Topologically Integrated Geographic Encoding And Referencing Research Articles

Road network intersection density and childhood obesity risk in the US: a national longitudinal study

ObjectivesRoad intersection density is an important indicator of walkability. The objectives of this study were to examine the trends in intersection density in the US from 2007 to 2011 and assess the associations between intersection density and childhood obesity risk at the state level. Study designLongitudinal analyses were conducted to assess the spatial-temporal changes of population-weighted intersection density in relation to the risk of childhood obesity in the US. MethodsRoad network data from the Topologically Integrated Geographic Encoding and Referencing (TIGER) (2007–2011), the prevalence of overweight and obesity data from the National Survey of Children's Health (NSCH) (2007–2011), and the American Community Survey (ACS) (2011) were used. Geographic information system (GIS) visualization and spatial and regression analyses were conducted. Mixed-effect models were fit to assess the longitudinal relationship between intersection density and childhood obesity. ResultsBetween 2007 and 2011, population-weighted intersection density remained relatively stable in most states. Low-intersection-density states were clustered in the Southeastern region in both 2007 and 2011. The high-intersection-density states were clustered in the Middle Atlantic Division. California and Nevada also were identified as high-intersection-density clusters in 2011. States with lower road intersection density corresponded with states with higher childhood obesity prevalence. Our mixed-effect model estimates suggested that increased intersection density was associated with decreased obesity prevalence. ConclusionsThis study provided empirical evidence for longitudinal associations between neighborhood intersection density and childhood obesity prevalence based on national data and offered a new perspective of the role that road network plays in childhood obesity prevention.

Erratum: "Estimated Changes in Life Expectancy and Adult Mortality Resulting from Declining PM2.5 Exposures in the Contiguous United States: 1980-2010".

[This corrects the article DOI: https://doi.org/10.1289/EHP507.].

Testing the Impact of Road Network Connectivity on Criminal Lethality

Increased road network connectivity has been linked to more positive outcomes among all health outcomes. Road network connectivity has yet to be tested in association with specifically criminal lethality. The current study looks to incorporate road network connectivity as an explanatory variable for criminal lethality. Data on Road Network Connectivity and Criminal Lethality are gathered for 190 cities. Data sources include the National Incident-Based Reporting System (NIBRS), 2010 Census, 2010 American Community Survey, Google Earth, and Census Topologically Integrated Geographic Encoding and Referencing (TIGER) files. The data demonstrate that a city’s road network connectivity is related to decreases in the rates of lethality among assaults. Implications of this finding are discussed.

The impact of geocoding method on the positional accuracy of residential burglaries reported to police

PurposeThe purpose of this paper is to better understand the variability in burglary geocoding positional accuracy between United States Census Topologically Integrated Geographic Encoding and Referencing (TIGER)-based street geocoding and results produced using reference data made publicly available by Google.Design/methodology/approachThis research compares the Euclidian distance between ground-truthed burglaries and results produced using two different geocoding reference data sets: TIGER-based street geocoding and publicly available data within Google Earth. T-tests and z-tests are used to discern whether positional errors are statistically significant.FindingsBoth within suburban and urban jurisdictions, Google outperformed street geocoding in terms of positional accuracy. Positional errors on average were 1/4th as large for Google in a suburban setting and 1/5th as large in an urban setting compared to street geocoding.Practical implicationsPolice departments that are relying on street geocoding techniques may achieve improved spatial precision by using Google’s reference data if they contain parcel-level information. Moreover, relying on less precise spatial referencing methods may place burglaries in locations where the events do not actually occur or cluster.Originality/valueThis is the first analysis of law enforcement data to examine the positional accuracy of geocoded offense data using Google Earth compared to the commonly used street geocoding method of interpolation.

Conceptual framework and quantification of population vulnerability for effective emergency response planning.

The study focused on the methodological advancement and analytical approach of using multilevel data to define population vulnerability and risk in bioemergency disaster planning. The authors considered two types of vulnerabilities, transportation vulnerability that stems from lack of access to transportation (public or private) and communication vulnerability that stems from unavailability of needed language-specific communication resources. The authors used Transit Authority general transit feed data and the American Community Survey 5-year estimate data (2006-2010 summary files) to quantify these vulnerabilities. These data were integrated with Topologically Integrated Geographic Encoding and Referencing (TIGER) data for spatial analysis. A response plan was generated for Tarrant County, TX, and deemed feasible before consideration of vulnerable populations. The results point to the importance of integrating geographical and population demographic features that represent potential barriers to the optimum distribution and utilization of resources into the analysis of response plans. An examination of transportation vulnerabilities indicate that, of those vulnerable in Tarrant County, nearly 23,000 individuals will be at-risk of not being able to reach the Point Of Dispensing (POD) to obtain services as they are beyond walking distance to the POD and lack access to transportation resources. The analysis of language vulnerability depicts an uneven distribution resulting in nonuniform demand at PODs for translation resources. There are more than 11,000 at-risk households in the South East region of Tarrant County alone that are truly in need of translation services. The authors demonstrated that multiple vulnerabilities at each POD can be quantified by aggregating the vulnerability at the available granularity (ie, all blocks or block groups) in a given service area. The quantification of vulnerability at each service area facilitates a POD-based at-risk analysis for the response plan. Disparities stemming from social, behavioral, cultural, economic, and health characteristics of diverse subpopulations could induce the need for additional targeted resources to support emergency response efforts.

CONFLATION OF NATIONAL BRIDGE INVENTORY DATABASE WITH TIGERBASED ROAD VECTORS

Abstract. The National Bridge Inventory (NBI) database provides detailed bridge information with full coverage in the United States. In the database, each bridge record has an associated geographic location, which makes it possible to match the NBI records with road vectors in a geospatial database. This paper presents our approach to conflating the NBI database with U.S. Census Bureau's Topologically Integrated Geographic Encoding and Referencing (TIGER) road vectors based on a point-to-line matching algorithm, which combines both geometric and semantic similarity measurements. Experiments have shown that most NBI bridges can be successfully matched to TIGER road vectors with an overall success rate over 80%. The matched results are then used to help define linear features for bridges in the road database. The resultant road databases have enhanced bridge information and are further used in creating a 3D road database. The proposed methodology has been fully implemented and has been used in various applications.

Positional Accuracy of TIGER 2000 and 2009 Road Networks

AbstractThe 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 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.

A Snake-based Approach for TIGER Road Data Conflation

The TIGER (Topologically Integrated Geographic Encoding and Referencing) system has served the U.S. Census Bureau and other agencies' geographic needs successfully for two decades. Poor positional accuracy has however made it extremely difficult to integrate TIGER with advanced technologies and data sources such as GPS, high resolution imagery, and state/local GIS data. In this paper, a potential solution for conflation of TIGER road centerline data with other geospatial data is presented. The first two steps of the approach (feature matching and map alignment) remain the same as in traditional conflation. Following these steps, a third is added in which active contour models (snakes) are used to automatically move the vertices of TIGER roads to high-accuracy roads, rather than transferring attributes between the two datasets. This approach has benefits over traditional conflation methodology. It overcomes the problem of splitting vector road line segments, and it can be extended for vector imagery conflation as well. Thus, a variety of data sources (GIS, GPS, and Remote Sensing) could be used to improve TIGER data. Preliminary test results indicate that the three-step approach proposed in this paper performs very well. The positional accuracy of TIGER road centerline can be improved from an original 100 plus meters' RMS error to only 3 meters. Such an improvement can make TIGER data more useful for much broader application.

From TIGER to Audit Instruments: Measuring Neighborhood Walkability with Street Data Based on Geographic Information Systems

The relationship between urban form and pedestrian mobility is an area of increasing policy interest within the planning, transportation, environmental, and public health fields. Many municipalities are seeking to adopt variations of smart growth principles that seek, in part, to increase pedestrian choice in an urban environment. This paper explores how the path network around key urban destinations can be visually and quantitatively analyzed to provide useful planning and evaluation tools for these pedestrian-oriented environments. Neighborhood environments surrounding transit stops and schools are used as examples of how to visualize and quantify local walkable environments. Three key techniques based on geographic information system (GIS) are presented: street network classification, pedestrian catchment areas, and intersection intensities. Although such measures have been used elsewhere to some extent, this paper includes the idea of impedance, a method to help distinguish between automobile-oriented and pedestrian-oriented areas. A series of GIS-based qualitative visualization and quantitative analyses are presented, as are some basic steps on conducting the analyses within a GIS environment. A discussion of key data sources, including TIGER (topologically integrated geographic encoding and referencing) street data and new pedestrian audit instruments, are also presented as different ways to assess local walkability.

From TIGER to Audit Instruments

The relationship between urban form and pedestrian mobility is an area of increasing policy interest within the planning, transportation, environmental, and public health fields. Many municipalities are seeking to adopt variations of smart growth principles that seek, in part, to increase pedestrian choice in an urban environment. This paper explores how the path network around key urban destinations can be visually and quantitatively analyzed to provide useful planning and evaluation tools for these pedestrian-oriented environments. Neighborhood environments surrounding transit stops and schools are used as examples of how to visualize and quantify local walkable environments. Three key techniques based on geographic information system (GIS) are presented: street network classification, pedestrian catchment areas, and intersection intensities. Although such measures have been used elsewhere to some extent, this paper includes the idea of impedance, a method to help distinguish between automobile-oriented and pedestrian-oriented areas. A series of GIS-based qualitative visualization and quantitative analyses are presented, as are some basic steps on conducting the analyses within a GIS environment. A discussion of key data sources, including TIGER (topologically integrated geographic encoding and referencing) street data and new pedestrian audit instruments, are also presented as different ways to assess local walkability.

Spatial demography

This issue of PNAS brings together a collection of seven papers representing work in emerging areas of spatial demography, with special reference to North America. The papers have been assembled through the efforts of Susan Hanson of Clark University (Worcester, MA) and W. A. V. Clark of the University of California, Los Angeles. Although place, density, and movement have always had a part in population studies, they have not often held center stage. Demographers' prime coordinates are the two time-like coordinates of time and age. The three space-like coordinates of physical location and the many-dimensional coordinates of social location have tended to play supporting roles. Today, this situation is changing, and a host of scientific questions that intermingle geography with demography and with the whole range of the social sciences are coming to the fore. Two reasons for the expansion of spatial demography are readily apparent. One is the recent availability of finegrained spatial data linking geographic coordinates and categories to demographic, social, and economic variables, suited for analysis with the new computing tools of geographical information systems. In the United States, the Census Bureau's TIGER (Topologically Integrated Geographic Encoding and Referencing) system of the 1990s opened up opportunities that the private sector quickly augmented, and parallel developments have proceeded around the world. A second reason, in the United States, is “adversarial legalism” (1). Political decisions regarding distributional equity across jurisdictions, involving representation, public housing, discrimination, and civil rights, are propelled into the courts, creating and funding a demand for expertise in spatial analysis. A deeper reason, perhaps, is an emotional recognition that a once-rich diversity of local particularities in customs, accents, values, legends, architecture, instincts, foods, and memories is vanishing, or retreating into less visible forms. Our technological ability to analyze differences from town to town, valley to valley, …

Partnering for the People

During the past few years, the term “partnership” has been elevated in the Federal government to the state of an integral business practice, and rightly so. However, some Federal agencies have been partnering successfully for many decades. Two such agencies, the Department of Commerce’s U.S. Census Bureau and the Department of the Interior’s U.S. Geological Survey (USGS), have had partnerships that resulted in fundamental changes to the field of geographic data processing. While these agencies had cooperated successfully on projects earlier, from 1983 to 1989, they conducted a major partnership that resulted in the creation of the USGS’ 1:100,000-scale Digital Line Graph (DLG) and the Census Bureau’s Topologically Integrated Geographic Encoding and Referencing (TIGER) database. These datasets changed not only Federal geographic data processing practices but also set the course for future geographic data processing in state, local, and tribal governments, as well as in academia and private industry. Now, both agencies have embarked on ambitious programs to improve and evolve their datasets to meet more demanding Federal and other needs for the coming decades. The Census Bureau is improving its combined Master Address File (MAF) and TIGER database through an effort known as the MAF/TIGER Accuracy Improvement Project (MTAIP). Simultaneously, the USGS is undertaking the creation of The National Map, the initial foundation of which is existing datasets such as the National Hydrography Dataset (NHD). Although each agency’s program objectives are unique to their respective missions, the products of their labors are, once again, complementary. Given their history of partnership, it was logical for the Census Bureau and the USGS to again share risks and results. Commonalities between their respective databases and missions have been identified and an effort launched to reduce redundancy in data collection and preparation, while still assuring the objectives of the resulting databases are achieved and meet the overarching objectives of the National Spatial Data Infrastructure (NSDI).

Effects on local emergency departments of large-scale urban chemical fire with hazardous materials spill.

On 18 July 2001, a train hauling hazardous materials, including hydrochloric acid, hydrofluoric acid, and acetic acid, derailed in the city of Baltimore, Maryland, resulting in a fire that burned under a downtown street for five days. Firefighters were stymied in their efforts to extinguish the fire, and the city was subjected to thick smoke for several days. To determine whether an urban chemical fire with a hazardous materials spill resulted in a detectable public health impact, and to demographically describe the at-risk population for potential smoke and chemical exposure. The United States Centers for Disease Control and Prevention (CDC) was consulted about possible side effects from chemical exposure. Total numbers of emergency department (ED) patients and admissions from 15:00 hours (h), 15 July 2001 to 15:00 h, 21 July 2001 were collected from five local hospitals. Patient encounters citing specified chief complaints from 15:00 h, 15 July to 15:00 h, 18 July (pre-accident) were compared with the period from 15:00 h, July 18 to 15:00 h, 21 July (post-accident). Data were analyzed using Fisher's exact test. The United States Census Bureau's Topologically Integrated Geographic Encoding and Referencing (TIGER) digital database of geographic features and ArcView Geographic Information Systems (GIS) were used to create maps of Baltimore and to identify populations at-risk using attribute census data. There were 62,808 people residing in the immediate, affected area. The mean of the values for age was 33.7 +/- 3.2 years (standard deviation; range = 16 yrs) with 49% (30,927) males and 51% (31,881) females. A total of 2,922 ED patient encounters were screened. Chief complaints included shortness of breath, pre-event = 109 vs. post-event = 148; chest complaints = 90 vs. 113; burns and/or skin irritation = 45 vs. 42; eye irritation 26 vs. 34; throat irritation = 33 vs. 27; and smoke exposure = 0 vs. 15. There was a statistically significant increase (p < 0.05) for shortness of breath and smoke exposure-related complaints. No statistically significant increase in numbers of admitted patients with these complaints was found. In the setting of a large-scale urban chemical fire, local EDs can expect a significant increase in the number of patients presenting to EDs with shortness of breath and/or smoke inhalation. Most do not require in-patient hospitalization. Careful assessment of impact on local EDs should be considered in future city-accident planning. Some official warnings were widely misinterpreted or ignored. Public education on potential hazards and disaster preparedness targeted to populations at-risk should receive a high priority. Geographic information systems (GIS) may serve as useful tools for identifying demographics of populations at-risk for disaster planning and responses.

The TIGER Data Base Structure

Automated mapping requires a digital data source for map production. The files comprising the Topologically Integrated Geographic Encoding and Referencing (TIGER) System are the data source for all maps produced to support the 1990 decennial census. These files constitute a data base of national extent. The TIGER data base may be viewed at two levels, the conceptual level and the implementation level. The conceptual level provides the theoretical basis for the structure of the partition files comprising the data base, as well as an explanation of the relationships among the basic spatial objects. The implementation level reveals how the data base is organized as a specific network-oriented file structure within a general purpose file management system. Understanding the TIGER data base is necessary to comprehend its full capacity as a cartographic source, and to appreciate the reasons behind the design decisions that shaped the Census Bureau's fully automated field map production system and its computer-a...

Mapping from a Cartographic Extract

The Topologically Integrated Geographic Encoding and Referencing (TIGER) data base is the Census Bureau's primary source for map production. Collecting and chaining the TIGER data base's topological elements of 0-cells (points), 1-cells (lines), and 2-cells (areas) into meaningful cartographic objects is a major step in map creation. Rather than have each mapping application directly access the TIGER data base and independently perform the chaining process, the Census Bureau creates a secondary data base – a cartographic extract – from which mapping applications retrieve the coordinate strings of pre-chained cartographic objects. While developed initially to reduce overall computer processing requirements, the cartographic extract has additional implications for mapping applications, such as providing a conceptual framework for approaching the mapping task and enhancing the preproduction decision-making processes.

The TIGER system: a Census Bureau innovation serving data analysts.

This article describes the U.S. Census Bureau's TIGER (Topologically Integrated Geographic Encoding and Referencing) system, an automated geographic data base. The emphasis is on the availability of file extracts and their usefulness to data analysts. In addition to describing the available files, it mentions various applications for the data, explains the data limitations, and notes problems encountered to date.

Surveying and Mapping Beyond 2000

Two areas that demand major research commitments include real‐time surveying and mapping systems and geographic data bases. Future developments in these areas are vital to the computer integration of engineering processes, and the automation of construction. Global positioning and machine vision systems have already demonstrated the potential for performing real‐time surveying and mapping tasks. Some of the fundamental problems and benefits associated with geographic data bases have also been demonstrated by the Topologically Integrated Geographic Encoding and Referencing (TIGER) file system being developed by the U.S. Bureau of Census. However, many complex technical problems in both areas need to be resolved. In addition, the shortage of qualified surveying engineers and problems in surveying education can be major obstacles to the implementation of these new technologies.