Space–Time Analysis: Concepts, Quantitative Methods, and Future Directions

Abstract

Throughout most of human history, events and phenomena of interest have been characterized using space and time as their major characteristic dimensions, in either absolute or relative conceptualizations. Space–time analysis seeks to understand when and where (and sometimes why) things occur. In the context of several of the most recent and substantial advances in individual movement data analysis (time geography in particular) and spatial panel data analysis, we focus on quantitative space–time analytics. Based on more than 700 articles (from 1949 to 2013) we obtained through a key word search on the Web of Knowledge and through the authors' personal archives, this article provides a synthetic overview about the quantitative methodology for space–time analysis. Particularly, we highlight space–time pattern revelation (e.g., various clustering metrics, path comparison indexes, space–time tests), space–time statistical models (e.g., survival analysis, latent trajectory models), and simulation methods (e.g., cellular automaton, agent-based models) as well as their empirical applications in multiple disciplines. This article systematically presents the strengths and weaknesses of a set of prevalent methods used for space–time analysis and points to the major challenges, new opportunities, and future directions of space–time analysis.