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Evaluation of geoimputation strategies in a large case study

Overview of attention for article published in International Journal of Health Geographics, July 2018
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Title
Evaluation of geoimputation strategies in a large case study
Published in
International Journal of Health Geographics, July 2018
DOI 10.1186/s12942-018-0151-y
Pubmed ID
Authors

Naci Dilekli, Amanda E. Janitz, Janis E. Campbell, Kirsten M. de Beurs

Abstract

Health data usually has missing or incomplete location information, which impacts the quality of research. Geoimputation methods are used by health professionals to increase the spatial resolution of address information for more accurate analyses. The objective of this study was to evaluate geo-imputation methods with respect to the demographic and spatial characteristics of the data. We evaluated four geoimputation methods for increasing spatial resolution of records with known locational information at a coarse level. In order to test and rigorously evaluate two stochastic and two deterministic strategies, we used the Texas Sex Offender registry database with over 50,000 records with known demographic and coordinate information. We reduced the spatial resolution of each record to a census block group and attempted to recover coordinate information using the four strategies. We rigorously evaluated the results in terms of the error distance between the original coordinates and recovered coordinates by studying the results by demographic sub groups and the characteristics of the underlying geography. We observed that in estimating the actual location of a case, the weighted mean method is the most superior for each demographic group followed by the maximum imputation centroid, the random point in matching sub-geographies and the random point in all sub-geographies methods. Higher accuracies were observed for minority populations because minorities tend to cluster in certain neighborhoods, which makes it easier to impute their location. Results are greatly affected by the population density of the underlying geographies. We observed high accuracies in high population density areas, which often exist within smaller census blocks, which makes the search space smaller. Similarly, mapping geoimputation accuracies in a spatially explicit manner reveals that metropolitan areas yield higher accuracy results. Based on gains in standard error, reduction in mean error and validation results, we conclude that characteristics of the estimated records such as the demographic profile and population density information provide a measure of certainty of geographic imputation.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 14%
Student > Ph. D. Student 3 14%
Other 2 9%
Researcher 2 9%
Lecturer > Senior Lecturer 1 5%
Other 3 14%
Unknown 8 36%
Readers by discipline Count As %
Medicine and Dentistry 4 18%
Biochemistry, Genetics and Molecular Biology 1 5%
Nursing and Health Professions 1 5%
Economics, Econometrics and Finance 1 5%
Computer Science 1 5%
Other 2 9%
Unknown 12 55%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 August 2018.
All research outputs
#20,529,173
of 23,098,660 outputs
Outputs from International Journal of Health Geographics
#552
of 633 outputs
Outputs of similar age
#287,839
of 329,833 outputs
Outputs of similar age from International Journal of Health Geographics
#11
of 14 outputs
Altmetric has tracked 23,098,660 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 633 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.