Dr. Oshan receives research support from the National Science Foundation

The SMASH team will collaborate with researchers from Arizona State University on an NSF continuing grant award to develop novel multiscale spatial analytics.

Project description: Conventionally, researchers have used statistical models that implicitly assume that the processes generating these outcomes are uniform across locations. Yet, the processes that lead to this variation may vary in different spatial contexts, and statistical approaches are needed that account for this variation. In this study, the researchers develop statistical methods that address correlations between spatially proximate outcomes, as well as open source software to make these approaches freely available to other scholars. Specifically, the researchers develop methods for the statistical modeling of data at multiple scales by advancing multiscale geographically weighted regression (MGWR) methods, which allows the effects of predictor variables to vary based on their spatial context. This approach address known challenges to statistical inferences, specifically that regression models may indicate biased and contrary effects if the models do not adequately account for the scale and structure in the dataset. Additional aims include new methods for adjusting inferences when multiple hypotheses are examined and the implementation of diagnostic tools for examining the robustness of model assumptions. To demonstrate the methods, the researchers apply the modeling approach to empirical datasets with spatial structure, such as mortality during the COVID-19 pandemic, teen pregnancy rates, and voting trends in recent elections.