2000115-Strengthening-Communities-with-Neighborhood-Data

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Advances in Analytic Methods for Neighborhood Data 333 Figure 7.1.4. Location of Neighborhoods in Transient Underdeveloped Category (Type 2) Legend 2A: Seniors and Centers 2B: Families on the Move 2C: Immigrants important for each type, and it enables us to compare a neighborhood to its peers with on those factors, in order to identify priority areas for development interventions. For instance, having identified a group of peer neighborhoods for the census tract in North Lawndale, we can use the typology to see how this tract is doing on some key drivers of neighborhood change for Stable
334 Strengthening Communities with Neighborhood Data Table 7.1.2. North Lawndale, In-Type Comparison Tract 291800 Type 2-A, Seniors and Centers Change in value (RSI, 1990–2006) 33% 100% Median income $18,560 $17,000 Vacant units 29.2% 19.0% Social capital 3.72 3.95 Unemployment rate 37.0% 20.9% Turnover (% moved in past five years) 55.2% 30.8% Educational attainment: no high school diploma 69.1% 42.6% Low-Income neighborhoods (as identified by the models developed in the other phases of the DNT project). An initial comparison (summarized in table 7.1.2) reveals that this tract is actually trailing its peers on a few important dimensions, including employment, educational attainment, and, to a lesser extent, social capital 14 —all of which are drivers of improvement in place. This information can then be used to prioritize interventions and address these particular areas. The typology can also be used to go one step further and find out how similar neighborhoods have been dealing with these issues. In particular, the typology can be used from the bottom up to find the neighborhoods that are most similar to a particular place (in the case of the North Lawndale neighborhood discussed above, several neighborhoods on the West and South Side of Chicago, as well as some communities in South Dallas), see which ones have successfully dealt with the same development issues (in this case, unemployment and educational attainment), and identify the interventions that can best be adapted and applied. 15 An additional important use for the typology is that it can help analysts think strategically about the trajectory in which a neighborhood is headed and what kind of place its residents would like it to be. Consider, for instance, neighborhoods in type 6 (Coming Attractions). Based on the dynamics of change identified using the transition matrix, we know that these areas can evolve in several directions and become very different types of places. By comparing the characteristics of these different types, we can identify areas of intervention that would help push the neighborhood in the desired direction. For instance, if the residents of a type-6 neighborhood determined that they would like their community
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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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Institutional Context 67 By identif
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Institutional Context 71 8. For mor
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100 Strengthening Communities with
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4 A Framework for Indicators and De
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A Framework for Indicators and Deci
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5 Using Data for Neighborhood Impro
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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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The Regional Equity Atlas Using Dat
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The Bond Measure Is Approved Using
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ESSAY A Solvable Problem Hidden in
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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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444 Index privacy concerns (continu
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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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