Administrative data sources are increasingly being used for spatial analysis and policy formation. For example, ‘welfare to work’ programs have stimulated demand for spatial mismatch studies in which ES-202 employment files are used. The increased resolution gained by geocoding the address records in administrative files can be of enormous research value when the process under study resolves over small distances. Yet the resulting point-referenced data are problematic for inferential analysis. In particular, administrative data typically represent a sample of convenience, thus posing serious validity problems for statistical inference. The authors propose a robust estimation method for spatial pattern inference based on spatially censored data. The performance of the estimator is explored with the aid of simulated data and is also demonstrated with ES-202 data from North Carolina.
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