Literature DB >> 21590677

Evaluation of the positional difference between two common geocoding methods.

Dustin T Duncan1, Marcia C Castro, Jeffrey C Blossom, Gary G Bennett, Steven L Gortmaker.   

Abstract

Geocoding, the process of matching addresses to geographic coordinates, is a necessary first step when using geographical information systems (GIS) technology. However, different geocoding methodologies can result in different geographic coordinates. The objective of this study was to compare the positional (i.e. longitude/latitude) difference between two common geocoding methods, i.e. ArcGIS (Environmental System Research Institute, Redlands, CA, USA) and Batchgeo (freely available online at http://www.batchgeo.com). Address data came from the YMCA-Harvard After School Food and Fitness Project, an obesity prevention intervention involving children aged 5-11 years and their families participating in YMCA-administered, after-school programmes located in four geographically diverse metropolitan areas in the USA. Our analyses include baseline addresses (n = 748) collected from the parents of the children in the after school sites. Addresses were first geocoded to the street level and assigned longitude and latitude coordinates with ArcGIS, version 9.3, then the same addresses were geocoded with Batchgeo. For this analysis, the ArcGIS minimum match score was 80. The resulting geocodes were projected into state plane coordinates, and the difference in longitude and latitude coordinates were calculated in meters between the two methods for all data points in each of the four metropolitan areas. We also quantified the descriptions of the geocoding accuracy provided by Batchgeo with the match scores from ArcGIS. We found a 94% match rate (n = 705), 2% (n = 18) were tied and 3% (n = 25) were unmatched using ArcGIS. Forty-eight addresses (6.4%) were not matched in ArcGIS with a match score ≥80 (therefore only 700 addresses were included in our positional difference analysis). Six hundred thirteen (87.6%) of these addresses had a match score of 100. Batchgeo yielded a 100% match rate for the addresses that ArcGIS geocoded. The median for longitude and latitude coordinates for all the data was just over 25 m. Overall, the range for longitude was 0.04-12,911.8 m, and the range for latitude was 0.02-37,766.6 m. Comparisons show minimal differences in the median and minimum values, while there were slightly larger differences in the maximum values. The majority (>75%) of the geographic differences were within 50 m of each other; mostly <25 m from each other (about 49%). Only about 4% overall were ≥400 m apart. We also found geographic differences in the proportion of addresses that fell within certain meter ranges. The match-score range associated with the Batchgeo accuracy level "approximate" (least accurate) was 84-100 (mean = 92), while the "rooftop" Batchgeo accuracy level (most accurate) delivered a mean of 98.9 but the range was the same. Although future research should compare the positional difference of Batchgeo to criterion measures of longitude/latitude (e.g. with global positioning system measurement), this study suggests that Batchgeo is a good, free-of-charge option to geocode addresses.

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Year:  2011        PMID: 21590677     DOI: 10.4081/gh.2011.179

Source DB:  PubMed          Journal:  Geospat Health        ISSN: 1827-1987            Impact factor:   1.212


  21 in total

1.  Lesbian, gay, bisexual, and transgender hate crimes and suicidality among a population-based sample of sexual-minority adolescents in Boston.

Authors:  Dustin T Duncan; Mark L Hatzenbuehler
Journal:  Am J Public Health       Date:  2013-12-12       Impact factor: 9.308

2.  Assessing the Reliability of Performing Citywide Chronic Disease Surveillance Using Emergency Department Data from Sentinel Hospitals.

Authors:  David C Lee; Jordan L Swartz; Christian A Koziatek; Andrew J Vinson; Jessica K Athens; Stella S Yi
Journal:  Popul Health Manag       Date:  2017-03-24       Impact factor: 2.459

3.  Quantifying spatial misclassification in exposure to noise complaints among low-income housing residents across New York City neighborhoods: a Global Positioning System (GPS) study.

Authors:  Dustin T Duncan; Kosuke Tamura; Seann D Regan; Jessica Athens; Brian Elbel; Julie Meline; Yazan A Al-Ajlouni; Basile Chaix
Journal:  Ann Epidemiol       Date:  2016-10-29       Impact factor: 3.797

4.  The role of neighborhood characteristics in racial/ethnic disparities in type 2 diabetes: results from the Boston Area Community Health (BACH) Survey.

Authors:  Rebecca S Piccolo; Dustin T Duncan; Neil Pearce; John B McKinlay
Journal:  Soc Sci Med       Date:  2015-02-04       Impact factor: 4.634

Review 5.  GIScience and cancer: State of the art and trends for cancer surveillance and epidemiology.

Authors:  Liora Sahar; Stephanie L Foster; Recinda L Sherman; Kevin A Henry; Daniel W Goldberg; David G Stinchcomb; Joseph E Bauer
Journal:  Cancer       Date:  2019-05-30       Impact factor: 6.860

6.  Assessment of spatial mobility among young men who have sex with men within and across high HIV prevalence neighborhoods in New York city: The P18 neighborhood study.

Authors:  Dustin T Duncan; Seann D Regan; Su Hyun Park; William C Goedel; Byoungjun Kim; Staci C Barton; Perry N Halkitis; Basile Chaix
Journal:  Spat Spatiotemporal Epidemiol       Date:  2020-06-21

7.  Location of Pre-exposure Prophylaxis Services Across New York City Neighborhoods: Do Neighborhood Socio-demographic Characteristics and HIV Incidence Matter?

Authors:  Byoungjun Kim; Denton Callander; Ralph DiClemente; Chau Trinh-Shevrin; Lorna E Thorpe; Dustin T Duncan
Journal:  AIDS Behav       Date:  2019-10

8.  Geocoding large population-level administrative datasets at highly resolved spatial scales.

Authors:  Sharon E Edwards; Benjamin Strauss; Marie Lynn Miranda
Journal:  Trans GIS       Date:  2014-08

9.  A multi-stage approach to maximizing geocoding success in a large population-based cohort study through automated and interactive processes.

Authors:  Jennifer S Sonderman; Michael T Mumma; Sarah S Cohen; Elizabeth L Cope; William J Blot; Lisa B Signorello
Journal:  Geospat Health       Date:  2012-05       Impact factor: 1.212

10.  Local spatial clustering in youths' use of tobacco, alcohol, and marijuana in Boston.

Authors:  Dustin T Duncan; Michael Rienti; Martin Kulldorff; Jared Aldstadt; Marcia C Castro; Rochelle Frounfelker; James H Williams; Glorian Sorensen; Renee M Johnson; David Hemenway; David R Williams
Journal:  Am J Drug Alcohol Abuse       Date:  2016-04-20       Impact factor: 3.829

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