An understanding of the spatial relationships in sociological and epidemiological applications is an important tool in the analysis of urban data. While point level data (e.g. observations at a given latitude/longitude) provide the most detail about spatial phenomenon, spatial data aggregated to the level of relevant municipal regions is easily accessible and can provide insights at a level useful for policy decisions for governments and communities. This work identifies two areas of focus in the analysis of spatially aggregated data. First, a new specification for dependence in spatial regression models for aggregated data using the Hausdorff distance and extended Hausdorff distance is introduced. The new dependence structure is shown to account for the shape and orientation of the irregular and disconnected regions often encountered in practice and evaluated in the context of model performance as well as a real data example. An R package compatible with existing spatial packages which implements the construction of spatial weight matrices generated using the (extended) Hausdorff distance is provided along with a vignette illustrating its use on real data. Second, the idea of a spatial case-crossover model is explored in the context of connection to existing spatial methods. A method for including spatial dependence in a spatio-temporal case-crossover model is also explored.