and the University of California, Santa Barbara
University of Illinois, Urbana-Champaign
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
(1) spatial data visualization and exploration (including
the application of dynamically linked windows);
(2) the analysis of clusters and point patterns (including
space-time cluster statistics);
(3) global and local indicators of spatial autocorrelation
(including LISA and visualization of spatial autocorrelation);
(4) variogram analysis (basic concepts of geostatistics);
(5) introduction to spatial regression analysis.
The main focus will be 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, CrimeStat and various
extensions to commercial GIS products.
Prerequisites include a familiarity with multivariate
statistics and basic concepts of probability theory,
as well as a some knowledge of desktop GIS software
(for example, as gained from the interactive web tutorials
provided by several vendors).
The course will be held on the beautiful, coastal campus
of the University of California, Santa Barbara.