A GIS-Based Model to Determine Site Suitability of Emergency Evacuation Shelters

In recent years, the increase in the number of hurricanes and other costal hazards in the US pose a tremendous threat to the residents of coastal states. According to the National Hurricane Center, Florida is the most vulnerable coastal state to hurricanes. Mitigation policies have been formulated to reduce mortality and provide emergency services by evacuating people from the hazard zone. Many of these evacuees, particularly the elderly or lower income populations, rely on evacuation shelters for temporary housing. Because of the cost and limited use, evacuation shelters are almost exclusively dual use shelters where the primary purpose of the facility is for some other public function (e.g. school, hospital, etc.). In 2000, the estimated shortage of public shelter spaces in Florida was about 1.5 million. The purpose of this study was to rank the existing and candidate shelters (schools, colleges, churches and community centers) available in the state based on their site suitability. The research questions examined in this study include: (1) How many candidate shelters are located in physically suitable areas (e.g. not in a flood prone area, not near hazardous facilities, etc.)?; (2) How many existing shelters are located in physically un suitable areas, but in socially suitable areas (situated in areas with demand)?; (3) How many alternative existing and/or candidate shelters with high/very high physical suitability are located near physically un suitable existing shelters and thus, may be better choices for a shelter?; and (4) How many existing shelters located in physically un suitable areas are not near alternative existing and/or candidate shelters? A Geographic Information System‐based suitability model integrating Weighted Linear Combination (WLC) with a Pass/Fail screening technique was implemented for the 17 counties of Southern Florida. It was found that 48% of the existing shelters are located in physically unsuitable areas. Out of all the candidate shelters, 57% are located in physically unsuitable areas. For 15 of the existing shelters in unsuitable locations, no alternative candidate or existing shelter with medium to high physical suitability exists within 10 miles (16.1 km).     

TIGER/SDTS: Standardizing an Innovation

The explosion of computer processing capabilities for manipulating geographic data has produced a concomitant increase in the number of geographic data file formats available. The many formats make it difficult to exchange and manipulate geographic data from several sources, and sometimes even from the same source. The U.S. Bureau of the Census has been a contributor to the “Yet Another Geographic File Format” movement over the past two decades with its Address Coding Guides (following the 1970 decennial census), the GBF/DIME-Files (following the 1980 decennial census), and four different versions of its TIGER/Line files at various times during the 1990 decennial census cycle. The TIGER data base is a massive computer file that provides geographic information about the entire United States and its territories in great detail, down to the individual city block and its component boundary features. Its value to more than Census Bureau activities is enormous. To enhance the value of the TIGER data base, and to make it easier to use, the Census Bureau is releasing the file in the new Spatial Data Transfer Standard (SDTS) format. The benefits of a standard transfer format are manifold. This paper discusses some of the intergovernmental activities that were required before the exchange standard was adopted and some of the problems of implementing the standard within the Census Bureau. The Census Bureau is not alone in its decision to release geographic data files in the SDTS format, and some of the benefits of using the standard for exchanging data among agencies also are described.     

Data visualization at the US census bureau – an American tradition

Recent developments in data visualization, developer Application Program Interfaces (APIs), and web services reinforce a long American tradition of statistical mapping and innovation at the US Census Bureau. Consistent with other international statistical agencies, the Census Bureau has used contemporary innovations in statistical mapping and data visualization to disseminate national census results for over 14 decades. The US Census Bureau’s data products and analyses have enabled decision makers and the public to access census results quickly and easily. The new information technology environment requires the Census Bureau to more rapidly expedite these results and deliver mapping products to new customers as well as to its traditional data consumers. The Census API has empowered developers and commercial companies to test the limits of a new emerging world of big data solutions. This article presents some of the most recent data visualization products from the Census Bureau, including an expanding Data Visualization Gallery to merge geospatial information and statistics on the map.     

Cartographic Support for the 2010 Decennial Census of the United States

As this article is published, the U.S. Census Bureau is completing work for the twenty third decennial census of the United States. Once again, the MAF/TIGER system served as the geospatial infrastructure supporting numerous census operations and data collection, tabulation, and dissemination activities. From data collection to data dissemination we trace the recent activities of the 2010 Decennial Census of the United States to illustrate the role maps and geospatial data play in an increasing variety of public and private sector activities across the nation. To ensure a successful 2010 Census, millions of maps had to be created. This article will give an overview of the automated mapping system designed to create these maps. This includes a discussion about associated software needed and the variety of map types that were developed. Finally, future map production and geospatial activities at the Census Bureau will be discussed.     

The TIGER System: Yesterday,Today, and Tomorrow

The TIGER System is many things to many people. At this juncture, the TIGER System has fulfilled the precensus geographic support functions for which the Geography Division designed it. During the next 18 months, the TIGER System will support a number of functions needed to complete the tabulation of the collected data and make those data useful to the numerous constituencies that carry out the myriad tasks that define our lives. Simultaneously with the use of the TIGER System to support the data tabulation and dissemination missions of the Census Bureau, work will be under way to define a framework for the future of this bold new product – and this future looks bright. If the TIGER System is to be judged truly useful outside the Census Bureau, similar planning will need to be going on in offices and institutions across America. This is true especially in the context of geographic information system (GIS) applications involving the digital products of the TIGER System and the demographic data products of the 1990 census.     

TIGER空间数据模型及空间数据标准问题探讨

以TIGER这种既成事实的空间数据格式为例,详细介绍了TIGER的数据模型及文件组织形式,对TI-GER/Line如何描述空间对象信息进行分析,把这种格式作为空间对象拓扑模型实际实施的一种范例作以详细介绍,以起到抛砖引玉的作用,使读者能够更深入地了解空间对象表示的基本要求,对未来空间数据标准化起到一定的参考作用。     

Positional Accuracy of Spatial Data: Non-Normal Distributions and a Critique of the National Standard for Spatial Data Accuracy

Spatial data quality is a paramount concern in all GIS applications. Existing spatial data accuracy standards, including the National Standard for Spatial Data Accuracy (NSSDA) used in the United States, commonly assume the positional error of spatial data is normally distributed. This research has characterized the distribution of the positional error in four types of spatial data: GPS locations, street geocoding, TIGER roads, and LIDAR elevation data. The positional error in GPS locations can be approximated with a Rayleigh distribution, the positional error in street geocoding and TIGER roads can be approximated with a log‐normal distribution, and the positional error in LIDAR elevation data can be approximated with a normal distribution of the original vertical error values after removal of a small number of outliers. For all four data types considered, however, these solutions are only approximations, and some evidence of non‐stationary behavior resulting in lack of normality was observed in all four datasets. Monte‐Carlo simulation of the robustness of accuracy statistics revealed that the conventional 100% Root Mean Square Error (RMSE) statistic is not reliable for non‐normal distributions. Some degree of data trimming is recommended through the use of 90% and 95% RMSE statistics. Percentiles, however, are not very robust as single positional accuracy statistics. The non‐normal distribution of positional errors in spatial data has implications for spatial data accuracy standards and error propagation modeling. Specific recommendations are formulated for revisions of the NSSDA.