2000115-Strengthening-Communities-with-Neighborhood-Data

96 Strengthening Communities with Neighborhood Data
The final examples of developing a framework to combine individual
indicators are organizations that seek to measure the extent to which
neighborhoods have access to full-service grocery stores and, in particular,
to identify “food deserts.” In 2011, TRF, a community development
financial institution, published an analysis on the Limited Supermarket
Access Area Score (Califano et al. 2011). The authors defined a limited
supermarket access area as one in which residents must travel significantly
farther to reach a supermarket than the comparatively acceptable
distance traveled by residents in well-served areas. 11 TRF defines comparatively
acceptable as the distance that residents of well-served areas
(block groups with incomes greater than 120% of the area’s median
income) travel to the nearest supermarket. The data sources included
Trade Dimensions for supermarket locations; the Decennial Census for
population, households, and residential land area; ACS data for household
income; and the Bureau of Labor Statistics Consumer Expenditure
Survey for demand for food at home. TRF also published block group
data for the nation on their PolicyMap data portal (described below).
In 2013 the US Department of Agriculture introduced complementary
data and the Food Access Research Atlas, an online mapping tool. In their
definition, a census tract is considered to have low access if a significant
number or share of individuals in the tract is far from a supermarket.
Big Data
The administrative and primary data sources listed above are similar to
those available to NNIP partners in the mid 1990s. The technological
advances described in the next section create the potential for new types
of data sources for neighborhood indicators. Many of the new sources
loosely fall under the umbrella of “big data,” which refers to data that have
levels of volume, velocity, and/or variety that traditional computational
techniques cannot handle. The commercial sector already leverages big
data for marketing, and Fleming’s essay at the end of chapter 2 sets out
aspirational goals for wider use of big data by government. Universities
have developed specialized centers to advance analytic techniques using
big data to the benefit of the public sector and wider community. These
centers include the Center for Urban Science and Progress at New York
University, the Urban Center for Computation and Data at the University
of Chicago, and the Event and Pattern Detection Laboratory at Carnegie
Mellon University.