Positional Accuracy and Geographic Bias of Four Methods of Geocoding in Epidemiologic Research

Schootman, Mario; Sterling, David A.; Struthers, James; Yan, Yan; Laboube, Ted; Emo, Brett; Higgs, Gary

doi:10.1016/j.annepidem.2006.10.015

Cited by 68 publications

(76 citation statements)

References 24 publications

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“…Positional error ranged from negligible (5 meters Euclidean distance) to exceptional (almost 20,000 meters, in the case of a golf course) and was larger than distances observed in residential address geocoding validation studies (11)(12)(13)(14)(15). However, this study examined physical activity facilities, which are often large in size and could have ambiguous point locations (e.g., golf courses, which cover a relatively large geographic area), rather than residences.…”

Section: Positional Accuracymentioning

confidence: 99%

“…Despite the recognition of these potential errors and considerable growth in the use of GIS technology in health research, existing validation studies generally focus on the positional error of geocoded residential addresses (11)(12)(13)(14)(15)(16). Count and attribute error in databases of community resources, as opposed to residential addresses that are managed in GIS programs have not been assessed.…”

Section: Introductionmentioning

confidence: 99%

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Validation of a GIS Facilities Database: Quantification and Implications of Error

Boone

Gordon‐Larsen

Stewart

et al. 2008

Annals of Epidemiology

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Purpose-To validate a commercial database of community-level physical activity facilities that can be used in future research examining associations between access to physical activity facilities and individual-level physical activity and obesity.Methods-Physical activity facility characteristics and locations obtained from a commercial database were compared to a field census conducted in 80 Census block groups within two U.S. communities. Agreement statistics, agreement of administratively-defined neighborhoods, and distance between locations were used to quantify count, attribute, and positional error.Results-There was moderate agreement (concordance: non-urban: 0.39; urban: 0.46) of presence of any physical activity facility and poor to moderate agreement (kappa range: 0.14 to 0.76) of physical activity facility type. The mean Euclidean distance between commercial database versus field census locations was 757 and 35 meters in the non-urban and urban communities, respectively. However, 94 and 100% of non-urban and urban physical activity facilities, respectively, fell into the same 5-digit ZIP code, dropping to 92 and 98% in the same block group and 71% along the same street.Conclusions-Our findings suggest that the commercial database of physical activity facilities may contain appreciable error, but patterns of error suggest that built environment-health associations are likely biased downward.

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Section: Positional Accuracymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of a GIS Facilities Database: Quantification and Implications of Error

Boone

Gordon‐Larsen

Stewart

et al. 2008

Annals of Epidemiology

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“…For example, studies used a global positioning systems (GPS) unit that obtains precise geodetic locations of addresses from satellites (Ward et al, 2005;Zhan et al, 2006;Schootman et al, 2007). This method is time-consuming and may not be feasible in the absence of a field survey.…”

Section: States Of America (Usa) Researchers In the University Of Somentioning

confidence: 99%

“…This method is time-consuming and may not be feasible in the absence of a field survey. One can also utilise aerial photography as the "gold" standard, which has been less frequently used than GPS measurements (Schootman et al, 2007). For example, Cayo and Talbot (2003) defined the "true" location of each address as the point that was the visual centre of the house using 1 m resolution digitally enhanced aerial orthoimagery with a horizontal accuracy of 10 m. The determined points were manually placed in the centre of the structure.…”

Section: States Of America (Usa) Researchers In the University Of Somentioning

confidence: 99%

A multifaceted comparison of ArcGIS and MapMarker for automated geocoding

2012

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Abstract. 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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“…An issue being recognized more currently is that of geocoding accuracy. Zandbergen (2007) found a median street coding error of 41 m, with a 99th percentile error of 273 m; Schootman et al (2007) report errors of 51 m or more; Strickland et al (2007) report errors ''less than 100 m''; and Zimmerman et al (2007) report a median error of 44 m. These findings shed doubt on epidemiological findings based on residential proximity to busy streets of the order of 50 m, as discussed above, especially given the exponential nature of concentration decay downwind of busy roads. It seems likely that more reliable results might be obtained using continuous rather than categorical measures of residential proximity, such as in Tonne et al (2007).…”

Section: Problems Of Confounding Collinearity and Measurement Errormentioning

confidence: 99%

On exposure and response relationships for health effects associated with exposure to vehicular traffic

Lipfert

Wyzga

2008

J Expo Sci Environ Epidemiol

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This work examines various metrics and models that have been used to estimate long-term health effects of exposure to vehicular traffic. Such health impacts may include effects of air pollution due to emissions of combustion products and from vehicle or roadway wear, of noise, stress, or from socioeconomic effects associated with preferred residential locations. Both categorical and continuous exposure metrics are considered, typically for distances between residences and roadways, or for traffic density or intensity. It appears that continuous measures of exposure tend to yield lower risk estimates that are also more precise than categorical measures based on arbitrary criteria. The selection of appropriate exposure increments to characterize relative risks is also important in comparing pollutants and other agents. Confounding and surrogate variables are also important issues, since studies of traffic proximity or density cannot identify the specific agents related to traffic exposures that might be responsible for the various health endpoints that have been implicated. Studies based on ambient air quality measurements are necessarily restricted to species for which data are available, some of which may be serving as markers for the actual agents of harm. Studies based on modeled air quality are limited by the accuracy of mobile source emission inventories, which may not include poorly maintained (high emitting) vehicles. Additional exposure modeling errors may result from precision limitations of geocoding methods. Studies of the health effects of traffic are progressing from establishing the existence of relationships to describing them in more detail, but effective remedies or control strategies have generally not yet been proposed in the context of these epidemiological studies. Resolution of these dose-response uncertainties is important for the development of effective public health strategies for the future.

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Positional Accuracy and Geographic Bias of Four Methods of Geocoding in Epidemiologic Research

Cited by 68 publications

References 24 publications

Validation of a GIS Facilities Database: Quantification and Implications of Error

Validation of a GIS Facilities Database: Quantification and Implications of Error

A multifaceted comparison of ArcGIS and MapMarker for automated geocoding

On exposure and response relationships for health effects associated with exposure to vehicular traffic

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