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CONNECTING PEOPLE TO PLACE: IMPROVING COMMUNITIES THROUGH INTEGRATED DATA SYSTEMS Leah Hendey Urban Institute June 21, 2013
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Definition of an IDS Integrate individual-level data from multiple administrative agencies on an ongoing basis Levels: states, counties, and cities Purposes: improve case management for individuals, inform advocacy, policymaking, targeting, and program evaluation. Not IDS: management information system with only program service data The timing of data integration varies considerably among IDS
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Cross-site Project Goals Enhance access to IDS by NNIP partners to apply IDS data to problems at the neighborhood level Demonstrate value of adding place info to people data Create two-way exchange of info between NNIP & IDS and establish relationships Improve IDS data quality and demand for IDS
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Project Criteria Use data from an established IDS Must use data from at least two sources of information. Geographic location or neighborhood needs to be a data element in IDS and in the analysis. Use local data from an NNIP Partner Link at address/record level or aggregate data and link at neighborhood level Inform specific local problem & share results with community.
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Project Phasing Phase I: Planning Create full proposal for phase II. Form relationships with IDS agency Fast-Track Projects Shorter planning period – begin phase II in 2013. Phase II: Implementation Data prep & analysis, report and community forum. Cross-site Analysis & Dissemination
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Add a Place-Based Perspective with NNIP Neighborhood Data to IDS Data Problem: Multisystem youth account for large share of service use and are spatially concentrated – how does their neighborhood affect system involvement? IDS Data: Youth in multiple systems (e.g. juvenile justice, child welfare, homeless, etc.) NNIP Data: Community assets, school performance, crime rates Forum: CBOs and School officials with facilitated discussion of appropriate interventions to aid these youth
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Link Additional Individual-Level NNIP Data to IDS Data Problem: What is the effect of early childhood education (ECE), after- school programming, and public assistance use on children’s test scores? How does neighborhood influence the outcomes? IDS Data: K-8 education and public assistance records NNIP Data: ECE and after-school program enrollment; neighborhood context data Forum: Bring together community residents of underserved areas to help advocate for quality ECE and after-school programming.
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Link Property-Level NNIP Data to IDS Data Problem: How to target limit housing rehabilitation resources in a city? IDS Data: Children’s asthma incidence and chronic absenteeism NNIP Data: Parcel data on code enforcement, housing inspections and property conditions, age of housing stock Forum: Bring agency officials, schools and parents together to increase awareness of consequences of unhealthy housing.
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Key Dates & Deadlines 6/14: rSOI and Concept Paper released Through 6/28: Questions accepted 7/1: Notice of Intent to apply 7/19: Complete SOIs due 8/9: Selected sites announced 9/1: Phase I begins
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Questions? Leah Hendey lhendey@urban.org lhendey@urban.org 202-261-5856
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