Evidence map›Paper›PMID 19032791›Full record

ArticleInternational journal of health geographics2008

An effective and efficient approach for manually improving geocoded data.

Daniel W Goldberg, John P Wilson, Craig A Knoblock, Beate Ritz, Myles G Cockburn

Abstract readComparative Study
In one paragraph

Article in International journal of health geographics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
74citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

74 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. The quality of social determinants data in the electronic health record: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2021
    Pooled it
  3. Article
  4. Review
  5. Childhood Cancer Risk in Hispanic Enclaves in California.Journal of immigrant and minority health · 2025
    Article
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14 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Daniel W GoldbergDepartment of Computer Science, University of Southern California, Los Angeles, CA, USA. dwgoldbe@usc.edu
John P Wilson
Craig A Knoblock
Beate Ritz
Myles G Cockburn

Funding

Translational Research Support CoreP30ES007048 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Joseph Leo Wiemels · 1996 to 2026
$46.4M
UCLA CENTER FOR THE STUDY OF PARKINSON'S DISEASEP50NS038367 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CHESSELET, MARIE-FRANCOISE S · 1999 to 2011
$15.3M
Center for Gene-Environment Studies in Parkinson DiseaseU54ES012078 · NIEHS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CHESSELET, MARIE-FRANCOISE S · 2002 to 2006
$7.1M
PARKINSON'S DISEASES SUSCEPTIBILITY GENES AND PESTICIDESR01ES010544 · NIEHS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI RITZ, BEATE R. · 2000 to 2015
$5.7M
NIEHS NIH HHS 5P30 ES07048NIEHS NIH HHS ES10544NIEHS NIH HHS P30 ES007048NIEHS NIH HHS R01 ES010544NIEHS NIH HHS U54 ES012078NIEHS NIH HHS U54ES12078NINDS NIH HHS NS038367NINDS NIH HHS P50 NS038367
6 · The paper itself

Abstract

backgroundThe process of geocoding produces output coordinates of varying degrees of quality. Previous studies have revealed that simply excluding records with low-quality geocodes from analysis can introduce significant bias, but depending on the number and severity of the inaccuracies, their inclusion may also lead to bias. Little quantitative research has been presented on the cost and/or effectiveness of correcting geocodes through manual interactive processes, so the most cost effective methods for improving geocoded data are unclear. The present work investigates the time and effort required to correct geocodes contained in five health-related datasets that represent examples of data commonly used in Health GIS.

resultsGeocode correction was attempted on five health-related datasets containing a total of 22,317 records. The complete processing of these data took 11.4 weeks (427 hours), averaging 69 seconds of processing time per record. Overall, the geocodes associated with 12,280 (55%) of records were successfully improved, taking 95 seconds of processing time per corrected record on average across all five datasets. Geocode correction improved the overall match rate (the number of successful matches out of the total attempted) from 79.3 to 95%. The spatial shift between the location of original successfully matched geocodes and their corrected improved counterparts averaged 9.9 km per corrected record. After geocode correction the number of city and USPS ZIP code accuracy geocodes were reduced from 10,959 and 1,031 to 6,284 and 200, respectively, while the number of building centroid accuracy geocodes increased from 0 to 2,261.

conclusionThe results indicate that manual geocode correction using a web-based interactive approach is a feasible and cost effective method for improving the quality of geocoded data. The level of effort required varies depending on the type of data geocoded. These results can be used to choose between data improvement options (e.g., manual intervention, pseudocoding/geo-imputation, field GPS readings).

Indexed as

Data Interpretation, StatisticalDatabases, FactualGeographic Information SystemsHumansRisk Factors

Identifiers

PMID19032791
PMCPMC2612650

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.