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Crowdsourcing Bike Share Station Locations

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  • Greg P. Griffin
  • Junfeng Jiao

Abstract

Problem, research strategy, and findings: Planners increasingly involve stakeholders in co-producing vital planning information by crowdsourcing data using online map-based commenting platforms. Few studies, however, investigate the role and impact of such online platforms on planning outcomes. We evaluate the impact of participant input via a public participation geographic information system (PPGIS), a platform to suggest the placement of new bike share stations in New York City (NY) and Chicago (IL). We conducted 2 analyses to evaluate how close planners built new bike share stations to those suggested on PPGIS platforms. According to our proximity analysis, only a small percentage of built stations were within 100 feet (30 m) of suggested stations, but our geospatial analysis showed a substantial clustering of suggested and built stations in both cities that was not likely due to random distribution. We found that the PPGIS platforms have great promise for creating genuine co-production of planning knowledge and insights and that system planners did take account of the suggestions offered online. We did not, however, interview planners in either system, and both cities may be atypical, as is bike share planning; moreover, multiple factors influence where bike stations can be located, so not all suggested stations could be built.Takeaway for practice: Planners can use PPGIS and similar platforms to help stakeholders learn by doing and to increase their own local knowledge to improve planning outcomes. Planners should work to develop better online participatory systems and to allow stakeholders to provide more and better data, continuing to evaluate PPGIS efforts to improve the transparency and legitimacy of online public involvement processes.

Suggested Citation

  • Greg P. Griffin & Junfeng Jiao, 2019. "Crowdsourcing Bike Share Station Locations," Journal of the American Planning Association, Taylor & Francis Journals, vol. 85(1), pages 35-48, January.
  • Handle: RePEc:taf:rjpaxx:v:85:y:2019:i:1:p:35-48
    DOI: 10.1080/01944363.2018.1476174
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    Citations

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    Cited by:

    1. Parisa Zare & Christopher Pettit & Simone Leao & Ori Gudes, 2022. "Digital Bicycling Planning: A Systematic Literature Review of Data-Driven Approaches," Sustainability, MDPI, vol. 14(23), pages 1-20, December.
    2. Ruoxin Zhu & Diao Lin & Yujing Wang & Michael Jendryke & Rui Xin & Jian Yang & Jianzhong Guo & Liqiu Meng, 2020. "Social Sensing of the Imbalance of Urban and Regional Development in China Through the Population Migration Network around Spring Festival," Sustainability, MDPI, vol. 12(8), pages 1-21, April.
    3. Smith, C. Scott & Schwieterman, Joseph P., 2021. "Using multivariate adaptive regression splining (MARS) to identify factors affecting the performance of dock-based bikesharing: The case of Chicago’s Divvy system," Research in Transportation Economics, Elsevier, vol. 89(C).
    4. Marimuthu, Malliga & D'Souza, Clare & Shukla, Yupal, 2022. "Integrating community value into the adoption framework: A systematic review of conceptual research on participatory smart city applications," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    5. Lee, Hye Kyung & Jiao, Junfeng & Choi, Seung Jun, 2021. "Identifying spatiotemporal transit deserts in Seoul, South Korea," Journal of Transport Geography, Elsevier, vol. 95(C).

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