Positional Accuracy of TIGER 2000 and 2009 Road Networks
نویسندگان
چکیده
The Topologically Integrated Geographic Encoding and Referencing (TIGER) data are an essential part of the US Census and represent a critical element in the nation’s spatial data infrastructure. TIGER data for the year 2000, however, are of limited positional accuracy and were deemed of insufficient quality to support the 2010 Census. In response the US Census Bureau embarked on the MAF/TIGER Accuracy Improvement Project (MTAIP) in an effort to improve the positional accuracy of the database, modernize the data processing environment and improve cooperation with partner agencies. Improved TIGER data were released for the entire US just before the 2010 Census. The current study characterizes the positional accuracy of the TIGER 2009 data compared with the TIGER 2000 data based on selected road intersections. Three US counties were identified as study areas and in each county 100 urban and 100 rural sample locations were selected. Features in the TIGER 2000 and 2009 data were compared with reference locations derived from high resolution natural color orthoimagery. Results indicate that TIGER 2009 data are much improved in terms of positional accuracy compared with the TIGER 2000 data, by at least one order of magnitude across urban and rural areas in all three counties for most accuracy metrics. TIGER 2009 is consistently more accurate in urban areas compared with rural areas, by a factor of at least two for most accuracy metrics. Despite the substantial improvement in positional accuracy, large positional errors of greater than 10 m are relatively common in the TIGER 2009 data, in most cases representing remnant segments of minor roads from older versions of the TIGER data. As a result, based on the US Census Bureau’s suggested accuracy Address for correspondence: Paul A. Zandbergen, Department of Geography, University of New Mexico, Bandelier West Room 111, MSC01 1110, Albuquerque, NM 87131, USA. E-mail: [email protected] Transactions in GIS, 2011, 15(4): 495–519 © 2011 Blackwell Publishing Ltd doi: 10.1111/j.1467-9671.2011.01277.x metric, the TIGER 2009 data meet the accuracy expectation of 7.6 m for two of the three urban areas but for none of the three rural areas. The suggested metric is based on the National Standard for Spatial Data Accuracy (NSSDA) protocol and was found to be very sensitive to the presence of a small number of very large errors. This presents challenges during attempts to characterize the accuracy of TIGER data or other spatial data using this protocol.
منابع مشابه
The importance of accurate road data for spatial applications in public health: customizing a road network
BACKGROUND 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 ...
متن کاملA Photogrammetric Approach for Assessing Positional Accuracy of OpenStreetMap© Roads
As open source volunteered geographic information continues to gain popularity, the user community and data contributions are expected to grow, e.g., CloudMade, Apple, and Ushahidi now provide OpenStreetMap © (OSM) as a base layer for some of their mapping applications. This, coupled with the lack of cartographic standards and the expectation to one day be able to use this vector data for more ...
متن کاملEvaluation and comparison performance of deep neural networks FCN and RDRCNN in order to identify and extract urban road using images of Sentinel-2 with medium spatial resolution
Road extraction using remote sensing images has been one of the most interesting topics for researchers in recent years. Recently, the development of deep neural networks (DNNs) in the field of semantic segmentation has become one of the important methods of Road extraction. In the Meanwhile The majority of research in the field of road extraction using DNN in urban and non-urban areas has been...
متن کاملForest Roads Mapped Using LiDAR in Steep Forested Terrain
LiDAR-derived digital elevation models can reveal road networks located beneath dense forest canopy. This study tests the accuracy of forest road characteristics mapped using LiDAR in the Santa Cruz Mountains, CA. The position, gradient, and total length of a forest haul road were accurately extracted using a 1 m DEM. In comparison to a field-surveyed centerline, the LiDAR-derived road exhibite...
متن کاملGeocoding accuracy and the recovery of relationships between environmental exposures and health
BACKGROUND 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 o...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Trans. GIS
دوره 15 شماره
صفحات -
تاریخ انتشار 2011