City of Garland, Texas
Tracking neighborhood vitality is an important issue for local governments. Declining neighborhoods will adversely affect the city's tax base, increase crime rates and drive away desirable homeowners creating a spiral of decline. This becomes not only a financial issue but also a quality of life issue for a city's residents. Some measurement of these trends must be used in order to intervene. But how can the quality and vitality of any particular neighborhood be measured?
The City of Garland, Texas is taking a proactive approach to measuring the vitality of its neighborhoods with a combination of data collection, analysis, and long-range planning tools. At the center of Garland's efforts is the Neighborhood Benchmarking Program (NBP), which takes basic planning concepts and marries these with performance indicators, GIS technologies, and administrative strategies. The City's Geographic Information Systems Department supported these efforts by developing a Neighborhood Information System (NIS) in cooperation with the City's Organizational Development Team (ODT), an internal consulting group.
Building an information system such as the NIS presented some unique challenges. The NIS data warehouse contains data compiled from various sources, such as the City of Garland's tax system, Code Compliance System, Pavement Management System, Utility Billing System, and information from other sources such as the local council of governments and realtor-oriented databases. Compiling these data required researching data sources, extraction from their native systems, conversion into Garland's GIS database format, geo-coding by address and other geo-based attributes, and in some cases creating new graphics and assigning new geo-codes.
To serve as an objective indicator of neighborhood conditions, the NIS compiles housing and nuisance code violations, street condition ratings, property values, litter index ratings, and other data from multiple city databases into one location for analysis. In addition, housing turnover rates and economic indicators are derived from external sources. ODT also adds survey results and field-collected neighborhood appearance ratings to the NIS.
Coordination of all these data into one location—as well as its analysis at the neighborhood level—enables staff to construct neighborhood profiles, monitor changes in neighborhood indicators and conditions, and develop a coordinated approach to addressing neighborhood issues. Bringing this information together in a GIS environment will help to identify spatial patterns and citywide neighborhood trends.
The first step in this project, as any other, was to define the ultimate objectives, such as what information would be needed out of the system and what types of analysis would be performed against the data. This is, of course, necessary in order to decide what data needed to go into the system. ODT expressed the following goals for the NIS:
First, the geographic boundaries of a neighborhood would have to be defined and agreed upon. A neighborhood can be defined in many ways. While valid opinions about what constitutes a neighborhood range across the spectrum from physical to cultural, a decision had to be made as to what geographic unit would constitute a neighborhood for the purposes of building a GIS dataset. The agreed-upon solution was to use subdivision phase as a neighborhood unit, because the houses of a common subdivision phase would be largely of the same age, price range, and quality of construction. The subdivision phase solution would also permit neighborhoods to be redefined by using different collections of subdivision phases.
Subdivision phases had been digitized by Garland's Engineering Department but were created to a CADD and not a GIS standard, and therefore were not topologically clean or suitable for the purposes of GIS analysis. Cleanup was performed to create enclosed polygons, and conflation to the existing parcel base. This provided the geographic unit to then reference all of the necessary data.
With the neighborhoods defined, and the graphical elements produced and cleaned to a GIS standard, compiling data from various systems both within and without the city to meet the above stated goals could begin. Garland uses a variety of applications both developed in-house and purchased off the shelf to meet the needs of city operations. Client-server systems as well as mainframe systems were involved in this project. The following is a list of the data sources utilized:
William Langely can be reached at (972) 205-2214 or at firstname.lastname@example.org.