Spatiotemporal Variograms as Neighborhood Definers

Spatial neighborhood definitions are a consistent source of disagreement among geographic scholars. This research will focus on the implementation and evaluation of spatiotemporal variograms (STVs) as a source of spatial neighborhood definition. STVs show the similarity, measured by semivariance, of spatial events to each other when separated by time and space. Over both time and space, there should exist distances over which pairs of points become "independent" of one another. We seek seeks to answer two questions in relation to STVs and their use as neighborhood definitions: (1) What data and process adjustments are necessary to implement STVs to provide neighborhood search definitions in time and space, and (2) Given that there are many ways to define a neighborhood, STVs may provide a comprehensive method that uses the data themselves to inform the size and scope of neighborhoods, with the added advantage to evaluating both spatial and temporal axes at once. We demonstrate a well-defined neighborhood that accounts for temporal variation as well as spatial and will be a needed addition as the tools incorporating simultaneous spatial and temporal neighborhoods are implemented.