Introduction to Spatial Regression Analysis (Boulder, CO)
Spatially-referenced data add important contextual and locational information to the social and behavioral sciences, such as 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. This 5-day course introduces the spatial-analytic framework, and explores the range of issues that generally must be dealt with when analyzing spatial data. The role of spatial autocorrelation in spatial data sets is a central 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 should it inform the specification and estimation of regression models. Specific modeling techniques include: indices of spatial autocorrelation (Moran's I, Geary's C, LISA), spatial regression models (SAR and SARAR), and geographically weighted regression (GWR).
The course is structured around a combined lecture format (mornings) and computing lab exercises (afternoons). The focus of the course is on spatial statistical analysis, not Geographic Information Systems (GIS). Hands-on application of statistical methods in afternoon lab sessions will enable participants to pursue a broad range of social and behavioral science research topics. Software emphasis will be given to GeoDa and R for exploratory spatial data analysis and modeling. Some acquaintance with this software is helpful but is not a prerequisite. Detailed R code will be provided and discussed in labs, and we will lean how to interpret, visualize and map model output.
Prerequisites for maximizing learning in this course are a solid grounding in standard multivariate regression techniques and a minimal level of comfort with matrix notation and algebra.
Fee: Members = $1500; Non-members = $3000