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Interuniversity Consortium on Political & Social Research Summer Program
CSISS is offering scholarships to assist registrants in the following two workshops sponsored by ICPSR.
See www.icpsr.umich.edu/sumprog/S2000/ for details and requirements, or contact Hank Heitowit (), Director, ICPSR Summer Program.
ICPSR Workshop: Introduction to Spatial Data Analysis
Urbana-Champaign, IL
May 22-26, 2000
Instructor: Luc Anselin
Host Institution: ICPSR and the University of Illinois
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:
(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); and
(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 in the laboratory facilities of the Office of Computing and Communications for the Social Sciences at the University of Illinois, Urbana-Champaign.
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ICPSR Workshop: Spatial Regression Analysis
Ann Arbor MI
August 14-18, 2000
Instructor: Luc Anselin
Host Institution: ICPSR, University of Michigan
Spatial regression analysis or spatial econometrics is the collection of statistical and econometric methods specifically geared at dealing with problems of spatial dependence and spatial heterogeneity encountered in cross-sectional (and panel) data sets. The use of spatial econometric techniques is increasingly common in empirical work in the social sciences, including urban and regional economics, criminology, demography and electoral analyses.
The main objective of the course is to review the state of the art of the specification, estimation and testing of models that incorporate spatial dependence (and spatial heterogeneity).While the focus will be on spatial aspects, the types of methods covered have general validity in applied statistical work.
The course will include topics such as the specification of dependent stochastic processes (specifically, various types of spatial autoregressive models), maximum likelihood estimation of dependent processes, instrumental variables and general method of moments estimation, specification tests, and asymptotic and finite sample properties. While most of the material will be applied to the standard regression model, some attention will be paid to panel data contexts (space-time models) as well as to spatial probit models. An important aspect of the course is the application of the spatial regression techniques in empirical practice, using the SpaceStat software package or general purpose statistical toolboxes.
Prerequisites include intermediate regression analysis (or intermediate econometrics) as well as familiarity with spatial data analysis at the level of ICPSR's "Introduction to Spatial Data Analysis" course.
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