European cities leading the way in car-free living in a bid to tackle toxic air pollution, 2017. [Online]. Available: transport/the-european-cities-leading-the-way-in-carfree-living-in-a-bid-to-tackletoxic-air-pollution-a3658216.html. [Accessed 17 8 2019].

[31] Phillips, C.B., et al., Online versus in-person comparison of Microscale Audit of Pedestrian Streetscapes (MAPS) assessments: Reliability of alternate methods. International Journal of Health Geographics, 16(27), 2017.

[32] European Environment Agency, Urban Atlas 2012. [Online]. Available: https:// [Accessed 10 8 2019].

[33] Landis, J.R. & Koch, G.G., The measurement of observer agreement for categorical data. Biometrics, 33(1), pp. 159–174, 1977.

[34] Shrout, P.E., Measurement reliability and agreement in psychiatry. Statistical Methods in Medical Research, 7(3), pp. 301–317, 1998.

[35] Bartzokas-Tsiompras, A. & Photis, Y. N., Measuring rapid transit accessibility and equity in migrant communities across 17 European cities. International Journal of Transport Development and Integration, 3(3), pp. 245–258, 2019.

[36] Delbosc, A. & Currie, G., Using Lorenz curves to assess public transport equity. Journal of Transport Geography, 19(6), pp. 1252–1259, 2011.

[37] Batista E Silva, F., Poelman, H., Martens, V. & Lavalle, C., Population estimation for the Urban Atlas Polygons, European Commision, JRC Technical Reports, 2013. [Online]. Available: [Accessed 10 8 2019].

[38] ELSTAT, Population Census 2011, 2011. [Online]. Available: en/home/.

[39] Anselin, L., Local Indicators of Spatial Association—LISA. Geographical Analysis, 27(2), pp. 93–115, 1995