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Estimating the Intensity of a Spatial Point Process from Locations Coarsened by Incomplete Geocoding
Author(s) -
Zimmerman Dale L.
Publication year - 2008
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2007.00870.x
Subject(s) - geocoding , intensity (physics) , point process , process (computing) , point (geometry) , computer science , statistics , environmental science , cartography , geography , mathematics , physics , optics , geometry , operating system
Summary The estimation of spatial intensity is an important inference problem in spatial epidemiologic studies. A standard data assimilation component of these studies is the assignment of a geocode, that is, point‐level spatial coordinates, to the address of each subject in the study population. Unfortunately, when geocoding is performed by the standard automated method of street‐segment matching to a georeferenced road file and subsequent interpolation, it is rarely completely successful. Typically, 10–30% of the addresses in the study population, and even higher percentages in particular subgroups, fail to geocode, potentially leading to a selection bias, called geographic bias, and an inefficient analysis. Missing‐data methods could be considered for analyzing such data; however, because there is almost always some geographic information coarser than a point (e.g., a Zip code) observed for the addresses that fail to geocode, a coarsened‐data analysis is more appropriate. This article develops methodology for estimating spatial intensity from coarsened geocoded data. Both nonparametric (kernel smoothing) and likelihood‐based estimation procedures are considered. Substantial improvements in the estimation quality of coarsened‐data analyses relative to analyses of only the observations that geocode are demonstrated via simulation and an example from a rural health study in Iowa.