Keywords

Learning outcomes

This course combines lecture format and computing lab exercises. The focus of the course is on spatial statistical methods and analysis, not Geographic Information Systems (GIS). Application of spatial statistical methods in lab sessions will enable participants to pursue a broad range of social and behavioral science research. Software emphasis will be given to GeoDa and R for exploratory spatial data analysis and modeling. Some familiarity with this software is helpful but is not a prerequisite. Detailed R code will be provided and discussed in labs, and participants will learn how to interpret, visualize and map model output.

Contents

Spatially-referenced data add important contextual and locational information to the social and behavioral sciences, especially sociology, anthropology, political science, economics and public health. A geographic or spatial-analytic framework, which will be taught in this workshop, can be used to explore the importance of spatial relationships in a variety of social and behavioral processes. However, spatial data and spatial relationships necessitate the use of analytic tools beyond those provided by standard statistical methods such as OLS. The course introduces the standard spatial-analytic framework, and explores the range of issues that generally must be considered when analyzing spatial data. The role of spatial autocorrelation in spatial data sets is a focus of this course. Throughout the course we will address the following questions: how and why does spatial autocorrelation arise; how is it measured and understood; how does it relate to issues of spatial heterogeneity and spatial dependence; and how these factors inform the specification and estimation of regression models. Specific modeling techniques are covered including: spatial autocorrelation measures (Moran's I, Geary's C, LISA), spatial regression models (SAR / SARAR), and geographically weighted regression (GWR).

Evaluation and evaluation criteria

Numeric 1-5.
participation 20%, lab assignments worth 35% one final research paper or exam worth 45%

Study materials

Selected textbook chapters, journal articles and lab exercises

Further information

Number of students (min-max): 20

Belongs to following study modules