data management and spatial analysis but the information derived from GIS is only as accurate as the data that went into the system in the first place, and as relevant as the questions posed. Understanding housing markets and the demand and supply of different types of housing is important. These gaps in data and staffing leave local HUD agencies with inadequate information for making decisions about how and where they should allocate their resources for maximum effectiveness.

Using GIS to collect, store and deliver data, and ensuring the quality of the data are important, but the application of these data to policy analysis and planning depends on the relevance of the research questions posed. In addition, the relevant data (e.g., Census of Population and Housing; American Housing Survey, Department of Labor employment data, satellite imagery, EPA air quality data, DOT traffic and accident data, airport noise exposure data) have been collected for many different applications and must be adapted if HUD’s clients and partners are to use them. Data have meaning only within the context of an argument or hypothesis about how something works.

This report adopts a regional/metropolitan-level focus for addressing urban and housing issues, as described in Chapter 1. HUD can expand its research at the regional and metropolitan level to include geographic analysis of the spatial dimensions of urban poverty, the dynamics of neighborhood change, and market trends that affect the U.S. housing markets. This chapter discusses the potential of an expanded urban research agenda that is appropriate for HUD as a federal agency and identifies priorities for geographic analysis of urban and housing issues.

THE SPATIAL DIMENSIONS OF URBAN POVERTY

Understanding urban poverty requires attention to processes at the regional and metropolitan levels that result in inner-city poverty. GIS can help integrate data from multiple levels to facilitate regional analyses. The dynamics of neighborhood change and the factors that concentrate poverty in urban areas can also be analyzed using geographic data and tools. The poor are often spatially segregated from the middle class and physically removed from basic services, such as health care, childcare, and retail, 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 (Jargowsky, 1997). Similarly, although poverty rates have declined for many groups, the income gap between the rich and the poor is widening (Lichter and Crowley, 2002). Understanding the spatial dimensions of urban poverty and neighborhood change is essential to carrying out HUD’s mission.



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