A Framework for the Comparative Analysis of Diverse Mobility Data

Keywords: spatial analysis, human mobility, big data, multiscale, spatiotemporal
Abstract Type: Paper Abstract

Abstract

The complex nature of human systems can make it difficult to fully grasp modern urban challenges. The solutions to pressing societal concerns frequently rely on an understanding of the dynamics of human activity and mobility. Mobility data provides valuable information about human behavior but, due to privacy concerns, fine-resolution data is difficult to acquire. However, big data collected from GPS-enabled devices creates new opportunities to study human movement. Unfortunately, the size, scale, and structure of these mobility datasets produce a new generation of obstacles. We developed a framework for comparing mobility datasets to extract important characteristics of human mobility. The insights derived from our analysis indicate how diverse sources of mobility data can be used in tandem to improve our understanding of human behavior. To demonstrate the value of our approach, this paper applies the framework to a case study in San Diego County, California, United States (US).