2000115-Strengthening-Communities-with-Neighborhood-Data

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Using Data for City and Regional Strategies 235 Figure 6.2. Wholeness Index in Dallas, 2006 Taking the Good with the Bad Legend Legend Wholeness Indicators Rated as Best 7 or More 4 – 6 1 – 3 None City of Dallas Wholeness Indicators Rated as Worst 7 or More 4 – 6 1 – 3 None City of Dallas Source: Martin and Bray (2006). Note: Color versions of the maps are available at http://www.neighborhoodindicators.org/library/catalog/dallas-whole-city-2006- wholeness-index-summary-report
236 Strengthening Communities with Neighborhood Data patterns of disadvantage in the city and the urgency of addressing the gaps. As one strategy to accomplish this, the mayor instituted the Mayor’s Southern Dallas Task Force, 17 which divided Southern Dallas into 10 neighborhoods and brought citizens and business leaders together over two years to suggest plans for improvement in each neighborhood. Strategies ranged from smaller steps, like neighborhood branding campaigns and new security cameras in business areas, to larger ones, like reopening a hospital or bringing more retail to the neighborhoods. Despite raised expectations, the task force recommendations got little attention from policymakers (University of Texas at Dallas 2008; Dallas Morning News 2009). The DMN staff planned to call on the city government to seriously consider the recommendations of the task force, but instead of a stand-alone editorial, they decided to reinforce their argument with a rich portrait of South Dallas. This special section would include neighborhood conditions and advocate for policies and programs to improve conditions for residents there. The DMN staff approached the authors of the Wholeness Index to assemble and analyze many sources of data to illustrate the disparities between northern and southern Dallas. (The Williams Institute had recently been renamed the Institute of Urban Policy and Research and moved to the University of Texas at Dallas.) The researchers worked closely with DMN staff to identify five focus neighborhoods in South Dallas and provided information for each neighborhood that described their demographics, health conditions, crime, and education outcomes. The Institute of Urban Policy and Research team saw the limitations of the secondary data and also conducted a two-month windshield survey. The institute trained members of its Community Research Team, who were residents of South Dallas communities, to systematically collect information about land use, housing conditions, walkability, and other quality-of-life factors. These data were then assembled into a mapping program that allowed the exploration and summary of these physical characteristics by neighborhood. The institute’s work with DMN resulted in a major eight-page spread in a Sunday paper in September 2009, complete with maps, data, and stories about the five South Dallas neighborhoods. The collection of articles documented current disparities, highlighted programs that were helping to close the gap, and called for additional investments in the neighborhoods. The impressive feature section earned the DMN editorial staff a Pulitzer Prize in 2010 for editorial writing (Wilkins 2010).
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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 5 to set
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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 65 This infor
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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 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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440 Index Neighborhood Early Warnin
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442 Index OnTheMap, 81, 104 open da
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448 Index strategic investment init
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450 Index Twin Cities (Minnesota) d
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