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Executive Summary It is 2007 and a large public housing complex in Our Town is slated for demolition. Local officials consider alternative response initiatives for their city, including a community center for information, computer and job skills training, and a safe place for residents to bring their children to play and learn. Prospective residents will need information about the availability of new housing and transportation to jobs and childcare, health care, and other family services. They can get this information from the local library that provides access to HUD USER1 and to a related newsletter with up-to-date information on urban and community issues, as well as from other federal, state, and local government agencies, and private Internet data sources. City officials use these resources to gather the information they need to decide the best course. The information they gather includes lessons learned from previous housing initiatives about neighborhood impacts, the influence of regional housing markets on Section 8 voucher programs, and the implications of poverty concentration. They use tools available online to create maps to display this information, and they bring these maps to a town meeting about planning the new housing and community facilities. These data and tools are available in part because the U.S. Department of Housing and Urban Development’s (HUD) Office of Policy Development 1 An online source of HUD data and related information. See <http://www.huduser.org>.
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and Research (PD&R) uses geographic information systems (GIS)2 to analyze neighborhood change and market trends affecting the housing market, and to understand the relationship between transit and housing in lifting people out of poverty. Research done by HUD program offices, such as PD&R, has determined factors that contribute to the overall effectiveness of various housing programs, and characteristics of neighborhoods requiring incentives to promote the use of Section 8 low-income vouchers. HUD research is aimed at developing planning measures to meet the varied needs of dislocated residents and to improve the dispersion of low-income residents throughout the city and region. HUD and the local public housing officials work with partners in the Department of Health and Human Services and the Bureau of Labor Statistics to plan the best course of action. Local officials use GIS to determine where community block grants and new community facilities are most needed. The public housing residents had already used an online GIS program at their local housing authority. They understood that GIS can be used to consider housing options under the Section 8 program based on criteria that are important to Our Town residents, such as the vacancies’ proximity to quality schools, public transportation, entry-level employment opportunities, and special medical or social services. As a result of the information they have obtained from HUD, the local housing officials feel confident that investment in their chosen initiative will help provide decent, safe, and affordable housing, and support a safe and prosperous community. HUD AND A CHANGING URBAN AMERICA The story of Our Town highlights HUD’s role in providing data and information for communities across the nation. Created in 1965 to address civil rights, urban poverty, and the state of American cities, HUD is an agency with a mission to increase homeownership, support community development, and increase access to affordable housing free from discrimination (HUD, 2002). Many of the issues and problems that HUD must address are geographic in nature pertaining to: location, e.g., of housing and jobs; spatial relationships, e.g., among a neighborhood, a city, and a region; and the qualities of place, e.g., patterns of crime or environmental quality. To carry out their mission and to address complex issues of urban poverty and the declining state of American cities, HUD needs to collect and disseminate relevant data, carry out research, and partner effectively with other actors in the urban arena. 2 GIS is computer-based system for the collection, storage, analysis, and output of information that is spatially referenced (Obermeyer and Pinto, 1994).
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This will require technical capacity in the application of geographic information and considerable research expertise in spatial analysis. Although poverty occurs in higher proportions in rural areas than in urban areas, a much greater proportion of the U.S. population lives in cities. Therefore, poverty in the United States is largely urban, the product of processes working at the metropolitan and regional level. Economic growth at the regional level can raise housing costs in inner cities, shift investments from the city to the suburbs, and draw away the jobs that once provided income to the urban poor. Poverty in the central city has strong spatial characteristics. The poor are often spatially segregated from the middle class and physically removed from basic services, such as health care, childcare, and retail sales, and from cultural amenities such as libraries and museums. Both the percentage of inner city neighborhoods that are poor and the percentage of poor people living in those neighborhoods have risen in recent decades. Similarly, although poverty rates have declined for certain groups, the overall income gap between the rich and the poor is widening. The nature of urban and regional dynamics and of land and housing markets demand the deployment of modern data management techniques and analysis tools. Geographic tools and spatial analysis are useful to HUD for assessment of program effectiveness, understanding housing needs, and addressing broader issues of residential segregation and poverty, as well as infrastructure provision and access to services. GIS is a computer tool for understanding where things are located on the surface of the Earth. More than simply drawing maps, GIS allows analysts and citizens to answer questions such as: “Where is assisted housing located in my neighborhood?” “Which environmental hazards are within 5 miles of where I live?” “How can I get from where I live to where I can work?” “What is the best location for a day-care facility in my community?” GIS does this by permitting the integration and analysis of different kinds of data (e.g., environmental data and demographic data) and the display of the results in a visually attractive and graphically explanatory way. Used as a tool for data management and spatial analysis, the information derived from GIS is as accurate as the data that went into the system and as relevant as the questions posed. HUD serves the most disadvantaged and disenfranchised communities in the United States. This responsibility is shared among other federal, state, and local agencies, but because of its relationships with local communities and community groups, HUD has a unique ability to introduce local priorities into national dialogs and to provide support and encouragement so that local data meet national standards for inclusion in the national spatial data
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infrastructure (NSDI).3 The development of a parcel-level layer for metropolitan areas is particularly important to HUD, to the communities HUD serves, and to national initiatives, including the NSDI and other federal data initiatives. GIS for Use in Housing and Urban Development GIS provides a common framework—location—for information from a variety of sources. Because many of HUD’s programs have goals that can be identified by location, GIS can be a powerful tool for understanding and managing the results of HUD’s efforts (Box ES.1). The modeling and visualization capability of GIS provides a means of testing alternatives and turning data into information, and subsequently into knowledge. Urban, metropolitan, and state governments throughout the country have already turned to GIS as a means to deal with their own burgeoning demands for more effective and efficient service. Local governments and agencies will want to overlay and analyze the various HUD-developed indices, maps of HUD properties, Section-8 vouchers, etc. on top of other data sources such as base maps, aerial photography, and local analyses and forecasts used daily by a broad assortment of agencies for many urban planning, management, and service delivery purposes. This report provides HUD with direction in their effort to use GIS to better manage relevant data, address their clients’ needs, and understand the geographic processes underlying trends in housing markets and the evolution of urban issues in the United States. HUD’s current GIS programs include software-based initiatives (Community 20/20, E [environmental]-MAPS, R [research]-MAPS, and E [enterprise] GIS); research initiatives (programs in the colonias [informal settlements along the U.S.-Mexico border] and Global Urban Indicators); and GIS efforts in HUD’s 81 field offices across the nation. These programs are ongoing and evolving. Impediments to the success of these initiatives include lack of clear program ownership leading to discontinuity and inadequate data maintenance; lack of technical input into program design; failure to assess user needs and requirements; data dissemination via CD-ROM rather the Internet; lack of analytic capability in software-based systems, non-integration of relevant datasets, e.g., from other federal agencies or from local sources; and lack of technical and analytic capacity in local HUD staff and recipients of HUD grants. 3 The NSDI is defined as the technologies, policies, and people necessary to promote sharing of geospatial data throughout all levels of government, the private and non-profit sectors, and the academic community <http://fgdc.er.usgs.gov/nsdi/nsdi.html>.
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BOX ES.1 Benefits of GIS Some of the major capabilities of GIS that relate to HUD’s mission are described below: GIS provides the platform for the development of place-based data systems for measuring the effect of federally-supported housing programs and supporting housing policy decision making. Up-to-date, accurate information is needed to analyze issues and trends, to examine the effect of programs, and to support nationwide analysis. GIS provides the platform to conduct spatial analysis research to support policy making and impact assessment. Coupled with the growing availability of spatial analytical tools, GIS permits advanced spatial queries to inform policy making (e.g., “Show me all the housing units with children within 5 miles of a toxic waste site”). GIS provides a platform for collaboration among researchers, practitioners, and policy makers. GIS is a powerful visualization and communication tool that presents data in a map-like form that people can relate to and offers opportunity for collaborative work on interdisciplinary housing policy questions. GIS provides the technology to develop Internet-based tools to support housing decisions for low-income households. Information tools are currently available to higher income households. For example “realtor.com” provides detailed property and neighborhood information for houses available for sale in the private market. Johnson (2002) describes a prototype Internet-based GIS program designed to allow Section 8 participants to identify preferred communities. SOURCE: Ayse Can Talen, Fannie Mae, personal communication, February 21, 2002. In 2000, PD&R began to provide the technical expertise, research, and analysis for the development of the EGIS while the office of the Chief Information Officer continues to provide the infrastructure, an enterprise view of the data, and software for data dissemination. This report is intended to provide advice to PD&R and to provide strategic direction for HUD in the agency’s use of GIS.
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NRC COMMITTEE CHARGE In 2000, HUD asked the National Academies to evaluate their evolving GIS programs. These programs identify and standardize processes, formats, and specifications for Internet-based, interactive place-based inquiry applications using spatial data from HUD’s databases and other federal agency and local agency data sets. To provide perspective and guidance to the PD&R about current and future GIS research and applications and to provide strategic direction for the agency, the committee was asked to pay particular attention to PD&R’s specific mission goals including: ensuring the availability and accuracy of essential data on housing market conditions and trends; disseminating this information to the public; conducting research to expand the knowledge base needed for improved policy and practice nationwide; and working through interagency groups to achieve consensus on housing and urban issues. The staff and committee met with HUD staff and organized a workshop at the National Academies (Appendix B) to discuss a range of housing and urban issues. Workshop participants came from a variety of backgrounds (Appendix A) including federal agencies, state agencies, non-profit public policy groups at the national and local level, universities, and other research organizations. Topics included GIS initiatives at federal and state levels including HUD’s GIS programs, spatial analysis of neighborhood change, community-building using GIS, experience from HUD’s field offices, regional-scale analysis, and data applications and interoperability. The varied perspectives on data and research needs in urban and community planning informed the committee’s deliberations and enriched the report. Based on these discussions and presentations, the committee concluded that a forward-looking approach to GIS research and applications at HUD was more appropriate than a critical analysis of current GIS efforts many of which are ongoing and evolving. This report takes a regional or metropolitan area4 approach to housing and urban issues. The alternative is to play what Rusk (1999) calls the “insider game,” in which solutions to urban and neighborhood ills are sought in places where they arise rather than in regional processes that have local influence. The conclusions and recommendations of this study provide future direction for HUD and also have relevance for HUD’s partners in urban and community planning and decision making at local, regional, and national 4 A metropolitan area is a core area containing a large population nucleus, together with adjacent communities having a high degree of economic and social integration with that core (<http://www.census.gov/population/www/estimates/aboutmetro.html>). For a discussion, see Rusk (1999).
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levels in the United States. Although the committee recognizes that expertise in geographic research and analysis is necessary for the implementation of GIS to HUD’s mission, addressing workforce and organizational issues related to GIS application at HUD is not within the charge to the committee. Similarly, although the committee recognizes that data initiatives are costly and time-intensive, budgetary considerations are not detailed in this report. CONCLUSIONS AND RECOMMENDATIONS Ensuring the Availability and Accuracy of Essential Data HUD is responsible for providing data for housing and urban decision making nationwide. Steps to ensure the availability and accuracy of essential data include meeting Federal Geographic Data Committee (FGDC)5 data standards in all operations, taking steps to improve existing data, and creating an internal spatial data infrastructure (SDI). An internal SDI will permit the integration of local data from HUD’s partners and support an appropriate urban research agenda for the agency. This is tantamount to the creation of urban framework layers for the National Spatial Data Infrastructure (NSDI). HUD is well-suited to be one of the lead federal agencies in creating an urban SDI for the nation. 1. Be fully FGDC Compliant Recommendation: As a first step, HUD should meet federal data standards in all operations by: Participating fully in the FGDC and other federal initiatives to assure that agency efforts are consistent with the development of the NSDI; and Supporting its program participants’ efforts to provide operational data in FGDC standard format and to make these data available on the Internet along with other HUD data, subject to the limits of confidentiality. HUD datasets are collected from a variety of sources. Local, detailed datasets can be valuable to HUD’s local constituents but such data are often 5 The FGDC is comprised of 17 federal agencies and is responsible via an Executive Order of the President (Executive Order 12906, 1994) for the creation and maintenance of the NSDI . (<http://fgdc.gov>).
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incomplete and insufficiently documented. For optimal use and to comply with federal data policy, all HUD datasets should be accurate, consistent, and complete. Data from different sources often have to be integrated or “cleaned” spatially and thematically6 to maximize their utility. 2. Improve data quality Recommendation: As a first step, HUD should improve existing housing and related data. Existing data should be cleaned and checked for accuracy, consistency, and completeness. Data gaps should be identified and filled. HUD should adopt accuracy and documentation standards that build on FGDC data standards. To fully comply with the FGDC and other federal initiatives, HUD should develop an in-house, integrated data infrastructure. A spatial data infrastructure can: foster agency-wide data coordination, integration, sharing, and analysis; facilitate internal assessment of HUD programs, and analysis and reporting of federal urban investments; and aid in the delivery of services to HUD clients, such as metropolitan and local governments. 3. Create an agency-wide GIS for urban and community planning Recommendation: HUD should create an internal spatial data infrastructure for an agency-wide GIS to support an appropriate urban research agenda and to integrate locally derived data. An agency-wide GIS will permit inter alia the evaluation and assessment of the following: The strength of prior HUD investments; The results of HUD investment on the stability of neighborhoods, municipalities, schools, and school districts; The educational and economic opportunity in areas of potential HUD investments; and Future investment decisions that will foster health, education, economic opportunity, and residential and commercial stability of neighborhoods and regions. 6 Spatially, when different datasets are combined, boundaries and roads may be topologically inconsistent and require matching. Thematically, two datasets may have different attributes or coding necessitating matching or filling in of missing attributes.
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HUD could require public housing agencies to georeference their data, and the accuracy of local data geo-referencing can surpass the TIGER/Line files7 provided by the Bureau of the Census. HUD’s 81 field offices nationwide represent a wealth of local relationships. As a result of these relationships, HUD has significant access to local data and a singular ability to mandate national standards for local data. 4. Integrate local data into the agency-wide GIS Recommendation: HUD should develop mechanisms to accept and integrate relevant locally-derived data and georeference the data for integration in the agency-wide GIS. Specifically, HUD should spatially enable local data by performing address-matching of individual records at the finest scale using geographic coordinates. HUD should select, tabulate, analyze, and map relevant housing variables through a GIS at multiple relevant geographic scales (census block, block group, and tract; place, county, and metropolitan area). PD&R should take the lead within HUD in efforts to integrate grantee and other data at different levels: parcel, neighborhood, municipality, school and school district, metropolitan area, state, and national. In this way, data can be made available at multiple scales on a broad range of urban topics, including real estate market conditions, neighborhood educational and economic opportunity, crime, local fiscal capacity, tax rate condition, and environmental risk. Data can be disseminated via the Internet, saving HUD data-users the time-intensive work of data integration. Create an Urban Spatial Data Infrastructure for the NSDI This report discusses the growing demand for accurate, relevant data on housing and urban issues; the research required to better understand urban and neighborhood dynamics; the information needed to inform urban and housing policy nationwide; and the evolving roles of diverse partners in urban and community planning. With the agency’s GIS programs to monitor 7 Topologically Integrated Geographic Encoding and Referencing System (TIGER) (see <http://www.census.gov/geo/www/tiger/index.html>).
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modern urban conditions and respond to emerging trends in housing and urban issues, HUD is well-positioned to be one of the lead federal agencies in providing and managing urban framework data layers for the NSDI. Because HUD carries out its work within a constellation of agencies having varying responsibilities for urban and community issues, the USDI should be developed in partnership with those agencies. That vision for the future is encapsulated in the creation of an urban spatial data infrastructure (USDI) that includes parcel-level data and relevant environmental and socioeconomic data. 5. Create a USDI Recommendation: HUD should promote the development of a parcel-level data layer and other urban framework layers to create a USDI as a component of the NSDI for housing and urban development. The federal government should make available resources commensurate with this task. Core elements of the USDI can include: Public and federally-assisted housing data; Tenant and housing characteristics; Parcel-level data; Locally updated TIGER files; and Environmental and socioeconomic data. 6. Support development of data centers to support the USDI Recommendation: HUD should encourage and support the development of local, metropolitan, and regional data centers to facilitate local data coordination, use, and training toward the creation of a USDI. Disseminate Data and Information to the Public The Internet is a powerful means for disseminating information to the public. People without a home computer may have access to the Internet at local libraries or community centers. HUD data users range from technically sophisticated urban researchers to local advocacy groups who have little experience with spatial data or technologies. These users have a wide range of data needs and research and analytical capabilities, but all clients and all
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applications require information about data quality and usability including data interoperability.8 7. Create web tools Recommendation: HUD should continue to develop a spectrum of tools to meet users’ needs. For users with limited financial or technical resources, HUD should provide web-based mapping of HUD data and related information. For more advanced applications, HUD should develop tools for flexible querying, extracting and downloading data, including standard file formats for exchanging data. HUD is responsible for providing information about local housing conditions and making basic data on urban and housing issues available to the public. A user-friendly, web-based GIS is an efficient means of data dissemination but the development of a well-designed web-based GIS is a long-term process, and user input is critical. Confidentiality concerns inhibit local sharing and public access to data at needed resolutions. 8. Reach out to users Recommendation: To improve dissemination and promote the use of spatial data, HUD should: Involve users in design of the web-based GIS; Sponsor conferences and workshops for clients and partners about using spatial data; Support online groups for HUD spatial database users; and Produce an Internet newsletter devoted to spatial data and analysis. PD&R is well-positioned to: Work with HUD clients and data users to derive the most appropriate GIS designs and to identify needed data and functions. 8 Examples of exportable files that are interoperable and can be transferred from one type of software to another include shape files, E00 files, d-base, and tab-delimited ASCII.
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Manage data confidentiality. For some sensitive data, PD&R will need to develop a policy on releasing confidential data as well as algorithms to suppress sensitive data to protect privacy. Take the lead in establishing a node for housing and related economic and demographic data in the NSDI’s National Geospatial Data Clearinghouse. Support the functions of an agency-wide enterprise GIS across all relevant HUD units. GIS has multiple capabilities and each is valuable in its own way, for basic mapping, data handling, spatial analysis, etc. Local governments and community groups are beginning to take advantage of GIS and are developing capacity to do more sophisticated spatial analysis, yet, at present, most local groups use GIS only for visualization. Visualization allows users to view a few variables but does not support analysis of complex issues facing these communities. 9. Promote users’ spatial analytic capacity Recommendation: To help community groups and local governments develop spatial analysis capabilities, HUD should support the development of tools for spatial analysis. PD&R should support the development of online, down-loadable analytical tools that incorporate multivariate techniques. Expanding the Knowledge Base for Urban Policy and Practice HUD is a federal agency with a strategic goal to provide a decent, safe, and sanitary home and equal opportunity for every American. Creating a vision for the future of urban America is an appropriate goal for the agency. This implies a broad urban research agenda. 10. Address research priorities Recommendation: HUD should expand its research portfolio to emphasize the following urban issues: The spatial distribution of poverty in the United States; The changing demographics of American neighborhoods; and Market trends that affect the U.S. housing market.
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To help local governments and non-governmental groups develop their policy analysis capabilities, HUD needs internal spatial analysis capabilities and a systematic approach to monitor metropolitan housing market conditions and trends. 11. Support development of data and tools for monitoring housing markets Recommendation: To monitor and analyze metropolitan housing market conditions and trends, HUD should: Identify and adopt means and formats for routine collection of housing-related data relevant to user needs and agency mission goals at regular intervals, along with development and adoption of a standardized method for data analysis. Perform research toward the development of spatial analytic tools to address quality-controlled price indices and variations in local context, and for time-series and comparative analyses between and among places. An agency-wide GIS can be used to examine urban issues and housing trends across multiple geographic scales from neighborhood to regional and at various levels of spatial resolution in a metropolitan context. HUD can work with internal datasets and with those produced by partners; investigate the spatial structures and social processes at work in a metropolitan context that underpin many community concerns with housing and investment; and engender participation among partners with interests in policy analysis, research, and community building. An appropriate research agenda can promote the effectiveness of HUD programs. 12. Engage in research to promote program effectiveness Recommendation: HUD should incorporate into their research agenda and prioritize spatial analysis of the following urban issues at the regional and metropolitan-level: Housing market conditions and trends; Effects of these conditions on HUD program design and implementation; HUD program effectiveness and impacts on communities; Interactions among communities in metropolitan areas;
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Dynamics of neighborhood change including poverty concentration, racial segregation, and neighborhood effects; and Housing and labor market interactions including regional and cross-border analyses. Develop and Use Partnerships HUD can use GIS to facilitate the agency’s efforts to interact with organizations beyond its institutional boundaries to build vertical and horizontal networks to share data, discuss housing and urban issues, and ultimately create public policy to respond to these issues. HUD’s relationships with state and local groups enable the agency to promote public participation in decision making, narrow the divide that prevents disadvantaged communities from participating in urban and housing policy setting, and bring the capabilities of GIS to bear on issues of local and national relevance. 13. Use partnerships to promote inclusion of local data and perspectives Recommendation: HUD should facilitate the integration of local datasets and the development of mapping applications using the shared data; encourage public participation in the development and use of local datasets; and partner to develop local and in-house GIS capability. HUD can partner to link the methodological expertise housed in universities and the local expertise of communities and local governments. Local agencies can develop research questions, analyze research results, and develop appropriate local solutions. PD&R could work with local governments and community groups to define new ways for researchers to extend their skills to building local capacity and addressing local needs. 14. Partner with universities Recommendation: PD&R should build relationships with university and unaffiliated researchers to engender participation of local groups in policy analysis, research, and community building; and to promote the use of advanced spatial analysis in urban housing policy research to address the complexities of modern urban dynamics. GIS provides HUD with an opportunity to introduce housing and urban issues onto the national agenda. HUD can partner with other federal agencies having responsibility for providing and managing data relevant to urban,
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community, and housing issues. Transportation, social services, education, and employment are intimately linked to housing and community development. 14. Partner with other federal agencies Recommendation: PD&R should take the lead within HUD to build interagency relationships with federal data-providing agencies that have responsibilities related to urban and community issues, notably the Department of Transportation, the Department of Health and Human Services, and the Environmental Protection Agency. SUMMARY Steps to ensure the availability and accuracy of essential data include meeting FGDC data standards in all operations, improving existing data, and creating an internal spatial data infrastructure. This work is tantamount to the creation of urban framework layers for the NSDI. HUD is well-suited to be one of the lead federal agencies in creating an urban spatial data infrastructure for the nation. To disseminate data and information to the public, HUD should incorporate users’ needs, priorities, and abilities in the design of an agency-wide GIS and associated spatial analytical tools, and develop methods of managing data confidentiality concerns. HUD is also responsible for conducting research to expand the knowledge base needed to improve urban policy and practice nationwide. To this end, HUD should expand their research to prioritize spatial analysis of poverty in the United States, the changing demographics of American neighborhoods, and economic market trends that affect the U.S. housing market. To achieve these goals, HUD should collaborate effectively with local clients to create local data sets, link university researchers with these clients to promote the devolution of analytical capabilities to communities, and partner with other federal agencies having responsibility for urban, community and housing issues in the United States. The activities described above require trained personnel to lead and manage. To address these recommendations HUD should consider the addition of spatial data development specialists who have expertise in GIS, spatial analysis, geographic research, algorithm development, and spatial data manipulation. HUD faces formidable challenges including the decline of the inner city, entrenched poverty, and homelessness that resist simple solutions, as well as administrative and resource constraints in implementing spatial data initiatives. This report offers HUD a vision of the future of GIS for housing and urban development.
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Representative terms from entire chapter: