ABSTRACT
The analysis of spatiotemporal data has intrigued researchers as the confluence of space-time GIS and big data becomes an important theme in the domain of geographic information science. While spatial data analysis has been maturely developed, its extension to the spatiotemporal dimension, that is, the study of spatiotemporal data analysis–remains inadequate. In this chapter, we focus our discussion on spatiotemporal point pattern analysis to gain insight into this issue. We discuss recent approaches to facilitate the visualization and analysis of point patterns in space, time, and space-time. These approaches provide support for statistically robust estimation on spatiotemporally explicit characteristics of point patterns. We present a case study by applying spatiotemporal point pattern analysis within the context of dengue fever in Colombia, a vector-borne disease. In particular, we implement the spatiotemporal Ripley's K function at various scales to estimate the spatiotemporal signature of dengue fever. Our analysis results indicate that spatiotemporal point pattern analysis is pivotal to promoting our understanding of space-time complexity in dynamic spatial phenomena, represented by the diffusion of dengue fever.
