Quantifying geocode location error using GIS methods

Strickland, Matthew; Siffel, Csaba; Gardner, Bennett; Berzen, Alissa K.; Correa, Adolfo

doi:10.1186/1476-069x-6-10

Cited by 51 publications

(53 citation statements)

References 21 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: 91%

“…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: 91%

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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show abstract

“…meters, and larger in rural areas than in urban areas (Bonner et al 2003;Cayo and Talbot 2003;Morton et al 2007;Schootman et al 2007;Strickland et al 2007;Ward et al 2005;Whitsel et al 2004;Whitsel et al, 2006). Coordinates assigned by street-level geocoding are more likely to be close to their true location than those assigned by postal geocoding.…”

Section: This Coordinate Assignment Methods Is Called Street-level Geomentioning

confidence: 99%

“…Ideally, this process places addresses into the block where they really are. Investigations of the accuracy of assignment to census geographies have reported that 35% of addresses are placed in the wrong blocks, and five percent in the wrong tract (Krieger et al 2001;Morton et al 2007;Ratcliffe 2001;Schootman et al 2007;Strickland et al 2007). …”

Section: This Coordinate Assignment Methods Is Called Street-level Geomentioning

confidence: 99%

Geocoding to Create Survey Frames

Eckman

English

2012

Surv Pract

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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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Quantifying geocode location error using GIS methods

Cited by 51 publications

References 21 publications

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

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

Geocoding to Create Survey Frames

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

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