3. Informing Policy Decisions

Another type of government data is data that informs policy decisions. This data may not have much immediate economic value, but it guides better government decision-making and once those decisions have been made provides a context for evaluating those decisions. Although the value is not immediate, it is easy to identify. It is difficult to predict which applications of government data will succeed or fail, but it is easy to list the most important policy questions of the day and to find the data relevant to those questions.

The most interesting uses often come from neighborhoods. The National Neighborhood Indicators Partnership (NNIP) has been fostering the growth and sharing of neighborhood data since 1995. The partnership is made up of educational institutions, foundations, and local governments. In 2005, an NNIP partner in Chattanooga, Tennessee, assembled data that highlighted shortcomings in elementary school reading proficiency. In response to the new information, the county mayor created a Chief Reading Officer position.[84]

In Baltimore, the locations of two new charter schools which opened in 2005 were determined in collaboration with the Baltimore Neighborhood Indicators Alliance at the University of Baltimore using public demographic data.[85]

In Cleveland, public data obtained by the NNIP guided welfare-to-work policy initiatives:

NNIP’s Cleveland partner mapped the residences of welfare recipients needing employment against the locations of new entry-level job openings in the metropolitan area. Doing so dramatized a serious spatial mismatch that caught the attention of policy makers. The existence of the data and tools (e.g., the ability to forecast changes in commute times that would result from alternative changes in transit routes and schedules) and the prominence the analysis was given in the press were credited as key motivators for a substantial state grant for welfare-to-work planning that brought child care planners as well as transit planners to the table for the first time on this issue.[86]

A persistent problem for Philadelphia has been its racial disparities. When I lived in Philadelphia during graduate school I saw first hand the geographic divisions created by the commercial corridor downtown and by the universities in West Philadelphia. Geographic isolation makes it easy to fail to see, or to ignore, other differences.

The Philadelphia Inquirer collected data on homicides since 1988 and shared it using Google Fusion Tables. Josef Fruehwald, a graduate school colleague of mine, created the graph shown in Figure 18. Here is what he observed:

Since 1988, the African American community has been living in a Philadelphia with approximately a murder every day, or every other day. The White community, on the other hand, has been living in a Philadelphia with a murder once a week.[87]

There are two reasons why I chose to include this example here. First, Fruehwald’s analysis has a different purpose from most crime-based visualizations that data geeks have made over the last few years. Crime maps, for instance, are often titillating because of the fears the user has of walking into the wrong neighborhood. Fruehwald’s analysis, and especially his characterization of what it means, is instead rather humbling for those of us that lived in Philadelphia without knowing how bad it was for some communities.

Figure 18. Murder rates in Philadelphia, 1988–2011, by race of the victim. By Josef Fruehwald, using data from The Philadelphia Inquirer.

The second reason is more technical. Fruehwald’s choice of vertical axis, the murder rate, was carefully crafted to put the values into meaningful terms. It shows the average time between murders, in days, which is more understandable than the more typical measure of incidences per month or per year. This isn’t just a graph. It is a visualization made to covey a message.

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