GIS for Housing and Urban Development by National Research Council, Division on Earth and Life

By National Research Council, Division on Earth and Life Studies, Board on Earth Sciences and Resources, Committee on Geography, Committee on Review of Geographic Information Systems Research and Applications at HUD: Current Programs and Future Prospects

The document describes strength functions of geographic details structures (GIS) and spatial research by way of HUD’s workplace of coverage improvement and examine for realizing housing wishes, addressing broader problems with city poverty and group improvement, and bettering entry to details and companies by means of the various clients of HUD’s information. It bargains a imaginative and prescient of HUD as a tremendous participant in offering city information to federal tasks in the direction of a spatial information infrastructure for the country.

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All federal agencies are required to work with the FGDC. 5 The concept of a framework and a clearinghouse ensure that data now being developed are made available to many users. The National Geospatial Data Framework is designed to be a collaborative effort. 1). The framework data represent the best available data that are certified, standardized, and described according to a common standard. They provide a foundation on which organizations can build by adding their own data. 6 The appropriateness of the various spatial data can then be determined from their metadata descriptions, which are required for all datasets included in the Clearinghouse.

5 • American Housing Survey: provision of national and metropolitan estimates of housing conditions and zoned data. • Policy Development: research leading to improved policy in support of mission goals. • Research and Evaluation: post-program evaluation for fair housing programs, assistance programs, and mortgage market assessments; and mapping of racial and economic segregation, and mapping. S. Bureau of the Census; Fannie Mae and Freddie Mac home mortgage data collections; and the State of the Cities Report6 from which HUD creates value-added products on employment, education, and other indicators of urban quality-of-life.

Cost is generally higher with higher data quality. Unfortunately, data error is impossible to entirely eliminate, and it will always exist. Therefore, it is important for an organization to maintain a balance between cost and quality. The objective in this case is to “manage” rather than try to eliminate the error by acknowledging some degree of uncertainty through various statistical means (Aronoff, 1989). The above synopsis of the 13 datasets shows that there are rich sources of data that can be utilized for housing research; however, it reveals three issues.

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