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 The Workshop
This course provides an introduction to and overview of the application of spatial data analysis techniques in empirical social science research. With the exponentially growing use of geographic information systems (GIS) to store, manipulate and visualize geocoded information, it is increasingly important to understand the particular nature of geographic data and the specialized statistical techniques required for its analysis. The focus of the course is on how techniques for the analysis of spatial data can be effectively applied in a GIS environment, with a particular emphasis on the study of spatial patterns and spatial autocorrelation, such as the detection of clusters, outliers and any other relationships that pertain to the absolute and relative location of observations. Common applications of spatial data analysis techniques in the social sciences range from the discovery of crime clusters, hot spots and the detection of disease clusters, to spatial autocorrelation of demographic variables and regression models for real estate analysis.
The course reviews five main aspects of spatial data analysis:
- spatial data visualization and exploration (including the application of dynamically linked windows)
- the analysis of clusters and point patterns (including space-time cluster statistics)
- global and local indicators of spatial autocorrelation (including LISA and visualization of spatial autocorrelation)
- variogram analysis (basic concepts of geostatistics)
- introduction to spatial regression analysis
The emphasis in the course is on introducing concepts and techniques, not on becoming an expert in any of the specific areas covered. In other words, this is not a course on GIS or on geostatistics or on spatial regression analysis in and of themselves, but these topics are all covered as part of an overview of spatial data analysis methods.
The main focus is on data description and exploration. More advanced topics pertaining to spatial regression analysis are not considered here, but treated in a separate course. In addition to an overview of the main methodological issues and most commonly used test statistics, an important component of the course is to gain hands-on experience in the use of a range of software tools such as SpaceStat™.
Prerequisites include a familiarity with multivariate statistics and basic concepts of probability theory, as well as a familiarity with desktop GIS software (for example, as gained from the interactive web tutorials provided by several vendors). If you do not have this background, taking one of the more introductory courses in the CSISS portfolio is highly recommended.
Host Institution
The course will be hosted by the Center for Spatially Integrated Social Science (CSISS) and held on the beautiful campus of the University of California, Santa Barbara. Visit About Santa Barbara and
Local Weather to prepare for your workshop week.
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