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Exploratory Spatial Data Analysis: Tight Coupling Data and Space, Spatial Data Mining, and Hypothesis Generation

In: Regional Research Frontiers - Vol. 2

Author

Listed:
  • Trevor M. Harris

    (West Virginia University)

Abstract

Exploratory Spatial Data Analysis (ESDA) advances Tukey’s (1977) seminal work on Exploratory Data Analysis (EDA) through a tight coupling of geographical space to traditional EDA approaches. EDA represents both a philosophical and a methodological approach to data analysis and, in some contrast to inferential statistics, emphasizes hypothesis generation rather than hypothesis testing and confirmation. ESDA utilizes recent and dramatic advances in desktop processing and computer graphics to create an exploratory analytical environment capable of suggesting multiple pathways through the spatial data analysis process. ESDA provides a powerful idea and hypothesis generation platform with which to undertake complex data analysis and integrates well with recent advances in spatial statistical techniques, GIS, and geovisualization. The spatial and statistical modeling needs of regional science coupled with advances in big data and spatial data mining suggests ESDA will be of growing importance in geographical analysis and regional science in the future.

Suggested Citation

  • Trevor M. Harris, 2017. "Exploratory Spatial Data Analysis: Tight Coupling Data and Space, Spatial Data Mining, and Hypothesis Generation," Advances in Spatial Science, in: Randall Jackson & Peter Schaeffer (ed.), Regional Research Frontiers - Vol. 2, chapter 0, pages 181-191, Springer.
    • Handle: RePEc:spr:adspcp:978-3-319-50590-9_11
    DOI: 10.1007/978-3-319-50590-9_11
    as

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