2000115-Strengthening-Communities-with-Neighborhood-Data

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Index 427 comprehensiveness, in neighborhood improvement initiatives, 156 CompStat (New York City), 147n.3 concentrated inspection areas (CIAs), 215 condemned properties, 207, 208 conditional plots, cluster maps linked to, 379 confidence intervals, neighborhood indicators and, 292 confidentiality data integration, 176 school attendance data, 281 Connel, James P., 144 Consolidated Plan Data (HUD special tabulations), 75 constituent relationship management (CRM), 41, 52 311 systems and, 45–47 Consumer Financial Protection Bureau, Home Mortgage Disclosure Act query system, 392 context indicators, 136 contextual causality, interdependent levels of neighborhood effects and, 384n.3 “contextual causality” theory, 376 contiguity, quantifying, GIS tools and, 287 Continuum of Care lead organizations, 86 contribution analysis, 376 core indicators, tracking, in Eastern North Philadelphia, 164–165 correlation coefficients, areal units and modifiability of, 370 cost-benefit analysis, 140 Coulton, Claudia J., 9n.2, 86, 170, 171 Council on Geographic Information (Minnesota), 59 counterfactuals, refined, spatial concepts and crafting of, 306 covariates cluster randomized trials, 303 interrupted time series designs, 306 matched neighborhood designs, 304 Creative Commons license, 105 credit scores, 92 crime data, 30 crime rates, 195, 196 cluster analysis, 295 neighborhood effect hypothesis, 346 NNIP partner data holdings, 2013, 88 supportive housing developments in Denver, 306 vacant housing, 157, 158 crime-related analyses, address-level data and, 371 cross-sectional data, spatial dependence vs. spatial heterogeneity, 373–374 cross-sectional linear models, key potential contribution of, 381 cross-site surveys, implementation costs, 180n.24 crowdsourcing, 53, 98, 383 Cuyahoga County, Ohio, Development Department, 209 Cuyahoga Land Bank, Ohio, 209, 210 foreclosures and property acquisition, 215 mission, 214 strategic decisionmaking, 213–215 Cuyahoga Metropolitan Housing Authority, 209 cyber-GIS frameworks, 368 Dallas, Texas, neighborhood disparities addressed in, 233–238 Dallas Morning News, 233, 234, 236, 237, 238 data. See also local government data; neighborhood data and community change availability of, advances in, 391–392 comparing across neighborhoods, importance of, 220 geo-referenced, 367 geo-statistical, 376 health care, smarter use of, 238–242 investing in, 128–129 machine-readable, 121–122 mobilizing around key issues with, 156–158
428 Index data (continued) oversight of government operations and, 49 quality and accessibility of, changes in, 73 school attendance, mining for causes of chronic absence, 266–267 spatial, 376–377 strengthening communities and role of: review, 152–163, 166–177 data analysis, efficient government and, 41 data analysts, 49 data assembly, by local data intermediaries, 29, 30 database tools, 124 data capacity, expanding in local communities, payoff with, 409 data centers, reconfiguration of, 49 Data Collaborative (Detroit), 92 data collection and aggregation, specifying units for, 288 data-driven action coalitions, 224–227 data-driven decisionmaking educating future policymakers, 401, 404–405 explicit support for, 406 Data Driven Detroit, 92 data-driven performance programs, 50–52 DataFinder (Twin Cities), 60 data for neighborhood indicators, 74–101 data standards, 100–101 federal government data, 74, 82–85 household surveys, 95–96 neighborhood scores and metrics, 96–98 new types of, 98–99 observational surveys, 94–95 open-government data, 98 primary data sources, 93–96 proprietary data, 91–93 secondary data sources, 74–93 state and local government data, 85–86, 89–91 data.gov, 98, 104 DataKind, 108, 391 data layers, brownfields properties and, 44 data literacy, promoting, 132 data mining, 311 DataPlace website, 103 data portals commercial portals, 105–106 delivery, visualization, and analysis with, 102–107 government portals, 105–106 nonprofit portals, 102–105 data processing tools, simplifying, 130–131 data quality, increasing, 129 Data Quality Campaign, 21, 88 data scientists, 49 data sharing local government agencies, 16 privacy and security concerns, 53 data sharing in the Twin Cities (Minnesota), 57–70 intermediaries, 61–68 local emergence, 60–61 overview, 57–58 remaining barriers, 69–70 state initiatives, 58–60 trends in applications, 68–69 data sparseness, addressing, 287, 291–292 data standards geographic information systems and, 43 neighborhood indicators and, 98–99 data transformation expanding, 401, 404 by local data intermediaries, 29, 30–31 Davis, Sandra, 45 DBF documents, 121 Decennial Censuses, 20, 74, 75, 80, 82, 83, 90, 96, 104 decisionmaking data-driven, 146 indicators and, 135–147 delinquency, distressed, disinvested neighborhoods and, 6 Dell Children’s Medical Center of Central Texas, 231 democracy citizen participation between elections in, 18 as problem solving, 19
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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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ESSAY From Tornadoes to Transit How
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Institutional Context 59 profession
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Institutional Context 61 graphic, h
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Institutional Context 63 department
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Institutional Context 65 This infor
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Institutional Context 67 By identif
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Institutional Context 69 embedded i
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Institutional Context 71 8. For mor
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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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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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Page 385 and 386: 376 Strengthening Communities with
Page 421 and 422: 412 About the Authors served as dir
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Page 431 and 432: 422 Index alternative scenarios, fo
Page 433 and 434: 424 Index Center on Urban Poverty a
Page 435: 426 Index Community Development Blo
Page 439 and 440: 430 Index early warning systems, no
Page 441 and 442: 432 Index funders, data-driven deci
Page 443 and 444: 434 Index homeless students, chroni
Page 445 and 446: 436 Index Koschinsky, Julia, 307, 3
Page 447 and 448: 438 Index Minneapolis Inspections o
Page 449 and 450: 440 Index Neighborhood Early Warnin
Page 451 and 452: 442 Index OnTheMap, 81, 104 open da
Page 453 and 454: 444 Index privacy concerns (continu
Page 455 and 456: 446 Index Sawicki, David, 10n.2 sca
Page 457 and 458: 448 Index strategic investment init
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