Urban science and the future of sustainable cities: Planning support systems and open data perspectives

Urban science represents an emerging yet loosely defined field that typically leverages big data and information and communication technologies (ICTs) to address urban issues in an interdisciplinary setting. Translating voluminous amounts of data into useful information for decision-making has received increasing attention in recent years but has not been sufficiently resolved. This dissertation responds to the increasing need for better defining urban science and better understanding its role in urban planning. I seek to understand the role of emerging data and technology in innovative planning support strategies. I focus my study on Planning Support Systems (PSSs) and explore several opportunities for enhancing PSSs with open data, big data and open-source software. PSSs represent sustained intellectual efforts to improve urban planning outcomes through the development of tools, models, and processes to enable more informed decisions. The following research questions guide this research: 1) at the background of smart cities and big data, what are the opportunities and barriers for integrating contemporary technologies in the development of ‘next generation’ PSSs? 2) How can PSSs leverage open data, big data, and open-source software to achieve better decision support for public policy? And 3) How can open data and urban science add to the broader subject of urban planning? I answer these questions through a mixture of literature review and model prototyping. I first draw insights from urban science studies to critically assess ways of successfully navigating urban planning challenges posed by digital technology. I contend that PSSs need to be enhanced by urban science and open data to be more transparent and user-friendly, while paying explicit attention to its application contexts. I then explore and build more open, and context-sensitive planning support models based on my research at the Land Use Evolution and Impact Assessment Model (LEAM) PSS at the University of Illinois at Urbana-Champaign. Through my model development work on three cases, I demonstrate ways of integrating open data, transparent and replicable processes, and interpretable modeling features into traditional PSSs, and argue that we can indeed better align data-informed decision-making with essential planning goals for the future development of PSSs. Furthermore, I review planning theory as well as research on citizen participation in the age of information and communication, reevaluate the value that urban science adds to planning in terms of increased transparency and inclusiveness, and identify questions and limitations that need to be further addressed. The findings of this dissertation inform urban planners about multiple technical aspects needed for enhancing PSSs applications, and urban scientists about social and ethical challenges presented in policymaking, thereby suggesting a more interdisciplinary urban science study in the future. Long-term urban science development requires more interdisciplinary education and practice as well as the focus on data ethics, algorithm justice, and public empowerment.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Regional Planning

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Cong, Cong

Contributors dc:contributor

• Deal, Brian
• Wilson, Bev
• Lee, Bumsoo
• Pendall, Rolf
• Kalantari, Zahra

• Urban Science
• Planning Support Systems
• Open Data

Statement dc:rights

• Copyright 2022 Cong Cong

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/117586

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F117586