Methods for Space-Time Analysis and Modeling: An Overview

Eric Delmelle, Changjoo Kim, Ningchuan Xiao and Wei Chen
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Eric Delmelle: The University of North Carolina at Charlotte, Charlotte, NC, USA
Changjoo Kim: University of Cincinnati, Cincinnati, OH, USA
Ningchuan Xiao: The Ohio State University, Columbus, OH, USA
Wei Chen: The Ohio State University, Columbus, OH, USA

International Journal of Applied Geospatial Research (IJAGR), 2013, vol. 4, issue 4, 1-18

Abstract: With increasing availability of spatio-temporal data and the democratization of Geographical Information Systems (GIS), there has been a demand for novel statistical and visualization techniques which can explicitly integrate space and time. The paper discusses the nature of spatio-temporal data, the integration of time within GIS and the flourishing availability of spatial and temporal-explicit data over the Internet. The paper attempts to answer the fundamental question on how these large datasets can be analyzed in space and time to reveal critical patterns. The authors further elaborate on how spatial autocorrelation techniques are extended to deal with time, for point, linear, and areal features, and the impact of parameter selection, such as critical distance and time threshold to build adjacency matrices. The authors also discuss issues of space-time modeling for optimization problems.

Date: 2013
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