Abstract:
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Binary regression models are commonly used in disciplines such as epidemiology and ecology to make individual-level inference on spatial covariates. In many studies, binary data are shared in a spatially aggregated form to protect privacy. Often, the spatial aggregation process obscures the values of response variables, spatial covariates, and locations of each individual, which makes recovering individual-level inference difficult. We show that applying a series of transformations to a bivariate point process model allows researchers to recover individual-level inference for spatial covariates from spatially aggregated binary data. The series of transformations preserves the convenient interpretation of desired binary regression models that are commonly applied to individual-level data. Using a simulation experiment, we compare the performance of our proposed method applied to spatially aggregated binary data against the performance of standard approaches using the original individual-level data. We illustrate our method by modeling individual-level disease risk in a population using a data set that has been aggregated for privacy protection.
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