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![]() 272606 Slippery slopes: Stastistical methods for spatially varying associationsTuesday, October 30, 2012
: 2:50 PM - 3:10 PM
Regression frameworks provide the basis for many statistical modeling methods. The assumptions of independent, Gaussian errors, fixed associations across the data set, and constant variance provide a comfortable arrangement leading to many of our favorite tools for variable selection, testing, and inference. With geographically referenced data, we often want to loosen each of these restrictions allowing correlated, non-Gaussian errors, spatially varying associations, and heteroskedasticity. In this seminar, we will provide an overview of recent modeling approaches to these settings including geographically weighted regression (GWR), hierarchical modeling with random effects (random intercepts to induce spatial correlation between observations and random slopes to allow associations to vary across space). We will compare and contrast the assumptions, implementation, and inference enabled by these two approaches. We will also explore the robustness of results and assumptions required for successful implementation. We will compare results on data involving violent crime, illegal drug arrests, and alcohol sales.
Learning Areas:
Biostatistics, economicsEpidemiology Learning Objectives: Keywords: Statistics, Geographic Information Systems
Presenting author's disclosure statement:
Qualified on the content I am responsible for because: I conceived of the study and led the team of collaborators implementing the data analysis and interpreted the results. I am the principal investigator on multiple federally funded grants focusing on the development and application of analytic methods for spatially referenced data.
I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.
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