Big and Open Data for High-quality Transit Access: Design, Features, and Performance of Multi-modal Transit Catchment Areas revisits the concept of "transit area" and the existing, normative, and future practice of transit area planning against the backdrop of increasing availability and usage of big and open data in transit-land use planning. Using empirical data and case studies, it illustrates how transit area can be epitomized and defined in two dimensions: feature (form) and performance (function) and how big and open data and its combination with data from traditional sources can be used to characterize and quantify the two dimensions and to unravel their complex relationships. This book synthesizes the state-of-the-art in how big and open data has been exploited to facilitate transit-area planning, and it proposes a normative framework for transit-area planning. In this framework, big and open data, alone and in combination with data from traditional sources, can play a role that data from traditional sources alone cannot. The author takes a mixed-method approach to present and convey the contents to the reader. Survey data collected by the author are used to show what kind of big and open data has been and should be used in the current/future transit-area planning practices. Quantitative analysis and visualization based on real-world big and open data are employed when the book turns to issues concerning how transit area can be (re)defined and how to quantify and verify the relationships between the two dimensions of transit area: feature and performance. This book serves as a valuable reference to urban planners, researchers, students, and decision makers interested in the nexus of transit/transportation, land use, and sustainable development.
Zhou
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Weitere Infos & Material
1. Transit areas 2. A normative framework for transit-area planning 3. Big and open data in transit-area planning: Theory 4. Traditional approaches to transit-area planning 5. Big and open data meets transit-area planning: Practice 6. Identifying and measuring features of transit areas 7. Defining and measuring performance of transit areas 8. Transit areas: Linking features and performance Conclusion: A future of big and open data in transit-area planning
Zhou, Jiangping
Dr. Jiangping Zhou is Associate Professor and Deputy Director of the Master of Urban Planning program at University of Hong Kong (HKU). He is Chief Examiner of Master of Transport Policy and Planning at HKU. He regularly delivers sessions on big data analytics on public transportation for HKU's Master of Urban Analytics program. His research focuses on transport/transit systems and land use connections and how to improve their performance. He has published more than 70 articles in English in leading journals such as Proceedings of National Academy of Sciences of the United States of America, Urban Studies, and Journal of American Planning Association. He also has more than 60 refereed articles in the Chinese language. He is currently leading several research projects exploring the nexus of physical infrastructure, land use, and travel behaviors and how better/new data supply/analytics can produce new insights into it and can inform public policies.
