A-GWR: Fast and Accurate Geospatial Inference via Augmented Geographically Weighted Regression

Abstract

Geographically Weighted Regression (GWR) is a seminal technique with rich applications in geospatial data analysis. However, it has critical drawbacks in the age of big data in terms of expressiveness, i.e., predictive power, and scalability. This work proposes Augmented GWR (A-GWR) that alleviates these drawbacks. A-GWR adapts a novel technique, Stateless-MGWR or S-MGWR, that enriches the predictive power by allowing different training data features to influence at different spatial scales. S-MGWR uses a customized black-box optimization approach for discovering optimal parameters in a fast and efficient way. In addition, A-GWR modularly combines S-MGWR with versatile models such as random forest models. Moreover, A-GWR enables scalability by operating on partitioned data to adapt to tight computational budgets. Our extensive experiments on various real and synthetic datasets demonstrate the scalability and accuracy benefits of the proposed techniques over state-of-the-art competitors.