ABSTRACT
The availability of big data and rising computational power provides us an unprecedented opportunity to answer many questions raised in the preceding chapters in both spatial and temporal precisions quickly and effectively. Chapter 14 uses taxi trajectory data as an example to illustrate how to process and analyze spatiotemporal big data in urban studies. The chapter begins with an overview of big data, with a focus on taxi trajectory data, and explains how urban studies can benefit from such a data source. With a scaled-down taxi trajectory dataset in Shanghai, we use ArcGIS Pro to process data, examine activity patterns, and derive OD trips between areas. When the dataset is large, data processing and analysis require advanced programming or special software, which becomes a major technical barrier. For processing and analyzing the full-scale taxi trajectory dataset in Shanghai, we use XSTAR, a free package for the task. Its core design is a scalable index and storage structure and facilitates efficient data retrieval and processing. It fulfills challenging spatial analysis tasks with a user-friendly interface on a desktop computer.
