2000115-Strengthening-Communities-with-Neighborhood-Data

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Advances in Analytic Methods for Neighborhood Data 327 Figure 7.1.2. Neighborhood Types and Subtypes Lower income 1 The Truly Disadvantaged 2 Transient Underdeveloped 3 Stable Low Income 4 Port of Entry 1A Single Parents 1B Disadvantaged Seniors 1C Industrial Lands 2A Seniors and Centers 2B Families on the Move 2C Immigrants 3A Low-Income Families 3B Vacancies 3C Long-Term Residents 3D Regional Shops 4A Young Adults 4B Regional Shopping 4C Local Shops 4D Stable Residence 5 Urban Tapestry 5A Immigrant Eclectic 5B High Diversity 5C Young Adults 6 Coming Attractions 6A Commercial Districts 6B Middle-Class Residential 6C New Development 7 No Place Like Home 7A Blue-Collar Diverse 7B Settled and Stable 7C New Picket Fences 7D Corner Stores and Shopping Centers 7E High-End Housing 7F Higher-Income Homes 8 Close, Cool, Commercial 8A Walk-ups and Laundromats 8B Urban Pioneers 8C Downtown 8D Doormen and Delivery Higher income 9 Fortune 100 9A Old Money 9B Wealthy Boom 9C Exclusive Enclaves
328 Strengthening Communities with Neighborhood Data This typology also enables us to see whether different types tend to change over time, since it is constructed using data from 1990 and 2000. This information is summarized in the transition matrix reported in figure 7.1.3, which shows what percentage of neighborhoods in each type remained the same type 10 years later (the darker the color, the higher the percentage), and, if a neighborhood changed type, what other type of neighborhood it usually became. The matrix shows, for instance, how most neighborhoods tend to not change their type within these 10 years, though there are significant differences across neighborhood types. In particular, the low-income segments tend to change type more often than the higher-income segments: approximately 65 percent of type-1 neighborhoods were a different type 10 years later, while less than 20 percent of the neighborhoods in the three wealthiest segments changed type between 1990 and 2000. The information yielded by this transition matrix was included in the profiles for each neighborhood type. Figure 7.1.3. Transition Matrix Count in 2000 47 153 301 169 193 69 344 128 55 Type 1 35.8 40.8 19.2 0.8 0.8 1.7 0.8 120 Type 2 45.5 16.7 16.7 11.1 4 0.5 5.1 0.5 198 Type 3 1.2 2 89.8 0.4 2.5 4.1 244 1990 Type 4 Type 5 Type 6 0.9 3.7 74.1 17.6 3.7 1.7 7.2 21.1 58.9 2.8 7.8 0.6 1.2 9.5 13.1 61.9 3.6 9.5 1.2 108 180 84 Count in 1990 Type 7 3.6 2.2 6.6 0.3 86.3 0.5 0.5 366 Type 8 4.9 3.9 1 85.3 4.9 102 Type 9 1.8 5.3 14 1.8 77.2 57 Type 1Type 2Type 3Type 4Type 5Type 6Type 7Type 8Type 9 2000
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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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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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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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