A multifaceted comparison of ArcGIS and MapMarker for automated geocoding
Open accessAbstract
Geocoding is increasingly being used for public health surveillance and spatial epidemiology studies. Public health departments in the United States of America (USA) often use this approach to investigate disease outbreaks and clusters or assign health records to appropriate geographic units. We evaluated two commonly used geocoding software packages, ArcGIS and MapMarker, for automated geocoding of a large number of residential addresses from health administrative data in New York State, USA to better understand their features, performance and limitations. The comparison was based on three metrics of evaluation: completeness (or match rate), geocode similarity and positional accuracy. Of the 551,798 input addresses, 318,302 (57.7%) were geocoded by MapMarker and 420,813 (76.3%) by the ArcGIS composite address locator. High similarity between the geocodes assigned by the two methods was found, especially in suburban and urban areas. Among addresses with a distance of greater than 100 m between the geocodes assigned by the two packages, the point assigned by ArcGIS was closer to the associated parcel centroid ("true" location) compared with that assigned by MapMarker. In addition, the composite address locator in ArcGIS allows users to fully utilise available reference data, which consequently results in better geocoding results. However, the positional differences found were minimal, and a large majority of addresses were placed on the same locations by both geocoding packages. Using both methods and combining the results can maximise match rates and save the time needed for manual geocoding.
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Articles about geocoding patient address data evaluated the ability of specific geocoding tools, such as ArcGIS, to increase data quality by improving match rates or positional accuracy.
“Articles about geocoding patient address data, particularly, evaluated the ability of specific geocoding tools, such as ArcGIS, to increase data quality by improving match rates or positional accuracy.54,73–75”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [73]
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BackgroundMethodInfluentialPositional Accuracy of Two Methods of Geocoding
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- Authors
- Kumar, S., Liu, M., & Hwang, S.-A.
- Affiliations
- New York State Department of Health (US)
- Corresponding author
- Kumar, Sanjaya
- Liu, Ming
- Hwang, Syni-An
- ORCID
- Liu, Ming
- Venue
- Geospatial health
- Publisher
- PAGEPress Publications
- Published
- 2012-11-01
- Volume
- 7
- Issue
- 1
- Pages
- 145
- ISSN
- 1827-1987 (ISSN-L)
- 1970-7096
- OpenAlex
- W2159565817
- Cited by
- 12
- References
- 16
- Citations by year
- 2024: 1 · 2023: 1 · 2021: 2 · 2017: 1 · 2016: 1 · 2015: 3 · 2014: 3
- FWCI
- 11.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- Geospatial health (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- CNR Solar (Scientific Open-access Literature Archive and Repository) (Consiglio Nazionale delle Ricerche) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Centers for Disease Control and Prevention (5U38EH000184)
- Topics
- Social Sciences › Geography, Planning and Development › Geographic Information Systems Studies
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Keywords
- ArcGIS, administrative health data, positional accuracy, disease surveillance, spatial epidemiology, New York State
- MeSH terms
- California, Disease Outbreaks, Epidemiologic Methods, Humans, New York, Population Surveillance, Public Health, Software, United States, Cluster Analysis, Geographic Mapping
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