2000115-Strengthening-Communities-with-Neighborhood-Data

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Index 429 “democratizing information,” 3, 32, 155 demographics, Dynamic Neighborhood Taxonomy typology and, 320 demolitions, 207, 208, 212, 213, 214 Deng, L., 305 density, quantifying, GIS tools and, 287 Denver, Colorado, supportive housing developments and crime rates in, 306 dependent variable, spatial autocorrelation and, 380 description, neighborhood indicators studies and, 285 Deshuk, Alex, 48 destination choices, residential dissatisfaction framework, 352 Detroit, Michigan access to mental health and substance abuse services in, 297–298 hazardous waste exposure in, 299 Detroit Office of Foreclosure Prevention and Response, 92 Detroit Residential Parcel Survey, 92 Deutsche Bank, NEO CANDO data in lawsuit filed against, 212 “deviant behavior,” neighborhood effect hypothesis and, 346 diabetes patients, in Camden, New Jersey, 239, 240 difference-in-difference-type models, spatial estimators for, 381 digital divide, 128 community access to information, 119 narrowing, 109 disadvantaged neighborhoods looking at through equity lens, 221 negative outcomes for residents of, 220 disamenities, assessing geography of, 287–288, 296–299 discrimination, ethnic segregation and, 349 disk storage, 124 disparities in human well-being, neighborhood indicators and, 8, 220 displacement effects, spatial heterogeneity and, 368 dissemination of data, by local data intermediaries, 29, 31 distance, quantifying, GIS tools and, 287 distance weighting method, 297 distressed neighborhoods adverse outcomes for children, 6 in Cleveland, Ohio, 295 innovations in service delivery, 52–53 NNIP commitment to, 27–28 persistent problem of, 1 District of Columbia Promise Neighborhoods Initiative (DCPNI), 173, 174 diversity of needs, barriers to information and, 120 Doff, W., 341 domains, small-area indexes and, 293 Dombrow, Jonathan, 338n.2 Dun and Bradstreet, 92 Durbin, Dick, 199 Durland, M., 377 dynamic modeling, 311 Dynamic Neighborhood Taxonomy project (DNT), 372, 375 database, 367 launching, 318 Repeat Sales Index, 319, 329 Dynamic Neighborhood Taxonomy typology, 320–335 applying, 329, 332–335 drivers of change methodology, 338n.5, 338n.6 factors defining different layers within, 339n.10 heat map, 324 limitations and opportunities for improvement, 335, 337–338 methodology, 322–324 neighborhood types and subtypes, 327 overview, 320–322 sample output, 329 sample type profile, 330–332 structure, 324–326, 328 transition matrix, 328 Early Childhood Longitudinal Study— Kindergarten Cohort, 266
430 Index early warning systems, nonprofit portals, 101 East Multnomah Soil and Water Conservation District (Portland, Oregon), 260 East Wilkes Neighborhood Association (Portland, Oregon), 260 ecometrics, 286 Economic Census, 104 economic development practice, usefulness of neighborhood typology for, 320, 338 eCon Planning Suite, 105, 392 education engagement and, 138, 144–145, 146–147, 152, 394 NNIP partner data holdings, 2013, 87 educational attainment improvement in place and, 334 information intelligibility and, 127 neighborhood effect hypothesis and, 346 ego-centric neighborhoods, 289, 290 8 Principles of Open Government, 122 elections, 18 Electronic Records Management, 35n.2 e-mail, 49 empirical Bayes estimators, 291, 292 empirical imperative, 187 employed residents, in Eastern North Philadelphia, 164–165 employment improvement in place and, 334 neighborhood effect hypothesis and, 346 employment-focused intervention, Jobs-Plus employment program, 303 Empowering and Strengthening Ohio’s People (ESOP), 210 empowerment evaluation, 188 information and, 187 Empowerment Zone (EZ) program, evaluating impact of, 304–305 enabling technologies, new, 124–126 endogeneity biases, 356–358, 359 endogeneity problem, description of, 356 engagement, education and, 138, 144–145, 146–147, 152, 394 Enterprise Community Partners, 150 enterprise GIS, datasets and return on investment in, 43 Environmental Justice Action Group (Portland, Oregon), 255 Environmental Protection Agency, 122, 225 DBF files, 121 Enforcement and Compliance History Online (ECHO), 79 Smart Location Database, 79 Toxic Release Inventory, 299 environmental toxins, 297 EPIC (Economic Partners Investing in Communities) Dallas, 237 Equifax, 92 equity, defining, Regional Equity Atlas and, 251–252 errors data sparseness, 291 neighborhood indicators, 288 error term, spatial autocorrelation and, 380 Esri, 106, 392 ArcMapsoftware, 101 spatial statistics toolbox, 378 ethnic segregation, 349 Euclidean distances, food access measures, 298 European Union, open-data platforms in, 122 Eurostat, 106 evaluators of neighborhood interventions, practical challenges for, 301 event points, 376 EveryBlock.com, 122 evidence-based decision making, promoting, 132 evidence-based policy and practice, 187 “Excellence in Education” Healthy Campus grant, 232 expectation-maximization algorithms, DNT project, 322 Experian, 90, 392
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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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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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100 Strengthening Communities with
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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 387 and 388: 378 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
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Page 435 and 436: 426 Index Community Development Blo
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