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2000115-Strengthening-Communities-with-Neighborhood-Data

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Using <strong>Data</strong> for <strong>Neighborhood</strong> Improvement 153<br />

organized into six sections, sequenced roughly according to stage of<br />

development:<br />

• Efforts in community initiatives to assess neighborhood conditions<br />

and trends. Community initiatives began to use neighborhood<br />

indicators systems in an exploratory mode soon after they were<br />

developed and also collected original data on conditions not easily<br />

captured by administrative data.<br />

• Uses of data to learn and mobilize around key issues. Some influential<br />

uses have focused on individual topics, using data to better<br />

understand an issue and then to mobilize external support for<br />

neighborhood recommendations.<br />

• Sophisticated uses of property data in the foreclosure crisis. <strong>Data</strong><br />

systems <strong>with</strong> a wide array of information about properties have<br />

enabled more nuanced decisions about how to address the problems<br />

of neighborhoods hit hard by foreclosure and abandonment.<br />

• Applications in large-scale comprehensive initiatives: examining the<br />

potential for performance management. The largest comprehensive<br />

community development program operating in any US city in the<br />

2000s was Chicago’s New <strong>Communities</strong> Program (NCP), notable for<br />

its pioneering efforts to apply performance management in the community<br />

development context.<br />

• New insights on neighborhood change from Making Connections.<br />

The Annie E. Casey Foundation’s Making Connections initiative<br />

focused on improving low-income neighborhoods in 10 cities and<br />

supported an associated survey research program that has yielded<br />

important new insights related to neighborhood change.<br />

• Intensive data use in program planning and implementation: Promise<br />

<strong>Neighborhood</strong>s. The Promise <strong>Neighborhood</strong>s Initiative has upgraded<br />

expectations for the use of data in program planning and implementation<br />

and suggests future directions for the field.<br />

Efforts in Community Initiatives to Assess<br />

<strong>Neighborhood</strong> Conditions and Trends<br />

Chapter 1 noted that the first ongoing multisource neighborhood-level<br />

data system was developed by a community-oriented research center at<br />

Case Western Reserve University in Cleveland, Ohio. The system was<br />

established in part to support the new Cleveland Community Building

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