ABSTRACT
In this chapter we tackle the problem of spatial misalignment. That is, with the explosion in spatial data collection, it is more and more the case that different spatial data layers are collected at different scales. For example, we may have one layer at point level, another at point level but at different locations, yet another for one set of areal units and a last over a different set of areal units. Standard GIS software can routinely create overlays and themes with such layers but this is primarily descriptive. Here we seek a formal inferential framework to deal with such misalignment. As a canonical example, consider an environmental justice setting where we seek to assess whether one group is adversely affected by, say, an environmental contaminant, compared with another. So, we might record exposure levels at monitoring stations, we might collect adverse health outcomes at the scale of zip or post codes, and we might learn about population groups at risk through census data at census tract scale. How might we assemble these layers to assess inequities?
