2000115-Strengthening-Communities-with-Neighborhood-Data

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Using Data for Neighborhood Improvement 175 Figure 5.4. Licensed Childcare Provider Map of the Kenilworth-Parkside Neighborhood, District of Columbia Subsidized & Unsubsidized Licensed Childcare Providers Subsidized home based - bronze Subsidized home based - silver Subsidized home based - gold Subsidized centers - bronze Subsidized centers - silver Subsidized centers - gold Licensed unsubsidized child care DCPNI DCPS Pre-School/Pre-K Cesar Chavez PCS Source: Urban Institute mapping of data from the Office of the State Superintendent of Education, Division of Early Childhood Education, 11/16/10. Note: A color version of the map is available at http://www.neighborhoodindicators.org/library/ catalog/nnip-and-place-based-initiatives
176 Strengthening Communities with Neighborhood Data • GPRA 4. Number and percentage of students at or above grade level according to state mathematics and English language arts assessments in at least grades 3rd through 8th and once in high school • GPRA 10. Number and percentage of students who feel safe at school and traveling to and from school In order to report the GPRA measures, Promise grantees must regularly collect neighborhood-level data from national and local data files and special surveys on school climate and various neighborhood conditions. However, a more formidable challenge is for them to maintain data on individual children in a case management system. The data on services are collected for each child in the program and typically cover demographics, the type and dates of services provided, level of service (e.g., number of hours in counseling), and indicators of achievement (e.g., grade in a course that has been completed). 30 Although case management data are required for only two of the GPRA measures, Promise grantees are nonetheless encouraged to develop a case management system because of the importance of such systems to effective program management and outcomes. It is very difficult, for example, for case managers to offer sensible advice about next steps in supporting individual children and families in the program without considerable knowledge about their circumstances and their prior service histories. Much of the challenge for systems development, however, arises from the requirement that Promise Neighborhoods coordinate and track a number of services delivered by separate local providers. 31 This implies the need to integrate records that are maintained by separate providers on individual children and their families. It might involve, for example, integrating data about a student from school records (e.g., proficiency scores, absenteeism, school mobility) with data on the same child from child welfare agencies, health care providers, and afterschool programs. Credible summary information on what this type of program achieves requires summarizing data on what happens to individuals, but the task of data integration is obviously difficult, especially if attempted on a small scale. This task has certainly been a formidable challenge for the LISC’s TTM effort in Chicago. Perhaps more important than the technical difficulties are the steps that must be taken to maintain high standards regarding confidentiality. This work inevitably presses the boundaries set under the HIPAA and FERPA legislation noted in chapter 2. Even so, as
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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 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 65 This infor
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Institutional Context 71 8. For mor
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100 Strengthening Communities with
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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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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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