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Methods for Space-Time Analysis and Modeling: An Overview

Author

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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)

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.

Suggested Citation

  • Eric Delmelle & Changjoo Kim & Ningchuan Xiao & Wei Chen, 2013. "Methods for Space-Time Analysis and Modeling: An Overview," International Journal of Applied Geospatial Research (IJAGR), IGI Global, vol. 4(4), pages 1-18, October.
    • Handle: RePEc:igg:jagr00:v:4:y:2013:i:4:p:1-18
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    Cited by:

    1. Jixia Huang & Jinfeng Wang & Yanchen Bo & Chengdong Xu & Maogui Hu & Dacang Huang, 2014. "Identification of Health Risks of Hand, Foot and Mouth Disease in China Using the Geographical Detector Technique," IJERPH, MDPI, vol. 11(3), pages 1-17, March.

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