2000115-Strengthening-Communities-with-Neighborhood-Data

102 Strengthening Communities with Neighborhood Data
Case Western University in Cleveland, Ohio, a founding NNIP partner,
launched the Community and Neighborhood Data for Organizing
(CANDO) in 1992 as a system that users dialed into by modem. The system
contained social and economic neighborhood indicators based on
the 1990 Decennial Census and local administrative sources. The Urban
Institute 1996 analysis of the six original NNIP partners mentions three
of the partners using the web to distribute data: the Cleveland site, the
Piton Foundation’s Neighborhood Facts in Denver, Colorado, and The
Providence Plan neighborhood profiles.
During the decade after the founding of NNIP, many of the parcel- and
neighborhood-level data systems were funded in part by the Technology
Opportunities Program (TOP) administered by the National Telecommunications
and Information Administration of the US Department
of Commerce. From 1994 to 2004, the TOP made matching grants to
government agencies and community-based nonprofit organizations to
demonstrate how digital networks could support community improvement.
The National Telecommunications and Information Administration
sought projects specifically related to local data intermediaries that
fostered communication, resource sharing, and economic development
within communities. Over these 10 years, 11 grants totaling $5.7 million
were awarded to develop neighborhood-level information systems
in seven cities. 18 TOP is a stellar example of how the federal government
can accelerate innovation in support of the broader use of local data for
decisionmaking.
One of the TOP-supported sites was Neighborhood Knowledge
California (NKCA), developed by the Advanced Policy Institute at
the University of California, Los Angeles, which also built its city’s
early warning system. Launched in 2003, NKCA included a variety of
neighborhood-level national and local data for the entire state of California,
including Decennial Census and HMDA data (Steins 2003).
In addition to providing standard profiles, the site enabled users to
generate their own charts and maps, to build their own neighborhoods
from multiple census tracts, and to upload their local data for automated
geocoding and display.
The Fannie Mae Foundation, which supported three of the four local
early warning systems mentioned above, recognized that the NKCA efficiencies
of assembling federal data for one state could just as easily be
extended to the nation. Beginning in 2004, the foundation began the
in-house development of the first national community indicators por-