PySAL: Integrating Poisson count models and spatial effects for spatial interaction modeling.

Spatial interaction modeling involves the analysis of flows from an origin to a destination either over physical space (i.e., migration) or through abstract space (i.e., telecommunication). There is little to no software avaialble to carry out spatial interaction modeling and the analysis of flow data. This is especially true in the case of open source software and within the python ecosystem. Therefore, a comprehensive python package, which draws on existing PySAL infrastrucutre and extends it, would fill an important gap within the current set of avialable spatial analysis tools.

PySAL currenty provides a rich set of tools for modeling spatial effects within a regression framework, which is typically applied to areal units. While it is possible to extend some of these models to the case of spatial interaction data, new spatial weight structures will be necessary to capture the unique spatial dependence that occurs between a data point that has both an origin and a destination. Furthermore, the existing spatial regression models are specifically designed for continuous data, whereas many spatial interaction phenomena are more properly modeled as counts (i.e., commuting).