Journal article
Estimating the intensity of a spatial point process from locations coarsened by incomplete geocoding
Biometrics, Vol.64(1), pp.262-270
03/2008
DOI: 10.1111/j.1541-0420.2007.00870.x
PMID: 17680833
Abstract
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.
Details
- Title: Subtitle
- Estimating the intensity of a spatial point process from locations coarsened by incomplete geocoding
- Creators
- Dale L Zimmerman - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Biometrics, Vol.64(1), pp.262-270
- DOI
- 10.1111/j.1541-0420.2007.00870.x
- PMID
- 17680833
- ISSN
- 0006-341X
- eISSN
- 1541-0420
- Language
- English
- Date published
- 03/2008
- Academic Unit
- Statistics and Actuarial Science; Biostatistics
- Record Identifier
- 9984257626702771
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