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A Generalized Framework for Measuring Pedestrian Accessibility around the World Using Open Data

Author

Listed:
  • Liu, Shiqin
  • Higgs, Carl
  • Arundel, Jonathan
  • Boeing, Geoff

    (Northeastern University)

  • Cerdera, Nicholas
  • Moctezuma, David
  • Cerin, Ester
  • Adlakha, Deepti
  • Lowe, Melanie
  • Giles-Corti, Billie
Abstract
Pedestrian accessibility is an important factor in urban transport and land use policy and critical for creating healthy, sustainable cities. Developing and evaluating indicators measuring inequalities in pedestrian accessibility can help planners and policymakers benchmark and monitor the progress of city planning interventions. However, measuring and assessing indicators of urban design and transport features at high resolution worldwide to enable city comparisons is challenging due to limited availability of official, high quality, and comparable spatial data, as well as spatial analysis tools offering customizable frameworks for indicator construction and analysis. To address these challenges, this study develops an open source software framework to construct pedestrian accessibility indicators for cities using open and consistent data. It presents a generalized method to consistently measure pedestrian accessibility at high resolution and spatially aggregated scale, to allow for both within- and between-city analyses. The open source and open data methods developed in this study can be extended to other cities worldwide to support local planning and policymaking. The software is made publicly available for reuse in an open repository.

Suggested Citation

  • Liu, Shiqin & Higgs, Carl & Arundel, Jonathan & Boeing, Geoff & Cerdera, Nicholas & Moctezuma, David & Cerin, Ester & Adlakha, Deepti & Lowe, Melanie & Giles-Corti, Billie, 2021. "A Generalized Framework for Measuring Pedestrian Accessibility around the World Using Open Data," SocArXiv cua35, Center for Open Science.
    • Handle: RePEc:osf:socarx:cua35
    DOI: 10.31219/osf.io/cua35
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    References listed on IDEAS

    as
    1. Geoff Boeing, 2020. "A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood," Environment and Planning B, , vol. 47(4), pages 590-608, May.
    2. Bev Wilson & Arnab Chakraborty, 2013. "The Environmental Impacts of Sprawl: Emergent Themes from the Past Decade of Planning Research," Sustainability, MDPI, vol. 5(8), pages 1-26, August.
    3. Boeing, Geoff, 2017. "OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks," SocArXiv q86sd, Center for Open Science.
    4. Sergio Rey, 2009. "Show me the code: spatial analysis and open source," Journal of Geographical Systems, Springer, vol. 11(2), pages 191-207, June.
    5. Boeing, Geoff, 2020. "Street Network Models and Indicators for Every Urban Area in the World," SocArXiv f2dqc, Center for Open Science.
    6. Boeing, Geoff, 2020. "The Right Tools for the Job: The Case for Spatial Science Tool-Building," SocArXiv d267g, Center for Open Science.
    7. Badland, Hannah & Whitzman, Carolyn & Lowe, Melanie & Davern, Melanie & Aye, Lu & Butterworth, Iain & Hes, Dominique & Giles-Corti, Billie, 2014. "Urban liveability: Emerging lessons from Australia for exploring the potential for indicators to measure the social determinants of health," Social Science & Medicine, Elsevier, vol. 111(C), pages 64-73.
    8. United Nations UN, 2015. "Transforming our World: the 2030 Agenda for Sustainable Development," Working Papers id:7559, eSocialSciences.
    9. Liming Zhang & Dieter Pfoser, 2019. "Using OpenStreetMap point-of-interest data to model urban change—A feasibility study," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-34, February.
    10. Giles-Corti, Billie & Lowe, Melanie & Arundel, Jonathan, 2020. "Achieving the SDGs: Evaluating indicators to be used to benchmark and monitor progress towards creating healthy and sustainable cities," Health Policy, Elsevier, vol. 124(6), pages 581-590.
    11. Karima Kourtit & Peter Nijkamp & Mark D. Partridge & Marlon G. Boarnet, 2013. "The declining role of the automobile and the re-emergence of place in urban transportation: The past will be prologue," Regional Science Policy & Practice, Wiley Blackwell, vol. 5(2), pages 237-253, June.
    12. Pengjun Zhao, 2013. "The Impact of Urban Sprawl on Social Segregation in Beijing and a Limited Role for Spatial Planning," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 104(5), pages 571-587, December.
    13. Boeing, Geoff, 2018. "Urban Spatial Order: Street Network Orientation, Configuration, and Entropy," SocArXiv qj3p5, Center for Open Science.
    14. Barrington-Leigh, Christopher Paul & Millard-Ball, Adam, 2019. "A global assessment of street network sprawl," OSF Preprints 6vp8j, Center for Open Science.
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    Cited by:

    1. van Heerden, Quintin & Karsten, Carike & Holloway, Jenny & Petzer, Engela & Burger, Paul & Mans, Gerbrand, 2022. "Accessibility, affordability, and equity in long-term spatial planning: Perspectives from a developing country," Transport Policy, Elsevier, vol. 120(C), pages 104-119.
    2. Bartzokas-Tsiompras, Alexandros & Bakogiannis, Efthimios & Nikitas, Alexandros, 2023. "Global microscale walkability ratings and rankings: A novel composite indicator for 59 European city centres," Journal of Transport Geography, Elsevier, vol. 111(C).
    3. Khaled Alawadi & Ngoc Hong Nguyen & Mariam Alkaabi, 2023. "The edge and the center in neighborhood planning units: assessing permeability and edge attractiveness in Abu Dhabi," Transportation, Springer, vol. 50(2), pages 677-705, April.
    4. Geoff Boeing & Carl Higgs & Shiqin Liu & Billie Giles-Corti & James F Sallis & Ester Cerin & Melanie Lowe & Deepti Adlakha & Erica Hinckson & Anne Vernez Moudon & Deborah Salvo & Marc A Adams & Ligia , 2022. "Using Open Data and Open-Source Software to Develop Spatial Indicators of Urban Design and Transport Features for Achieving Healthy and Sustainable Cities," Papers 2205.05240, arXiv.org.
    5. Govinda R. Poudel & Anthony Barnett & Muhammad Akram & Erika Martino & Luke D. Knibbs & Kaarin J. Anstey & Jonathan E. Shaw & Ester Cerin, 2022. "Machine Learning for Prediction of Cognitive Health in Adults Using Sociodemographic, Neighbourhood Environmental, and Lifestyle Factors," IJERPH, MDPI, vol. 19(17), pages 1-14, September.

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