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.

• Introduces how new methods such as gradient boosting decision tree, PageRank, and graph convolutional neural networks can be used to unravel the relationships between transit area features and functions
• Uses a case study method to enhance quantitative analysis of transit areas, including comparative case studies wherever feasible
• Invites the reader to use desktop searches and text mining to explore and visualize the relationships between features and performance of transit areas described in the text, providing a some form of experiential learning