2000115-Strengthening-Communities-with-Neighborhood-Data

Institutional Context 43
are built using these core datasets, but they are maintained by personnel
within the service department. Although the structure of an enterprise GIS
looks and operates differently depending on the structure of the local government
in which it resides, the general operating principle is the same,
namely, that shared data resources enable local government departments
to do more together than one service department could do alone.
An enterprise approach to GIS works especially well for local governments
wanting to better manage community and neighborhood change,
as many service departments have a role to play in improving community
quality of life. Pettit and Kingsley (2011) identify key types of data
that most local governments have readily available. These include, for
example, basic property characteristics, ownership, property tax status,
sales prices, foreclosure filings, and building permits.
In an enterprise GIS, such datasets are stored in a manner that allows
all service departments to access them if needed. Regardless of where
GIS datasets are stored, all such datasets in an enterprise GIS should
be developed according to uniform data standards adopted by the local
government. Data standards provide a common understanding of what
is contained in each dataset. In some sense, they can be thought of as
the equivalent of recipe instructions that allow many cooks to create
the same dish (Fleming 2014). Maintaining data standards across the
local government organization enables GIS analysts to easily combine
different datasets to understand possible impacts of decisions in a matter
of minutes. Working with a diverse set of stakeholders—neighborhood
groups, community nonprofits, state and federal agencies, academia, and
private-sector interest groups, among others—local governments can
combine their GIS datasets and layers with data from other stakeholders
to tackle virtually any community challenge.
The potential for return on investment in an enterprise GIS has also
proved substantial. Babinski (2014) reports that the total capital costs to
build the GIS program in King County, Washington, came to $10.6 million
in 2001, and the annual costs to maintain, operate, and use the system
came to $14.6 million. However, a groundbreaking new return on investment
study by Richard O. Zerbe of the University of Washington’s Evans
School of Public Affairs found that the annual benefits received from the
program ranged from 6 to 12 times the annual costs, with a projected
net benefit estimated at $87 million for 2010. The largest benefits were
found in two county departments: natural resources ($54 million) and
transportation ($19 million).