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Using Big and Open Data to Analyze Transit-Oriented Development

Author

Listed:
  • Jiangping Zhou
  • Yuling Yang
  • Chris Webster

Abstract

Problem, research strategy, and findings: In this study, we investigate how to exploit big and open data (BOD) to quantitatively examine the relationships between transit-oriented development (TOD) attributes and TOD outcomes. Here, TOD attributes are measurable or perceivable attributes that TOD proponents cherish, and TOD outcomes are the targeted outcomes, such as increased ridership, associated at least partially with TOD attributes. Based on BOD from Shenzhen (China), we create indicators to measure both TOD attributes and outcomes. We explore the associations of TOD attributes, including centrality of a TOD site, travel time to the central business district, density, destination, diversity, and design, with TOD outcomes. We identify the TOD attribute that best predicts TOD outcomes such as metro ridership, frequent riders, people co-located in a station area, and ratios derived from these outcomes. We find that special neighborhoods, specific metro lines, and age of the district significantly influence TOD outcomes. Our study has a few limitations: a) the BOD we use do not directly measure TOD attributes, so proxies must be used; and b) the BOD we use contain little information about “why,” “who,” and “how,” such as why people rode transit, who they were, and how they perceived/appreciated various TOD attributes.Takeway for practice: BOD-derived variables allow planners to revalidate existing planning guidelines and principles concerning TOD and adapt them to local contexts. BOD can also be used to formulate new metrics to evaluate different TOD plans or projects in ways not achievable with traditional data alone. In short, BOD can and should be used to refine TOD analytics and design and to implement corresponding theories in pursuit of TOD.

Suggested Citation

  • Jiangping Zhou & Yuling Yang & Chris Webster, 2020. "Using Big and Open Data to Analyze Transit-Oriented Development," Journal of the American Planning Association, Taylor & Francis Journals, vol. 86(3), pages 364-376, July.
  • Handle: RePEc:taf:rjpaxx:v:86:y:2020:i:3:p:364-376
    DOI: 10.1080/01944363.2020.1737182
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    Citations

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    Cited by:

    1. Xiyuan Ren & ChengHe Guan & De Wang & Junyan Yang & Bo Zhang & Michael Keith, 2022. "Exploring land use functional variance using mobile phone derived human activity data in Shanghai," Environment and Planning B, , vol. 49(9), pages 2531-2547, November.
    2. Liu, Yudi & Nath, Nabamita & Murayama, Akito & Manabe, Rikutaro, 2022. "Transit-oriented development with urban sprawl? Four phases of urban growth and policy intervention in Tokyo," Land Use Policy, Elsevier, vol. 112(C).
    3. Rao, Fujie & Pafka, Elek, 2021. "Shopping morphologies of urban transit station areas: A comparative study of central city station catchments in Toronto, San Francisco, and Melbourne," Journal of Transport Geography, Elsevier, vol. 96(C).
    4. Liao, Cong & Scheuer, Bronte, 2022. "Evaluating the performance of transit-oriented development in Beijing metro station areas: Integrating morphology and demand into the node-place model," Journal of Transport Geography, Elsevier, vol. 100(C).
    5. Yang, Hongtai & Ping, An & Wei, Hongmin & Zhai, Guocong, 2023. "Unique in the metro system: The likelihood to re-identify a metro user with limited trajectory points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).

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