UC Riverside

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-GWRadapts a novel technique, Stateless-MGWR or S-MGWR, that en-riches the predictive power by allowing distinct bandwidths for individual features of the training data. S-MGWR uses a customized black-box optimization approach for discovering optimal band widths in a fast and efficient way. In addition, A-GWR modularly combines S-MGWR or GWR with versatile models such as random forest models. Moreover, A-GWR enables scalability by operating on flexible partitions of the data that can adapt to the computational budget. Our extensive evaluations on various real and synthetic datasets demonstrate the scalability and accuracy benefits of the proposed techniques over state-of-the-art competitors.