2000115-Strengthening-Communities-with-Neighborhood-Data

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Progress in Data and Technology 79 Environment EPA Smart Location Database Block group Unknown 2006–2010 ACS, 2010 LED, various others A collection of indicators from various sources that help to measure location efficiency. Includes indicators related to housing density, diversity of land use, neighborhood design, destination accessibility, transit service, employment, and demographics EPA Enforcement and Compliance History Online (ECHO) Point Varies by data source Varies by data source A collection of many individual EPA data sources information on regulated facilities related to air emissions, surface water discharges, hazardous waste, and drinking water systems. Contains permit data, inspection dates and findings, violations, enforcement actions, and penalties assessed Toxics Release Inventory Point Varies based on data source 1997–2014 Information about hazardous waste sites, facilities that produce and release air pollutants or toxics, and permits to discharge to water Note: For updated information and web links, see http://www.neighborhoodindicators.org//library/catalog/list-national-data-sets-small-area-data
80 Strengthening Communities with Neighborhood Data mentioned only the Decennial Census, even though several of the new data series had started by that time (Kingsley 1999). Table 3.1 lists many of the federal small-area datasets. The remainder of this section highlights a few of these data sources that represent major trends or important innovations in data production or dissemination. The Home Mortgage Disclosure Act (HMDA) is one example of a major advancement in data collection and provision. HMDA was enacted by Congress in 1975 and implemented by Federal Reserve Board regulations. HMDA legislation requires most lenders to report home mortgage applications with loan attributes, applicant characteristics, and the census tract of the property. Beginning in 1992, lenders were required to make the loan-level data public, and the Federal Financial Institutions Examination Council began to produce CDs with an easyto-use system to query the data (McCoy 2007). Although the HMDA was motivated by the need to monitor financial institutions’ investments in communities and identify potentially discriminatory lending patterns, it also enabled the creation of indicators on the race and gender of new borrowers, mortgage activity, and trends in loan amounts (Pettit and Droesch 2008). The availability of these indicators spurred a substantial body of research on the mortgage markets and empowered activists to reveal racial disparities in lending. When combined with a list of lenders specializing in subprime lending, this dataset played a critical role in documenting the spatial and racial patterns of subprime loans during the late 1990s and early 2000s (Immergluck and Wiles 1999; Treskon, Kornil and Silver 2003). Clearer definitions implemented in 2004 improved the ability to identify these loans from all lenders. Beginning in 2009, HMDA data were distributed online. Another example of a new data series that is useful for neighborhood and community information is the US Census Bureau’s Longitudinal Employer–Household Dynamics, which was launched in 2002. This data series uses sophisticated statistical and computing techniques to combine federal and state administrative data on employers and employees with other Census Bureau data, maintaining confidentiality protections for individuals and firms. Under the Longitudinal Employer–Household Dynamics, the Local Employment Dynamics Program is a voluntary partnership between state labor market information agencies and the US Census Bureau to develop new information about local labor market conditions. By relying on existing administrative data, the Census Bureau produces the new dataset at low cost and with no added respondent
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Efforts to address the problems of
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ABOUT THE URBAN INSTITUTE The nonpr
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iv 4 A 5 Using 6 Using 7 Advances C
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Acknowledgments We would like first
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1 Introduction to the Field The Inc
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Introduction to the Field 3 into an
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Introduction to the Field 5 to set
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Introduction to the Field 7 in chap
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Introduction to the Field 9 improve
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Introduction to the Field 11 Streng
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14 Strengthening Communities with N
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Table 2.1. (Continued) City/Metro N
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4 A Framework for Indicators and De
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5 Using Data for Neighborhood Impro
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Using Data for Neighborhood Improve
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New Insights on Neighborhood Change
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ESSAY Neighborhood Data and Locally
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ESSAY Cutting through the Fog Helpi
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6 Using Data for City and Regional
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ESSAY Creating and Using Neighborho
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ESSAY A Solvable Problem Hidden in
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ESSAY The Dynamic Neighborhood Taxo
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412 About the Authors served as dir
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About the Essayists Riccardo Bodini
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About the Essayists 417 community a
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About the Essayists 419 effects of
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422 Index alternative scenarios, fo
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424 Index Center on Urban Poverty a
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426 Index Community Development Blo
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428 Index data (continued) oversigh
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430 Index early warning systems, no
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432 Index funders, data-driven deci
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434 Index homeless students, chroni
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436 Index Koschinsky, Julia, 307, 3
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438 Index Minneapolis Inspections o
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440 Index Neighborhood Early Warnin
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442 Index OnTheMap, 81, 104 open da
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446 Index Sawicki, David, 10n.2 sca
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448 Index strategic investment init
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450 Index Twin Cities (Minnesota) d
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2000115-Strengthening-Communities-with-Neighborhood-Data

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