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Evaluating Urban Sensing Within Engaged Planning and Design

Evaluating Urban Sensing Within Engaged Planning and Design

Andrew Mondschein, Mona El Khafif
Copyright: © 2022 |Pages: 22
ISBN13: 9781799890904|ISBN10: 1799890902|ISBN13 Softcover: 9781799890911|EISBN13: 9781799890928
DOI: 10.4018/978-1-7998-9090-4.ch003
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MLA

Mondschein, Andrew, and Mona El Khafif. "Evaluating Urban Sensing Within Engaged Planning and Design." Trends and Innovations in Urban E-Planning, edited by Carlos Nunes Silva, IGI Global, 2022, pp. 1-22. https://doi.org/10.4018/978-1-7998-9090-4.ch003

APA

Mondschein, A. & El Khafif, M. (2022). Evaluating Urban Sensing Within Engaged Planning and Design. In C. Nunes Silva (Ed.), Trends and Innovations in Urban E-Planning (pp. 1-22). IGI Global. https://doi.org/10.4018/978-1-7998-9090-4.ch003

Chicago

Mondschein, Andrew, and Mona El Khafif. "Evaluating Urban Sensing Within Engaged Planning and Design." In Trends and Innovations in Urban E-Planning, edited by Carlos Nunes Silva, 1-22. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-9090-4.ch003

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Abstract

Civic technology models propose that urban environmental sensing and analysis can inform community-based decision making, but technology-driven planning and design is still subject to systemic challenges including a lack of community trust and local government capacities. This chapter examines the problem of integrating urban sensing into engaged planning and design. The authors assess the outcomes of two planning and design efforts in Virginia, USA utilizing the community-centered urban sensing (CCUS) and engagement platform. Developed by planners, urban designers, landscape architects, and data scientists at the University of Virginia, CCUS addresses the need for actionable information on the urban environment through community-engaged urban data collection and analysis. The cases provide evidence for the importance of engagement around sensing itself. Based on these findings, the authors propose a conceptualization of urban sensing applications that accounts for a range of local contexts from high capacity and trust to dysfunction and mistrust.