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
- 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.
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
Weather to prepare for your workshop week.