Location models can be used to propagate location uncertainty in boundary detection (Jacquez and Jacquez 1999). BoundarySeer can randomize the spatial location of the data to assess how the location uncertainty affects the boundaries and to provide a more accurate analysis. Randomization is a broad term, and it includes many different procedures. The nature of the randomization process can affect the outcome of the analysis. Thus, choosing how to randomize the data is an important step in data preparation and analysis.
Location models provide the basis for spatial randomization. A location model is a probability density function (pdf) that describes the likelihood of each location being sampled during randomization. BoundarySeer chooses spatial coordinates for a new sample location based on the location model specified.
The simplest location model is the polygon model, where all possible locations within a specified area have equal probability of being sampled. Population models are more complex, they vary the pdf by population density, with more populous areas having higher sampling probability. This makes sense for data that describe an incidence rate in areas where people are not uniformly distributed. Currently, only the polygon model is available within BoundarySeer.
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