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An urban informatics approach to understanding residential mobility in Metro Chicago

Author

Listed:
  • Haozhi Pan

    (12474Shanghai Jiao Tong University, China)

  • Si Chen
  • Yizhao Gao
  • Brian Deal

    (14589University of Illinois at Urbana-Champaign, USA)

  • Jinfang Liu

Abstract

This paper proposes that urban informatics can represent a flow of information from diverse and voluminous data into and back from the planning process. We present a proof-of-concept of how an urban informatics approach can improve public understanding of essential information in the planning process, and we propose a theoretical construct of how it can make planning processes more democratic and participatory, especially for disadvantaged groups. Specifically, this study uses a multi-dimensional spatial scan technique to discover household movement patterns in Chicago from millions of household address records between 2006 and 2015. The results locate the types of movement and displacement associated with specific places at a highly detailed spatial resolution. A user-interface is developed and proposed to enhance sharing of the information to the general public and involves diverse stakeholders in policy-making processes.

Suggested Citation

  • Haozhi Pan & Si Chen & Yizhao Gao & Brian Deal & Jinfang Liu, 2020. "An urban informatics approach to understanding residential mobility in Metro Chicago," Environment and Planning B, , vol. 47(8), pages 1456-1473, October.
    • Handle: RePEc:sae:envirb:v:47:y:2020:i:8:p:1456-1473
    DOI: 10.1177/2399808320924437
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    References listed on IDEAS

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    Cited by:

    1. Maria Cerreta & Simona Panaro & Giuliano Poli, 2021. "A Spatial Decision Support System for Multifunctional Landscape Assessment: A Transformative Resilience Perspective for Vulnerable Inland Areas," Sustainability, MDPI, vol. 13(5), pages 1-22, March.
    2. Saeid Nazari Adli & Subeh Chowdhury, 2021. "A Critical Review of Social Justice Theories in Public Transit Planning," Sustainability, MDPI, vol. 13(8), pages 1-13, April.
    3. Essam A. Rashed & Akimasa Hirata, 2021. "One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan," IJERPH, MDPI, vol. 18(11), pages 1-16, May.
    4. Haozhi Pan & Stan Geertman & Brian Deal, 2020. "What does urban informatics add to planning support technology?," Environment and Planning B, , vol. 47(8), pages 1317-1325, October.
    5. Yanliu Lin & Kasper Benneker, 2022. "Assessing collaborative planning and the added value of planning support apps in The Netherlands," Environment and Planning B, , vol. 49(2), pages 391-410, February.
    6. Filippo Carlo Pavesi & Anna Richiedei & Michele Pezzagno, 2021. "Advanced Modelling Tools to Support Planning for Sand/Gravel Quarries," Sustainability, MDPI, vol. 13(11), pages 1-14, June.

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