This line of inquiry investigates how scale can be better captured in local multivariate statistical models, such as multiscale geographically weighted regression (MGWR). This work entails developing algorithms for both the inference of spatial relationships and the prediction of spatial observations. It also seeks to scale these methods, which are frequently computationally cumbersome, so that they can be used at higher resolutions (i.e., within cities) and for larger scopes (i.e., national and global). Ongoing research includes an application to modeling obesity rates and developing an open source Python implementation of MGWR.