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

Trevor M. Harris ()
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Trevor M. Harris: West Virginia University

Chapter Chapter 11 in Regional Research Frontiers - Vol. 2, 2017, pp 181-191 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:adspcp:978-3-319-50590-9_11

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DOI: 10.1007/978-3-319-50590-9_11

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