Local Government Spotlight: Fort Worth, Texas
Using ArcGIS to Match Meters to Assets
Goal: Improve the accuracy of the city's asset and utility records
Barrier: Difficulty managing large number of utility accounts and assets
Solution: Utilized ArcGIS to match meters with corresponding assets
Outcome: By conducting virtual site visits to perform meter matching, the city reduced the number of in-person site visits to verify meters, which freed up staff time for other priorities and increased the city's operational efficiency.
Fort Worth, Texas, is the nation's 13th-largest city with approximately 895,000 people and a portfolio of more than 900 facilities and 3,000 electric meters. While many city meters were matched to assets using techniques such as rate code analysis, locating a subset of electric meters and matching them to assets was a challenge for meters installed in bulk. The city could not easily differentiate between meters servicing buildings and streetlights or other nonbuilding assets.
To match these remaining meters to assets, the city's energy services company contracted with FacilityDude to employ the geographic information system (GIS) software ArcGIS. Using ArcGIS, the city mapped the location of the utility meters in question against the county's GIS data layers, which contain parcel and ownership information for individual premises and property survey lines.
Using asset address information obtained from a city Risk Management Division report—and the meter service address information gathered from electric, natural gas, and water utility accounts—FacilityDude created an overlay of multiple layers and data points within ArcGIS. It then used proximity analysis functions and visual analysis to identify meter points adjacent to or near the footprint of a structure, streetlight, or other city-owned assets.
By conducting virtual site visits using the ArcGIS software to perform meter matching, the city reduced the number of in-person site visits to verify meters, which freed up staff time for other priorities and increased the city's operational efficiency.
Note: The information in this case study is based on primary research conducted in 2013. Learn more about the guide's research and development.
To learn more about meter matching and creating a central energy database, see Step 3.