- Open Access
The importance of accurate road data for spatial applications in public health: customizing a road network
© Frizzelle et al; licensee BioMed Central Ltd. 2009
- Received: 03 February 2009
- Accepted: 01 May 2009
- Published: 01 May 2009
Health researchers have increasingly adopted the use of geographic information systems (GIS) for analyzing environments in which people live and how those environments affect health. One aspect of this research that is often overlooked is the quality and detail of the road data and whether or not it is appropriate for the scale of analysis. Many readily available road datasets, both public domain and commercial, contain positional errors or generalizations that may not be compatible with highly accurate geospatial locations. This study examined the accuracy, completeness, and currency of four readily available public and commercial sources for road data (North Carolina Department of Transportation, StreetMap Pro, TIGER/Line 2000, TIGER/Line 2007) relative to a custom road dataset which we developed and used for comparison.
Methods and Results
A custom road network dataset was developed to examine associations between health behaviors and the environment among pregnant and postpartum women living in central North Carolina in the United States. Three analytical measures were developed to assess the comparative accuracy and utility of four publicly and commercially available road datasets and the custom dataset in relation to participants' residential locations over three time periods. The exclusion of road segments and positional errors in the four comparison road datasets resulted in between 5.9% and 64.4% of respondents lying farther than 15.24 meters from their nearest road, the distance of the threshold set by the project to facilitate spatial analysis. Agreement, using a Pearson's correlation coefficient, between the customized road dataset and the four comparison road datasets ranged from 0.01 to 0.82.
This study demonstrates the importance of examining available road datasets and assessing their completeness, accuracy, and currency for their particular study area. This paper serves as an example for assessing the feasibility of readily available commercial or public road datasets, and outlines the steps by which an improved custom dataset for a study area can be developed.
- Global Position System
- Geographic Information System
- Road Network
- Positional Accuracy
- Road Segment
Over the last two decades, the public health field has increasingly adopted the use of Geographic Information Systems (GIS) for analyzing environments in which people live and how those environments affect health. One subset of this research focuses on the impacts of road networks, examining accessibility to health care along those networks [1–3]. Another area of research has examined the geographic location of road networks relative to other locations of interest, such as places of residence, schools, or other community facilities, and how such proximity affects health outcomes. Specifically, this research has used road networks in GIS to examine exposures such as traffic , air pollution [5–8], and degree of urbanicity  and to study health outcomes such as physical activity [9–13], respiratory, pulmonary and cardiovascular morbidity and mortality [14–26], pre-term birth [27, 28], and childhood cancers [29–32].
Several studies have assessed the positional accuracy or error of digital line data. Efforts to examine accuracy are limited in their applicability because they either require a large expenditure of effort to calculate accuracy, or they require a consistency and comparability in data attribution that may not be present among most road datasets. These studies include the development of models for calculating positional error of line segments [41, 42], calculating distances between road segments matched by road name , assessing error via the distances between intersections in the data and reference points collected at road intersections with global positioning system (GPS)  or from aerial photography , and comparing road density to a very detailed road network built manually for such a purpose . In general, there does not appear to be a proven method of effectively assessing positional accuracy for a wide variety of digital line features such as roads. Since positional error distributions are typically not normally distributed, and vary greatly depending on the type of feature, the methods for assessing positional accuracy will likely depend on the source of the spatial data being analyzed and the method by which is was created .
This study examines the accuracy, completeness, and currency of four readily available public and commercial sources for road data relative to a custom road dataset that we used for comparison. The customized road dataset was built for the purpose of analyzing the association of health behaviors with the built environment among women living in central North Carolina in the United States from pregnancy to postpartum . The development of a road dataset tailored to the specific needs of this project was a result of the observation that other available public and commercial road datasets were insufficient in terms of their positional accuracy and completeness of features. Many of these public and commercial datasets are readily available and are used for spatial analysis in public health research. This paper serves as a guide for assessing the feasibility of readily available commercial or public road datasets, explains their limitations, discusses the circumstances under which the development of an improved custom road dataset would be beneficial, and outlines the steps to develop such a dataset.
The unique locations of 2,444 residences, representing homes for 1,491 participants over three time periods, were identified via GPS or address geocoding. Each residential location was subsequently moved manually, if necessary, to lie within 50 feet (15.24 meters) of the road segment on which they lived, falling on or near the driveway. We required that all participant home locations be within 50 feet (15.24 meters) of their road segment to facilitate GIS analyses. Neighborhoods were participant-specified, defined as the area covered by travelling up to 1 mile (1.6 kilometers) on the road network.
Accuracy, completeness, and currency are critical elements in this study. Accuracy is important because we investigated the nature of relatively small neighborhoods. The actual size of neighborhoods depended on the road connectivity around each participant's home, ranging from 0.05 square miles (0.13 km2) and 3.14 square miles (8.14 km2). Completeness is important because the study area of Alamance, Chatham, Durham, and Orange counties is rapidly growing and includes land uses ranging from high-density urban (e.g., the city of Durham) to low-density rural. The development of single-family home subdivisions is prevalent throughout the four counties and home construction has been widespread and consistent for more than a decade. Therefore, the road network is very dynamic, with new roads being built often, and that requires that road datasets be updated on a regular basis. Currency is important because a road network that is not maintained and updated regularly will not capture all new roads as they are built. In such a dynamically changing area such as our study area, road data can quickly become outdated.
Publicly and Commercially Available Road Datasets
Summary information on the four publicly and commercially available road datasets
North Carolina Dept. of Transportation
U.S. Census Bureau
U.S. Census Bureau
The NC DOT road dataset maintained by the state of North Carolina covers all 100 counties. It is a public domain dataset, available for free download from the NC DOT website . NC DOT data is available both county-by-county and as a comprehensive statewide layer. The metadata accompanying the dataset states that its purpose is to "assist governmental agencies and others in making resource management decisions through the use of a Geographic Information System... [via] location analysis."  We selected the most recent version at that time, which was current to 2005.
The US Census TIGER/Line road data are a subset of the TIGER/Line line features dataset maintained by the US Census Bureau in Washington, D.C. All TIGER/Line data have a very generic purpose, to be used for "geographic applications."  As with the NC DOT data, it is a free public domain, and must be downloaded county by county. TIGER/Line files contain a multitude of geographic features for the entire United States, but it is possible to subset out particular features for specific needs, such as roads in this case. The TIGER/Line datasets historically contain many errors of spatial accuracy and completeness, especially in rural areas . However in 2004, as part of the MAF/TIGER AIP, the First Addition of the TIGER/Line data began incorporating "realigned street feature coordinates" from counties or statistically equivalent entities . We chose the 2000 TIGER/Line data because it was readily available at the time of the study.
The ESRI StreetMap Pro data (now called Streetmap North America) are a commercially maintained dataset, available for purchase on a licensed basis from ESRI. StreetMap Pro is based on Tele Atlas data and covers the entire United States and Canada. The stated purpose of this dataset is to be used for "display, routing and geocoding."  The StreetMap Pro road features are current as of 2003.
All of these datasets were considered insufficient to suit the spatial needs of the project. We performed a visual investigation, such as described by Hawbaker and Radeloff , by overlaying participant locations on each of the road networks, and also comparing roads to aerial photographs and county tax parcel data. We determined that all three datasets were missing a large number of local roads, both rural and suburban, on which participants lived. The positional accuracy of the StreetMap data was poor in rural areas, while the TIGER/Line 2000 positional accuracy was poor in all portions of the study area through visual inspection. The NC DOT and StreetMap Pro datasets included many non-road features in rural areas, and the TIGER/Line 2000 features were highly generalized and not reflective of the existing road network.
In this analysis, we also considered the 2007 TIGER/Line road data as an additional comparison. However, these road data were not available at the time we were evaluating datasets. These data are a result of the US Census Bureau's plan to use locally derived feature data to increase positional accuracy to meet their stated goals that 95% of the features be within 7.6 meters of their true locations . Our intent in adding this dataset to the analysis is to provide readers with additional information when deciding which dataset is most appropriate for their work.
Customized Road Network
Since the NC DOT, TIGER/Line, and StreetMap Pro datasets did not meet our criteria of completeness or accuracy when we conducted the study, county-level road centerline datasets were obtained for the four-county study area. These datasets are developed and maintained by local county departments and designed to be compatible with the county-maintained tax parcel datasets. Visual comparison of the county centerline files with the larger, more expansive datasets revealed that the county data were superior to the others in both positional accuracy and currency of road features. We therefore chose to combine the various county datasets – from Alamance, Chatham, Durham, and Orange counties – into one layer covering the full study area.
The Alamance road data were developed by the Alamance County GIS Department and made available for download from their website. They were current as of 2004, and the initial review of the data showed some geographic errors (e.g. inclusion of non-road features) and quite a few attribute errors (e.g. no names on many rural roads). According to the metadata accompanying the dataset, the positional accuracy of the dataset conformed to the Technical Specifications for Base, Cadastral, and Digital Mapping document produced by the NC Land Records Management Program .
The Chatham County road data were produced by the Chatham County GIS Department, the Durham County road data were produced by the Durham County GIS Department, and the Orange County road data were produced by the Orange County GIS Department. All three of these datasets were current as of 2005. None of the datasets were accompanied by metadata, so we had no information on the scale of usage or on the assessed positional accuracy of the features. However, simple overlays with tax parcels and aerial photographs assured us that the accuracy was more than sufficient for our needs.
The four datasets were edge-matched and appended together to create a study area-wide road network dataset. Edge-matching is a spatial adjustment method that aligns features along the edge of adjacent datasets so that they can be merged together. Appending is a spatial editing method that merges multiple datasets of the same data type together into one larger dataset. The baseline dataset was overlaid on tax parcels and aerial photography to identify and delete non-road features that were erroneously included in the data. To make the dataset topologically sound, pseudo-nodes and overlapping line features were removed and intersections were created where two roads crossed but did not intersect. Subdivisions, apartment complexes, trailer parks, or other legitimate networks of roads on which people live were added when discovered to be missing from the data. Road features were modified and reshaped to more accurately fit reality when incorrect. This included updating roads that had been rerouted and widened from two-lane to four-lane-divided, lengthening roads that did not extend to their full length, and shortening roads that extended too far. Updates were performed through GIS overlays with aerial photographs and tax parcels, and by obtaining road locations in the field with differentially corrected GPS data collected with mapping grade Trimble GPS receivers.
Spatial and Statistical Analyses
The goal of our analysis was to quantitatively compare the four commercially and publicly available road network datasets to the custom road network data. It should be noted that none of these methods are designed to assess the error of individual road segments. While there are studies that have done this [36, 40–44], the time investment required for such an endeavour was considered to too large to justify the minimal gain in understanding the nature of the errors. Therefore, the methods that were chosen provide sufficient information on the spatial distribution of errors and are easier to implement.
We used three measures, described below, to comparatively assess the accuracy and completeness of the various publicly and commercially available datasets. The first two were localized, neighborhood-based methods that used a Euclidean buffer with a radius of 402 meters (0.25 miles) around each participant location to calculate local metrics that informed us of dataset comparability as it pertains to our participants. The third method was a more comprehensive approach that compared the datasets in their entirety to one another. We used the customized road network as the comparison dataset.
The first measure was distance from each participant's home to the nearest road, calculated for all road datasets. This Distance to Nearest Road measure allowed us to easily identify how many participants would not be located on their road if any of the other datasets were used. Recall that we hand corrected our participant locations to within 15.24 meters (50 feet) of their road feature in the customized road network. Therefore, we can say with confidence that any participant located farther than 15.24 meters from the nearest road might have been excluded from some of the network analyses if they were performed with a different road dataset. However, the alternative would have been to increase the search threshold for the network analysis, which would likely have resulted in some locations being assigned to the wrong roads. We calculated the percentage of participants whose nearest road was farther than that threshold distance, but compensating for slight differences in positional accuracy which may not have significantly impacted our research, we also calculated those percentages using thresholds of 50% and 100% larger than our 15.24 meters.
Total Road Length was our second measure. It was calculated within the Euclidean buffer around each of the participant locations. This measure was used in lieu of a density measure as all buffers were the same size and therefore the relationships were identical in the two measures. Comparatively, a significant deviation in total length between a readily available dataset and the customized data indicated that the available data omitted features, included non-road features, or had positional errors in the features that could be random or systematic. Thresholds values (deltas) were set for identifying those neighborhoods with divergent road networks.
The spatial distribution of the errors was then investigated using the Cell Length Summation results. In these analyses, the dependent variable was each cell's sum of the road length for the custom dataset, and the independent variables included the sum of each comparison dataset's roads. Only those cells containing roads from at least one of the five datasets were used, which reduced the sample size from the full 6.73 million to 729,597 observations. From the four linear regression models, the studentized residuals were calculated and assigned to each cell. The residuals summarize the standardized error in each cell between the custom dataset and the comparison dataset.
Mean, standard deviation, and maximum distance from the participant's home to the nearest road using five different road datasets (n = 2,444, participant locations)
Mean in m
Standard. Deviation in m
Maximum in m
Threshold analysis from Distance to Nearest Road measure that assesses the distance from the participant's home to the nearest road (n = 2,444 participant locations)
Observations > 15.24 m (%)
Observations 15.24 m – 22.86 m (%)
Observations 22.86 m – 30.48 m (%)
Observations > 30.48 m (%)
Total road length agreement between customized and other road datasets in vector cells containing at least one road dataset (n = 729,597 cells)
Pearson Correlation Coefficient
0.82 (< 0.001)
0.83 (< 0.001)
0.60 (< 0.001)
0.60 (< 0.001)
0.01 (< 0.001)
0.01 (< 0.001)
0.79 (< 0.001)
0.79 (< 0.001)
To examine geographically the areas with low agreement from the customized data, we used the residuals from univariate regressions of the cell length sum of the customized road data as dependent variable and each of the comparison datasets as independent variables. We explored additional regression analyses using 2000 US Census block group mean-centered socio-economic and urbanization variables to examine whether they explained the residuals calculated (results not shown), but none of the independent variables had meaningful relationships with the errors.
There are a number of benefits to using prepackaged comprehensive road data in research studies. These road datasets often cover very large areas, whether at the state or national level, and the associated attribute information is often clear and consistent. Often they are topologically sound, meaning that all roads are connected at intersections, creating a complete network that facilitates GIS network analysis. However, there are some important limitations as well. We found that these prepackaged datasets are often out of date, are missing new roads, and contain positional errors which can cause erroneous analysis results. By comparing these datasets to a custom designed road network dataset that was both current and accurate, we discovered that errors of positional accuracy, omission and commission occur more often and with greater magnitude in rural areas and in suburban areas experiencing rapid change and growth.
So how does one go about anticipating problems with available road datasets and then determining whether or not to create a custom road dataset? There are a several points to consider. First, determine the purpose of the original datasets and the reported scale and positional accuracy. This information should be provided in the accompanying metadata, although not all spatial datasets have metadata files. Small-scale datasets, meaning those that cover very large areas, tend to have generalized features and larger positional error in their features. Conversely, large-scale datasets, those that cover small areas, tend to have more detailed features and less positional error . But scale is a tricky concept for digital data, because small-scale data can be subset for a small area and still contain the original errors. For example, if a road dataset for the United States has a scale of 1:1,000,000, it will have very generalized and inaccurate features and will likely be missing many local roads. But if someone were to subset those data for the city of Chicago, it now covers a small area but it still contains the same errors and is still subject to the small-scale limitations. Positional accuracy, often referred to as horizontal accuracy in metadata, is a simpler concept to interpret. A reported horizontal error of 10 meters is an average error for a dataset, but it can be assumed it is consistent throughout and is not affected by a change in the spatial extent of the data. And in general, it is always important that all datasets, road or otherwise, have similar scales or positional accuracy.
Second, the currency of the dataset can be key. This is also reported in the metadata, and is usually called Time Period of Content or something similar. If a study area is experiencing or has experienced significant change between the date of the dataset and the current time, then it is likely that the data will not contain all of the roads needed. However, areas that do not experience much change in the road network, such as central cities, can better handle older road datasets than newer areas undergoing rapid changes. Knowledge of the study area and the nature of its growth is a key in assessing potential problems.
In any situation where the analysis focuses on locations of interest (e.g. participants, clinics, recreational facilities, etc.) with respect to a road network dataset, it is important that the source of the road network data match the means by which the locations were gathered. For example, if locations of a feature or a participant were collected with GPS receivers, then it is important to have highly accurate road centerlines, preferably those from local municipal or county departments. However, if locations were geocoded from address data in a GIS, then the most appropriate road network dataset to use with those locations would be the same road data used in the geocoding process. Even if geocoded results come from the same road data source, geocoding inaccuracies can still introduce substantial biases [58–62].
The two overriding concerns with the datasets we investigated were the level of positional accuracy and the completeness of the data. It is generally understood that most large road datasets substantially underestimate road density because they tend to omit minor roads and they are not updated regularly, missing new road development . In addition, depending on the underlying source data from which the road features are based, the overall positional accuracy of the features may be less than appropriate for the study. At the time we were developing the spatial database and evaluating road datasets, all three available datasets (StreetMap Pro, NC DOT, and TIGER/Line 2000) were determined to be inadequate for the study's needs due to missing roads in many neighborhoods in which the study participants lived. In addition, the TIGER/Line data exhibited poor positional accuracy and highly generalized road features.
Based on the results of the analyses and on visual comparisons, it appears that the NC DOT and TIGER/Line 2007 data appeared to be sufficiently similar to the project dataset that they would be acceptable substitutes if no other alternative existed. However, it is still important to note that both of these datasets are still lacking in temporal currency and inclusion of new roads, and therefore the effort to create a customized road dataset that was both current and complete was the best approach. There is no single numeric measure that effectively captures the fact that many neighborhood roads on which are project participants live do not exist in one or both of these datasets, although the neighborhood-based measures do get at this problem from several different angles.
There are some distinct circumstances in which the custom development of a road network data development can be considered both feasible and desirable. First, the study area must be sufficiently small. Our study area covered 4,773 square kilometers and has 11,500 kilometers of roads. This approach is not feasible for a statewide, regional, or nationwide study. Second, local knowledge of roads is a clear asset for understanding the nature of change and growth and because the project team will be able to easily and inexpensively conduct "ground truthing" or field visits with GPS receivers to collect or validate new roads or possibly erroneous road features. Third, a rural or suburban area that is experiencing moderate to rapid growth is a prime candidate for this approach. Areas such as these quickly fall out of date in road datasets. But it is ultimately up to the investigators and the project team to determine if the study area lends itself to the creation of a customized road dataset.
Having a customized road dataset provides a number of benefits. The data are more current and complete, so if the work is comprehensive, there will not be any areas within the study area where roads are not represented. There also will not be any non-road features (e.g., driveways, power lines, railroads, rivers) in the dataset. This is very important, because the inclusion of non-road features can provide alternative non-existent routes in network analysis that will skew the results and add unexpected error. However, the significant downside is the sheer amount of time and expense required to complete it.
This study demonstrates the importance of examining the available prepackaged comprehensive road datasets and assessing their completeness, accuracy, and currency for their particular study area. If enough critical roads in the study area are missing or inaccurate, then that makes a strong case for following our methodology in creating a new road dataset using data from municipalities and/or counties for the analysis.
Funding for this study was provided by the National Institutes of Health (NIH)/National Cancer Institute (#CA109804-01) and Department of Health and Human Services, Health Resources and Services Administration, Maternal & Child Health Bureau (# 1 R40MC07841-01-00). Data collection was supported by NIH/National Institute of Child Health and Human Development (#HD37584), NIH General Clinical Research Center (#RR00046), and NIH/National Institute of Diabetes and Digestive and Kidney Diseases (#DK 061981-02). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The Pregnancy, Infection, and Nutrition Study is a joint effort of many investigators and staff members whose work is gratefully acknowledged. Specifically, the authors acknowledge David Bergmark, Leigh Ann Cienek, Annie Lux, and Lindy Nelson for working on the customized road network dataset.
- Brabyn L, Skelly C: Modeling population access to New Zealand public hospitals. International Journal of Health Geographics. 2002, 1: 3-10.1186/1476-072X-1-3.PubMedPubMed CentralView ArticleGoogle Scholar
- Jordan L: Improving livelihoods by improving access: The application of geospatial technologies to healthcare accessibility. Harvard Health Policy Review. 2008, Spring 2008: 210-222.Google Scholar
- Luo W, Wang F: Measures of spatial accessibility to health care in a GIS environment: synthesis and a case study in the Chicago region. Environment and Planning B: Planning and Design. 2003, 30: 865-884. 10.1068/b29120.View ArticleGoogle Scholar
- Green RS, Smorodinsky S, Kim JJ, McLaughlin R, Ostro B: Proximity of California public schools to busy roads.(Children's Health). Environ Health Perspect. 2004, 112: 61-(66)PubMedPubMed CentralView ArticleGoogle Scholar
- Hoek G, Fischer P, Brandt Van Den P, Goldbohm S, Brunekreef B: Estimation of long-term average exposure to outdoor air pollution for a cohort study on mortality. J Exp Ana Env Epi. 2001, 11: 459-469. 10.1038/sj.jea.7500189.View ArticleGoogle Scholar
- Hewitt CN: Spatial variation in nitrogen dioxide concentrations in an urban area. Atmos Environ. 1991, 25B: 429-434.View ArticleGoogle Scholar
- Kinney PL, Aggarwal M, Northridge ME, Janssen NA, Shepard P: Airborne concentrations of PM(2.5) and diesel exhaust particles on Harlem sidewalks: a community-based pilot study. Environ Health Perspect. 2000, 108: 213-218. 10.2307/3454436.PubMedPubMed CentralGoogle Scholar
- Zhu Y, Hinds WC, Kim S, Shen S, Sioutas C: Study of ultrafine particles near a major highway with heavy-duty diesel traffic. Atmos Environ. 2002, 36: 4323-4335. 10.1016/S1352-2310(02)00354-0.View ArticleGoogle Scholar
- Davison K, Lawson C: Do attributes in the physical environment influence children's physical activity? A review of the literature. International Journal of Behavioral Nutrition and Physical Activity. 2006, 3: 19-10.1186/1479-5868-3-19.PubMedPubMed CentralView ArticleGoogle Scholar
- Chin GKW, Van Niel KP, Giles-Corti B, Knuiman M: Accessibility and connectivity in physical activity studies: The impact of missing pedestrian data. Preventive Medicine. 2008, 46: 41-45. 10.1016/j.ypmed.2007.08.004.PubMedView ArticleGoogle Scholar
- Lovasi GS, Moudon AV, Pearson AL, Hurvitz PM, Larson EB, Siscovick DS, Berke EM, Lumley T, Psaty BM: Using built environment characteristics to predict walking for exercise.(Research). International Journal of Health Geographics. 2008, 7: 10-10.1186/1476-072X-7-10.PubMedPubMed CentralView ArticleGoogle Scholar
- Oakes JM, Forsyth A, Schmitz K: The effects of neighborhood density and street connectivity on walking behavior: the Twin Cities walking study. Epidemiologic Perspectives & Innovations. 2007, 4: 16-10.1186/1742-5573-4-16.View ArticleGoogle Scholar
- Saelens BE, Sallis JF, Frank LD: Environmental correlates of walking and cycling: findings from the transportation, urban design, and planning literatures. Annals of Behavioral Medicine. 2003, 25: 80-91. 10.1207/S15324796ABM2502_03.PubMedView ArticleGoogle Scholar
- Brunekreef B, Holgate ST: Air pollution and health. The Lancet. 2002, 360: 1233-1242. 10.1016/S0140-6736(02)11274-8.View ArticleGoogle Scholar
- Brunekreef B, Janssen NA, de Hartog J, Harssema H, Knape M, van Vliet P: Air pollution from truck traffic and lung function in children living near motorways. Epidemiology. 1997, 8: 298-303. 10.1097/00001648-199705000-00012.PubMedView ArticleGoogle Scholar
- English , Neutra P: Examining associations between childhood asthma and traffic flow using a geographic information. Environ Health Perspect. 1999, 107: 761-10.2307/3434663.PubMedPubMed CentralView ArticleGoogle Scholar
- Gordian ME, Haneuse S, Wakefield J: An investigation of the association between traffic exposure and the diagnosis of asthma in children. J Exp Ana Env Epi. 2005, 16: 49-55. 10.1038/sj.jea.7500436.View ArticleGoogle Scholar
- Hoek G, Brunekreef B, Goldbohm S, Fischer P, Brandt van den PA: Association between mortality and indicators of traffic-related air pollution in the Netherlands: a cohort study. The Lancet. 2002, 360: 1203-1209. 10.1016/S0140-6736(02)11280-3.View ArticleGoogle Scholar
- Kim JJ, Smorodinsky S, Lipsett M, Singer BC, Hodgson AT, Ostro B: Traffic-related air pollution near busy roads: the East Bay Children's Respiratory Health Study. Am J Respir Crit Care Med. 2004, 170: 520-526. 10.1164/rccm.200403-281OC.PubMedView ArticleGoogle Scholar
- Maheswaran R, Elliott P: Stroke Mortality Associated With Living Near Main Roads in England and Wales: A Geographical Study. Stroke. 2003, 34: 2776-2780. 10.1161/01.STR.0000101750.77547.11.PubMedView ArticleGoogle Scholar
- Oosterlee A, Drijver M, Lebret E, Brunekreef B: Chronic respiratory symptoms in children and adults living along streets with high traffic density. Occup Environ Med. 1996, 53: 241-247. 10.1136/oem.53.4.241.PubMedPubMed CentralView ArticleGoogle Scholar
- Oyana TJ, Rogerson P, Lwebuga-Mukasa JS: Geographic clustering of adult asthma hospitalization and residential exposure to pollution at a United States-Canada border crossing. Am J Public Health. 2004, 94: 1250-1257. 10.2105/AJPH.94.7.1250.PubMedPubMed CentralView ArticleGoogle Scholar
- Peters JM, Avol E, Gauderman WJ, Linn WS, Navidi W, London SJ, Margolis H, Rappaport E, Vora H, Gong H, Thomas DC: A study of twelve Southern California communities with differing levels and types of air pollution. II. Effects on pulmonary function. Am J Respir Crit Care Med. 1999, 159: 768-775.PubMedView ArticleGoogle Scholar
- van Vliet P, Knape M, de Hartog J, Janssen N, Harssema H, Brunekreef B: Motor vehicle exhaust and chronic respiratory symptoms in children living near freeways. Environ Res. 1997, 74: 122-132. 10.1006/enrs.1997.3757.PubMedView ArticleGoogle Scholar
- Venn AJ, Lewis SA, Cooper M, Hubbard R, Britton J: Living near a main road and the risk of wheezing illness in children. Am J Respir Crit Care Med. 2001, 164: 2177-2180.PubMedView ArticleGoogle Scholar
- Wyler C, Braun-Fahrlander C, Kunzli N, Schindler C, Ackermann-Liebrich U, Perruchoud AP, Leuenberger P, Wuthrich B: Exposure to motor vehicle traffic and allergic sensitization. The Swiss Study on Air Pollution and Lung Diseases in Adults (SAPALDIA) Team. Epidemiology. 2000, 11: 450-456. 10.1097/00001648-200007000-00015.PubMedView ArticleGoogle Scholar
- Wilhelm M, Ritz B: Residential proximity to traffic and adverse birth outcomes in Los Angeles county, California, 1994–1996. Environ Health Perspect. 2003, 111: 207-216.PubMedPubMed CentralView ArticleGoogle Scholar
- Yang C-Y, Chang C-C, Chuang H-Y, Ho C-K, Wu T-N, Tsai S-S: Evidence for increased risks of preterm delivery in a population residing near a Freeway in Taiwan. Archives of Environmental Health. 2003, 58: 649-10.3200/AEOH.58.10.649-654. (646)PubMedView ArticleGoogle Scholar
- Crosignani P, Tittarelli A, Borgini A, Codazzi T, Rovelli A, Porro E, Contiero P, Bianchi N, Tagliabue G, Fissi R: Childhood leukemia and road traffic: A population-based case-control study. International Journal of Cancer. 2004, 108: 596-599. 10.1002/ijc.11597.View ArticleGoogle Scholar
- Pearson RL, Wachtel H, Ebi KL: Distance-weighted traffic density in proximity to a home is a risk factor for leukemia and other childhood cancers. J Air Waste Manag Assoc. 2000, 50: 175-180.PubMedView ArticleGoogle Scholar
- Nordlinder R, Jarvholm B: Environmental exposure to gasoline and leukemia in children and young adults – an ecology study. Int Arch Occup Environ Health. 1997, 70: 57-60. 10.1007/s004200050186.PubMedView ArticleGoogle Scholar
- Savitz DA, Feingold L: Association of childhood cancer with residential traffic density. Scand J Work Environ Health. 1989, 15: 360-363.PubMedView ArticleGoogle Scholar
- TIGER/Line 2004 First Edition Readme File.http://www.census.gov/geo/www/tiger/tiger2004fe/tgr2004fe.html
- O'Grady K, Godwin L: The Positional Accuracy of MAF/TIGER. 2000, Washington, D.C.: U.S. Department of Commerce, Bureau of the Census,http://www.census.gov/geo/mod/positional_accuracy.pdfGoogle Scholar
- Trainor T: U.S. Census Bureau geographic support: A response to changing technology and improved data. Cartography and Geographic Information Science. 2003, 30: 217-10.1559/152304003100011054.View ArticleGoogle Scholar
- Wu J, Funk TH, Lurmann FW, Winer AM: Improving spatial accuracy of roadway networks and geocoded addresses. Transactions in GIS. 2005, 9: 585-601. 10.1111/j.1467-9671.2005.00236.x.View ArticleGoogle Scholar
- Braddock M, Lapidus G, Cromley E, Cromley R, Burke G, Banco L: Using a Geographic Information System to Understand Child Pedestrian Injury. Am J Public Health. 1994, 84: 1158-1161. 10.2105/AJPH.84.7.1158.PubMedPubMed CentralView ArticleGoogle Scholar
- Vine MF, Degnan D, Hanchette C: Geographic Information Systems: Their Use in Environmental Epidemiologic Research. Environ Health Perspect. 1997, 105: 598-605. 10.2307/3433605.PubMedPubMed CentralView ArticleGoogle Scholar
- Walsh SJ, Page PH, Gesler WM: Normative models and healthcare planning: network-based simulations within a geographic information system environment. Health Serv Res. 1997, 32 (2): 243-260. 218PubMedPubMed CentralGoogle Scholar
- Hawbaker TJ, Radeloff VC: Roads and landscape pattern in northern Wisconsin based on a comparison of four road data sources. Conserv Biol. 2004, 18: 1233-1244. 10.1111/j.1523-1739.2004.00231.x.View ArticleGoogle Scholar
- Shi W, Liu W: A stochastic process-based model for the positional error of line segments in GIS. Int J Geogr Inf Sci. 2000, 14: 51-66. 10.1080/136588100240958.View ArticleGoogle Scholar
- Zhang J, Kirby RP: A geostatistical approach to modelling positional errors in vector data. Transactions in GIS. 2000, 4: 145-159. 10.1111/1467-9671.00044.View ArticleGoogle Scholar
- Van Niel TG, McVicar TR: Experimental evaluation of positional accuracy estimates from a linear network using point- and line-based testing methods. Int J Geogr Inf Sci. 2002, 16: 455-473. 10.1080/13658810210137022.View ArticleGoogle Scholar
- Zandbergen PA: Positional accuracy of spatial data: Non-normal distributions and a critique of the National Standard for Spatial Data Accuracy. Transactions in GIS. 2008, 12: 103-130. 10.1111/j.1467-9671.2008.01088.x.View ArticleGoogle Scholar
- Frizzelle B, Bergmark D, Cienek LA, Lux A, Nelson L: PIN3 Physical Activity Postpartum Study Spatial Data Development & Analysis. Version 2.2. Last updated April 23, 2009. 2009, Chapel Hill, NC,http://www.cpc.unc.edu/projects/pin/design_postpart/docs_postpart/PIN3-PAPost-Spatial-Data.pdfGoogle Scholar
- North Carolina Department of Transportation Geographic Information System. http://www.ncdot.org/IT/gis/
- North Carolina Department of Transportation: Integrated Statewide Road Network (ISRN). 2007, Raleigh, NCGoogle Scholar
- U.S. Census Bureau: Census 2000 TIGER/Line Files. Washington, D.C. 2000Google Scholar
- Rudnicki R: Assessment of TIGER/Line files for rural counties: The case of Hays County, Texas. URISA Journal. 1992, 4: 32-46.Google Scholar
- ESRI: StreetMap Pro. ESRI Data & Maps (ESRI ed., 2005 edition). 2005, Redlands, CA: ESRIGoogle Scholar
- U.S. Census Bureau: TIGER/Line Shapefiles 2007 Technical Documentation. 2008, Washington, D.C.: U.S. Census Bureau, http://www.census.gov/geo/www/tiger/tgrshp2007/TGRSHP07.pdfGoogle Scholar
- North Carolina Land Records Management Program: Technical Specifications for Base, Cadastral, and Digital Mapping. 1987, Raleigh: Land Records Management ProgramGoogle Scholar
- Beyer HL: Hawth's Analysis Tools for ArcGIS. 2004, http://www.spatialecology.com/htoolsGoogle Scholar
- Lin LI-K: A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989, 45: 255-268. 10.2307/2532051.PubMedView ArticleGoogle Scholar
- Lin LI-K: A note on the concordance correlation coefficient. Biometrics. 2000, 56: 324-325. 10.1111/j.0006-341X.2000.00775.x.View ArticleGoogle Scholar
- Landis JR, Koch GG: The Measurement of Observer Agreement for Categorical Data. Biometrics. 1977, 33: 159-174. 10.2307/2529310.PubMedView ArticleGoogle Scholar
- Tomlinson RF: Thinking about GIS: Geographic information system planning for managers. 2007, Redlands, CA: ESRI PressGoogle Scholar
- Mazumdar S, Rushton G, Smith BJ, Zimmerman DL, Donham KJ: Geocoding accuracy and the recovery of relationships between environmental exposures and health.(Research). International Journal of Health Geographics. 2008, 7: 13-10.1186/1476-072X-7-13.PubMedPubMed CentralView ArticleGoogle Scholar
- Whitsel EA: Error and bias in geocoding school and students' home addresses.(Correspondence)(Letter to the editor). Environ Health Perspect. 2008, 116: A330-10.1289/ehp.11542. (331).PubMedPubMed CentralView ArticleGoogle Scholar
- Zandbergen P: Influence of geocoding quality on environmental exposure assessment of children living near high traffic roads. BMC Public Health. 2007, 7: 37-10.1186/1471-2458-7-37.PubMedPubMed CentralView ArticleGoogle Scholar
- Zandbergen PA, Green JW: Error and bias in determining exposure potential of children at school locations using proximity-based GIS techniques.(geographic information systems)(Children's Health)(Clinical report). Environ Health Perspect. 2007, 115: 1363-(1368).PubMedPubMed CentralView ArticleGoogle Scholar
- Krieger N, Waterman P, Lemieux K, Zierler S, Hogan JW: On the wrong side of the tracts? Evaluating the accuracy of geocoding in public health research. Am J Public Health. 2001, 91: 1114-1116. 10.2105/AJPH.91.7.1114.PubMedPubMed CentralView ArticleGoogle Scholar
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