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

  • Trevor M. HarrisEmail author
Chapter
  • 615 Downloads
Part of the Advances in Spatial Science book series (ADVSPATIAL)

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.

Keywords

Spatial Autocorrelation Spatial Data Geographically Weight Regression Exploratory Data Analysis Regional Science 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Geology and GeographyWest Virginia UniversityMorgantownUSA

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