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Gerard Rushton - One of the best experts on this subject based on the ideXlab platform.
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Geocoding accuracy and the recovery of relationships between environmental exposures and health
International Journal of Health Geographics, 2008Co-Authors: Soumya Mazumdar, Dale L Zimmerman, Gerard Rushton, Brian J Smith, Kelley J DonhamAbstract:This research develops methods for determining the effect of Geocoding quality on relationships between environmental exposures and health. The likelihood of detecting an existing relationship – statistical power – between measures of environmental exposures and health depends not only on the strength of the relationship but also on the level of positional accuracy and completeness of the geocodes from which the measures of environmental exposure are made. This paper summarizes the results of simulation studies conducted to examine the impact of inaccuracies of geocoded addresses generated by three types of Geocoding processes: a) addresses located on orthophoto maps, b) addresses matched to TIGER files (U.S Census or their derivative street files); and, c) addresses from E-911 geocodes (developed by local authorities for emergency dispatch purposes). The simulated odds of disease using exposures modelled from the highest quality geocodes could be sufficiently recovered using other, more commonly used, Geocoding processes such as TIGER and E-911; however, the strength of the odds relationship between disease exposures modelled at geocodes generally declined with decreasing Geocoding accuracy. Although these specific results cannot be generalized to new situations, the methods used to determine the sensitivity of results can be used in new situations. Estimated measures of positional accuracy must be used in the interpretation of results of analyses that investigate relationships between health outcomes and exposures measured at residential locations. Analyses similar to those employed in this paper can be used to validate interpretation of results from empirical analyses that use geocoded locations with estimated measures of positional accuracy.
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Geocoding health data the use of geographic codes in cancer prevention and control research and practice
2007Co-Authors: Barry R Greene, Gerard Rushton, Michele M West, Josephine Gittler, Marc P Armstrong, Claire E Pavlik, Dale L ZimmermanAbstract:Introduction Geocoding Methods, Materials, and First Steps toward a Geocoding Error Budget M.P. Armstrong and C. Tiwari Using ZIP codes as Geocodes in Cancer Research K.M.M. Beyer, A.F. Schultz, and Z. Chen Producing Spatially Continuous Prostate Cancer Maps with Different Geocodes and Spatial Filter Methods G. Rushton, Q. Cai, and Z. Chen The Science and Art of Geocoding: Tips for Improving Match Rates and Handling Unmatched Cases in Analysis F. Boscoe Geocoding Practices in Cancer Registries T. Abe and D. Stinchcomb Alternative Techniques for Masking Geographic Detail to Protect Privacy D.L. Zimmerman, M.P. Armstrong, and Gerard Rushton Preserving Privacy: Deidentifying Data by Applying a Random Perturbation Spatial Mask Z. Chen, G. Rushton, and G. Smith Spatial Statistical Analysis of Point- and Area-Reference Public Health Data L.A. Waller Statistical methods for Incompletely and Incorrectly Geocoded Cancer Data D. L. Zimmerman Using Geocodes to estimate Distances and geographic Accessibility for Cancer Prevention and Control M. Armstrong, B. Greene, and G. Rushton Cancer Registry Data and Geocoding: Privacy, Confidentiality, and Security Issues J. Gittler Conclusions Appendix: Cancer Reporting and Registry Statutes and Regulations J. Gittler
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Modeling the probability distribution of positional errors incurred by residential address Geocoding
International Journal of Health Geographics, 2007Co-Authors: Dale L Zimmerman, Xiangming Fang, Soumya Mazumdar, Gerard RushtonAbstract:Background The assignment of a point-level geocode to subjects' residences is an important data assimilation component of many geographic public health studies. Often, these assignments are made by a method known as automated Geocoding, which attempts to match each subject's address to an address-ranged street segment georeferenced within a streetline database and then interpolate the position of the address along that segment. Unfortunately, this process results in positional errors. Our study sought to model the probability distribution of positional errors associated with automated Geocoding and E911 Geocoding. Results Positional errors were determined for 1423 rural addresses in Carroll County, Iowa as the vector difference between each 100%-matched automated geocode and its true location as determined by orthophoto and parcel information. Errors were also determined for 1449 60%-matched geocodes and 2354 E911 geocodes. Huge (> 15 km) outliers occurred among the 60%-matched Geocoding errors; outliers occurred for the other two types of Geocoding errors also but were much smaller. E911 Geocoding was more accurate (median error length = 44 m) than 100%-matched automated Geocoding (median error length = 168 m). The empirical distributions of positional errors associated with 100%-matched automated Geocoding and E911 Geocoding exhibited a distinctive Greek-cross shape and had many other interesting features that were not capable of being fitted adequately by a single bivariate normal or t distribution. However, mixtures of t distributions with two or three components fit the errors very well. Conclusion Mixtures of bivariate t distributions with few components appear to be flexible enough to fit many positional error datasets associated with Geocoding, yet parsimonious enough to be feasible for nascent applications of measurement-error methodology to spatial epidemiology.
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modeling the probability distribution of positional errors incurred by residential address Geocoding
International Journal of Health Geographics, 2007Co-Authors: Dale L Zimmerman, Xiangming Fang, Soumya Mazumdar, Gerard RushtonAbstract:Background The assignment of a point-level geocode to subjects' residences is an important data assimilation component of many geographic public health studies. Often, these assignments are made by a method known as automated Geocoding, which attempts to match each subject's address to an address-ranged street segment georeferenced within a streetline database and then interpolate the position of the address along that segment. Unfortunately, this process results in positional errors. Our study sought to model the probability distribution of positional errors associated with automated Geocoding and E911 Geocoding.
Dale L Zimmerman - One of the best experts on this subject based on the ideXlab platform.
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Spatial clustering of the failure to geocode and its implications for the detection of disease clustering.
Statistics in Medicine, 2008Co-Authors: Dale L Zimmerman, Xiangming Fang, Soumya MazumdarAbstract:Geocoding a study population as completely as possible is an important data assimilation component of many spatial epidemiologic studies. Unfortunately, complete Geocoding is rare in practice. The failure of a substantial proportion of study subjects' addresses to geocode has consequences for spatial analyses, some of which are not yet fully understood. This article explicitly demonstrates that the failure to geocode can be spatially clustered, and it investigates the implications of this for the detection of disease clustering. A data set of more than 9000 ground-truthed addresses from Carroll County, Iowa, which was geocoded via a standard address matching and street interpolation algorithm, is used for this purpose. Through simulation of disease processes at these addresses, the authors show that spatial clustering of Geocoding failure has no effect on the marginal power to detect spatial disease clustering if the likelihood of disease is independent of the failure to geocode, but that power is substantially reduced if disease likelihood and Geocoding failure are positively associated. Copyright © 2008 John Wiley & Sons, Ltd.
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Geocoding accuracy and the recovery of relationships between environmental exposures and health
International Journal of Health Geographics, 2008Co-Authors: Soumya Mazumdar, Dale L Zimmerman, Gerard Rushton, Brian J Smith, Kelley J DonhamAbstract:This research develops methods for determining the effect of Geocoding quality on relationships between environmental exposures and health. The likelihood of detecting an existing relationship – statistical power – between measures of environmental exposures and health depends not only on the strength of the relationship but also on the level of positional accuracy and completeness of the geocodes from which the measures of environmental exposure are made. This paper summarizes the results of simulation studies conducted to examine the impact of inaccuracies of geocoded addresses generated by three types of Geocoding processes: a) addresses located on orthophoto maps, b) addresses matched to TIGER files (U.S Census or their derivative street files); and, c) addresses from E-911 geocodes (developed by local authorities for emergency dispatch purposes). The simulated odds of disease using exposures modelled from the highest quality geocodes could be sufficiently recovered using other, more commonly used, Geocoding processes such as TIGER and E-911; however, the strength of the odds relationship between disease exposures modelled at geocodes generally declined with decreasing Geocoding accuracy. Although these specific results cannot be generalized to new situations, the methods used to determine the sensitivity of results can be used in new situations. Estimated measures of positional accuracy must be used in the interpretation of results of analyses that investigate relationships between health outcomes and exposures measured at residential locations. Analyses similar to those employed in this paper can be used to validate interpretation of results from empirical analyses that use geocoded locations with estimated measures of positional accuracy.
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Geocoding health data the use of geographic codes in cancer prevention and control research and practice
2007Co-Authors: Barry R Greene, Gerard Rushton, Michele M West, Josephine Gittler, Marc P Armstrong, Claire E Pavlik, Dale L ZimmermanAbstract:Introduction Geocoding Methods, Materials, and First Steps toward a Geocoding Error Budget M.P. Armstrong and C. Tiwari Using ZIP codes as Geocodes in Cancer Research K.M.M. Beyer, A.F. Schultz, and Z. Chen Producing Spatially Continuous Prostate Cancer Maps with Different Geocodes and Spatial Filter Methods G. Rushton, Q. Cai, and Z. Chen The Science and Art of Geocoding: Tips for Improving Match Rates and Handling Unmatched Cases in Analysis F. Boscoe Geocoding Practices in Cancer Registries T. Abe and D. Stinchcomb Alternative Techniques for Masking Geographic Detail to Protect Privacy D.L. Zimmerman, M.P. Armstrong, and Gerard Rushton Preserving Privacy: Deidentifying Data by Applying a Random Perturbation Spatial Mask Z. Chen, G. Rushton, and G. Smith Spatial Statistical Analysis of Point- and Area-Reference Public Health Data L.A. Waller Statistical methods for Incompletely and Incorrectly Geocoded Cancer Data D. L. Zimmerman Using Geocodes to estimate Distances and geographic Accessibility for Cancer Prevention and Control M. Armstrong, B. Greene, and G. Rushton Cancer Registry Data and Geocoding: Privacy, Confidentiality, and Security Issues J. Gittler Conclusions Appendix: Cancer Reporting and Registry Statutes and Regulations J. Gittler
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Modeling the probability distribution of positional errors incurred by residential address Geocoding
International Journal of Health Geographics, 2007Co-Authors: Dale L Zimmerman, Xiangming Fang, Soumya Mazumdar, Gerard RushtonAbstract:Background The assignment of a point-level geocode to subjects' residences is an important data assimilation component of many geographic public health studies. Often, these assignments are made by a method known as automated Geocoding, which attempts to match each subject's address to an address-ranged street segment georeferenced within a streetline database and then interpolate the position of the address along that segment. Unfortunately, this process results in positional errors. Our study sought to model the probability distribution of positional errors associated with automated Geocoding and E911 Geocoding. Results Positional errors were determined for 1423 rural addresses in Carroll County, Iowa as the vector difference between each 100%-matched automated geocode and its true location as determined by orthophoto and parcel information. Errors were also determined for 1449 60%-matched geocodes and 2354 E911 geocodes. Huge (> 15 km) outliers occurred among the 60%-matched Geocoding errors; outliers occurred for the other two types of Geocoding errors also but were much smaller. E911 Geocoding was more accurate (median error length = 44 m) than 100%-matched automated Geocoding (median error length = 168 m). The empirical distributions of positional errors associated with 100%-matched automated Geocoding and E911 Geocoding exhibited a distinctive Greek-cross shape and had many other interesting features that were not capable of being fitted adequately by a single bivariate normal or t distribution. However, mixtures of t distributions with two or three components fit the errors very well. Conclusion Mixtures of bivariate t distributions with few components appear to be flexible enough to fit many positional error datasets associated with Geocoding, yet parsimonious enough to be feasible for nascent applications of measurement-error methodology to spatial epidemiology.
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modeling the probability distribution of positional errors incurred by residential address Geocoding
International Journal of Health Geographics, 2007Co-Authors: Dale L Zimmerman, Xiangming Fang, Soumya Mazumdar, Gerard RushtonAbstract:Background The assignment of a point-level geocode to subjects' residences is an important data assimilation component of many geographic public health studies. Often, these assignments are made by a method known as automated Geocoding, which attempts to match each subject's address to an address-ranged street segment georeferenced within a streetline database and then interpolate the position of the address along that segment. Unfortunately, this process results in positional errors. Our study sought to model the probability distribution of positional errors associated with automated Geocoding and E911 Geocoding.
Paul A. Zandbergen - One of the best experts on this subject based on the ideXlab platform.
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Geocoding quality and implications for spatial analysis
Geography Compass, 2009Co-Authors: Paul A. ZandbergenAbstract:Many spatial analysis techniques rely on the ability to geocode individual locations based on addresses or other descriptive information. The quality of Geocoding and its effect on spatial analysis have received some attention in the literature, in particular in the field of health. This article reviews the foundation of Geocoding and presents a framework for evaluating Geocoding quality. Errors introduced by street gecoding include incompleteness, positional error, and incorrect assignment to geographic units. A review of empirical studies suggests that these errors are neither small nor random in nature and that substantial bias may be introduced in spatial analysis that employs the results of Geocoding. Several alternatives have also emerged, including the use of address points and parcels, and these are gradually becoming more widely used. Several areas for future research on Geocoding have been identified: (i) refinements of address data models to incorporate complex addressing situations; (ii) development of error propagation techniques to determine the level of Geocoding quality required for a particular analysis scenario; (iii) development of measures of reliability for Geocoding results; (iv) comparative analysis of Geocoding quality across different jurisdictions; and (v) validation of online Geocoding services and volunteered geographic information.
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Geocoding accuracy considerations in determining residency restrictions for sex offenders
Criminal Justice Policy Review, 2009Co-Authors: Paul A. Zandbergen, Timothy C HartAbstract:Geocoding is commonly employed to determine the location of addresses for use in spatial analysis, including the establishment of residency restriction zones for sex offenders. Street Geocoding has known limitations in terms of positional accuracy, which may adversely affect spatial analytic methods. A case study on sex offenders in Orange County, Florida, is used to characterize the positional accuracy of street Geocoding and its impact on spatial analysis. Positional accuracy of street geocoded locations of sex offenders' residences, schools, and day care facilities was determined using Geographic Information Systems (GIS) by measuring the distance to the correct property boundaries. Results show that positional errors in street Geocoding are substantial and may bias conclusions drawn from proximity analysis. Findings strongly suggest that street Geocoding is not appropriate for assessing residency restriction violations for sex offenders. These findings have important implications for criminal justice policies related to residency restrictions for sex offenders.
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a comparison of address point parcel and street Geocoding techniques
Computers Environment and Urban Systems, 2008Co-Authors: Paul A. ZandbergenAbstract:The widespread availability of powerful Geocoding tools in commercial GIS software and the interest in spatial analysis at the individual level have made address Geocoding a widely employed technique in many different fields. The most commonly used approach to Geocoding employs a street network data model, in which addresses are placed along a street segment based on a linear interpolation of the location of the street number within an address range. Several alternatives have emerged, including the use of address points and parcels, but these have not received widespread attention in the literature. This paper reviews the foundation of Geocoding and presents a framework for evaluating Geocoding quality based on completeness, positional accuracy and repeatability. Geocoding quality was compared using three address data models: address points, parcels and street networks. The empirical evaluation employed a variety of different address databases for three different Counties in Florida. Results indicate that address point Geocoding produces Geocoding match rates similar to those observed for street network Geocoding. Parcel Geocoding generally produces much lower match rates, in particular for commercial and multi-family residential addresses. Variability in Geocoding match rates between address databases and between geographic areas is substantial, reinforcing the need to strengthen the development of standards for address reference data and improved address data entry validation procedures.
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A comparison of address point, parcel and street Geocoding techniques
Computers Environment and Urban Systems, 2008Co-Authors: Paul A. ZandbergenAbstract:The widespread availability of powerful Geocoding tools in commercial GIS software and the interest in spatial analysis at the individual level have made address Geocoding a widely employed technique in many different fields. The most commonly used approach to Geocoding employs a street network data model, in which addresses are placed along a street segment based on a linear interpolation of the location of the street number within an address range. Several alternatives have emerged, including the use of address points and parcels, but these have not received widespread attention in the literature. This paper reviews the foundation of Geocoding and presents a framework for evaluating Geocoding quality based on completeness, positional accuracy and repeatability. Geocoding quality was compared using three address data models: address points, parcels and street networks. The empirical evaluation employed a variety of different address databases for three different Counties in Florida. Results indicate that address point Geocoding produces Geocoding match rates similar to those observed for street network Geocoding. Parcel Geocoding generally produces much lower match rates, in particular for commercial and multi-family residential addresses. Variability in Geocoding match rates between address databases and between geographic areas is substantial, reinforcing the need to strengthen the development of standards for address reference data and improved address data entry validation procedures. ?? 2007 Elsevier Ltd. All rights reserved.
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influence of Geocoding quality on environmental exposure assessment of children living near high traffic roads
BMC Public Health, 2007Co-Authors: Paul A. ZandbergenAbstract:Background The widespread availability of powerful Geocoding tools in commercial GIS software and the interest in spatial analysis at the individual level have made address Geocoding a widely employed technique in epidemiological studies. This study determined the effect of the positional error in street Geocoding on the analysis of traffic-related air pollution on children.
Soumya Mazumdar - One of the best experts on this subject based on the ideXlab platform.
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Spatial clustering of the failure to geocode and its implications for the detection of disease clustering.
Statistics in Medicine, 2008Co-Authors: Dale L Zimmerman, Xiangming Fang, Soumya MazumdarAbstract:Geocoding a study population as completely as possible is an important data assimilation component of many spatial epidemiologic studies. Unfortunately, complete Geocoding is rare in practice. The failure of a substantial proportion of study subjects' addresses to geocode has consequences for spatial analyses, some of which are not yet fully understood. This article explicitly demonstrates that the failure to geocode can be spatially clustered, and it investigates the implications of this for the detection of disease clustering. A data set of more than 9000 ground-truthed addresses from Carroll County, Iowa, which was geocoded via a standard address matching and street interpolation algorithm, is used for this purpose. Through simulation of disease processes at these addresses, the authors show that spatial clustering of Geocoding failure has no effect on the marginal power to detect spatial disease clustering if the likelihood of disease is independent of the failure to geocode, but that power is substantially reduced if disease likelihood and Geocoding failure are positively associated. Copyright © 2008 John Wiley & Sons, Ltd.
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Geocoding accuracy and the recovery of relationships between environmental exposures and health
International Journal of Health Geographics, 2008Co-Authors: Soumya Mazumdar, Dale L Zimmerman, Gerard Rushton, Brian J Smith, Kelley J DonhamAbstract:This research develops methods for determining the effect of Geocoding quality on relationships between environmental exposures and health. The likelihood of detecting an existing relationship – statistical power – between measures of environmental exposures and health depends not only on the strength of the relationship but also on the level of positional accuracy and completeness of the geocodes from which the measures of environmental exposure are made. This paper summarizes the results of simulation studies conducted to examine the impact of inaccuracies of geocoded addresses generated by three types of Geocoding processes: a) addresses located on orthophoto maps, b) addresses matched to TIGER files (U.S Census or their derivative street files); and, c) addresses from E-911 geocodes (developed by local authorities for emergency dispatch purposes). The simulated odds of disease using exposures modelled from the highest quality geocodes could be sufficiently recovered using other, more commonly used, Geocoding processes such as TIGER and E-911; however, the strength of the odds relationship between disease exposures modelled at geocodes generally declined with decreasing Geocoding accuracy. Although these specific results cannot be generalized to new situations, the methods used to determine the sensitivity of results can be used in new situations. Estimated measures of positional accuracy must be used in the interpretation of results of analyses that investigate relationships between health outcomes and exposures measured at residential locations. Analyses similar to those employed in this paper can be used to validate interpretation of results from empirical analyses that use geocoded locations with estimated measures of positional accuracy.
-
Modeling the probability distribution of positional errors incurred by residential address Geocoding
International Journal of Health Geographics, 2007Co-Authors: Dale L Zimmerman, Xiangming Fang, Soumya Mazumdar, Gerard RushtonAbstract:Background The assignment of a point-level geocode to subjects' residences is an important data assimilation component of many geographic public health studies. Often, these assignments are made by a method known as automated Geocoding, which attempts to match each subject's address to an address-ranged street segment georeferenced within a streetline database and then interpolate the position of the address along that segment. Unfortunately, this process results in positional errors. Our study sought to model the probability distribution of positional errors associated with automated Geocoding and E911 Geocoding. Results Positional errors were determined for 1423 rural addresses in Carroll County, Iowa as the vector difference between each 100%-matched automated geocode and its true location as determined by orthophoto and parcel information. Errors were also determined for 1449 60%-matched geocodes and 2354 E911 geocodes. Huge (> 15 km) outliers occurred among the 60%-matched Geocoding errors; outliers occurred for the other two types of Geocoding errors also but were much smaller. E911 Geocoding was more accurate (median error length = 44 m) than 100%-matched automated Geocoding (median error length = 168 m). The empirical distributions of positional errors associated with 100%-matched automated Geocoding and E911 Geocoding exhibited a distinctive Greek-cross shape and had many other interesting features that were not capable of being fitted adequately by a single bivariate normal or t distribution. However, mixtures of t distributions with two or three components fit the errors very well. Conclusion Mixtures of bivariate t distributions with few components appear to be flexible enough to fit many positional error datasets associated with Geocoding, yet parsimonious enough to be feasible for nascent applications of measurement-error methodology to spatial epidemiology.
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modeling the probability distribution of positional errors incurred by residential address Geocoding
International Journal of Health Geographics, 2007Co-Authors: Dale L Zimmerman, Xiangming Fang, Soumya Mazumdar, Gerard RushtonAbstract:Background The assignment of a point-level geocode to subjects' residences is an important data assimilation component of many geographic public health studies. Often, these assignments are made by a method known as automated Geocoding, which attempts to match each subject's address to an address-ranged street segment georeferenced within a streetline database and then interpolate the position of the address along that segment. Unfortunately, this process results in positional errors. Our study sought to model the probability distribution of positional errors associated with automated Geocoding and E911 Geocoding.
Thomas O. Talbot - One of the best experts on this subject based on the ideXlab platform.
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positional error in automated Geocoding of residential addresses
International Journal of Health Geographics, 2003Co-Authors: Michael R. Cayo, Thomas O. TalbotAbstract:Background Public health applications using geographic information system (GIS) technology are steadily increasing. Many of these rely on the ability to locate where people live with respect to areas of exposure from environmental contaminants. Automated Geocoding is a method used to assign geographic coordinates to an individual based on their street address. This method often relies on street centerline files as a geographic reference. Such a process introduces positional error in the geocoded point. Our study evaluated the positional error caused during automated Geocoding of residential addresses and how this error varies between population densities. We also evaluated an alternative method of Geocoding using residential property parcel data.
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Positional error in automated Geocoding of residential addresses
International Journal of Health Geographics, 2003Co-Authors: Michael R. Cayo, Thomas O. TalbotAbstract:Background: Public health applications using geographic information system (GIS) technology are steadily increasing. Many of these rely on the ability to locate where people live with respect to areas of exposure from environmental contaminants. Automated Geocoding is a method used to assign geographic coordinates to an individual based on their street address. This method often relies on street centerline files as a geographic reference. Such a process introduces positional error in the geocoded point. Our study evaluated the positional error caused during automated Geocoding of residential addresses and how this error varies between population densities. We also evaluated an alternative method of Geocoding using residential property parcel data. Results: Positional error was determined for 3,000 residential addresses using the distance between each geocoded point and its true location as determined with aerial imagery. Error was found to increase as population density decreased. In rural areas of an upstate New York study area, 95 percent of the addresses geocoded to within 2,872 m of their true location. Suburban areas revealed less error where 95 percent of the addresses geocoded to within 421 m. Urban areas demonstrated the least error where 95 percent of the addresses geocoded to within 152 m of their true location. As an alternative to using street centerline files for Geocoding, we used residential property parcel points to locate the addresses. In the rural areas, 95 percent of the parcel points were within 195 m of the true location. In suburban areas, this distance was 39 m while in urban areas 95 percent of the parcel points were within 21 m of the true location. Conclusion: Researchers need to determine if the level of error caused by a chosen method of Geocoding may affect the results of their project. As an alternative method, property data can be used for Geocoding addresses if the error caused by traditional methods is found to be unacceptable.
