Utility companies demand quality data. To that end, We Energies, a company SSP Innovations provides gas work order posting services for, undertook the process to fundamentally change their data collection process from As-Builts and As-Laids to GPS. The benefits in terms of accuracy and quality are easy to see, but there are other positives as well, such as GPS projects being automatically sent to a mapper’s work queue after the inspector completes it in the field. However, there were a few challenges that We Energies needed to overcome before they were ready to start GPS’ing gas facilities.

Quality Land Base

Highly accurate GPS data requires a quality land base. Therefore, We Energies prepared their GIS by first going through a land conflation process. CoreLogic® data was selected for the land base along with Navteq center-lines. This data became the new framework for We Energies’ GIS. Some of the issues encountered during and after this process were: 1) deciding the best data to use to conflate to the new land base and 2) reviewing over 10,000 flags post conflation that were generated based on a rule-set (e.g., main segment length changed by >20%).

Additionally, due to the long duration of the conflation process, there needed to be a way for the mapping team to continue processing and posting work orders so the backlog wouldn’t drastically increase. To address this, the service territory was split up into sections. One section would be frozen and sent to the conflation team with the remaining sections still available for posting edits.

Quality Data Collection Process

New land base in place, the GIS was ready for GPS data. We Energies field personnel use Trimble® Geo 7 series units to collect all the data needed for mapping main and service installations and repairs. This stage of the transition had its challenges as well. SSP’s point of contact at We Energies stated that accurate data collection is difficult in areas with the following: cities with tall buildings, next to buildings, and under dense tree cover. We Energies took several steps to address this issue: 1) they conducted additional training sessions to ensure proper collection procedures were being followed, 2) they allowed the inspectors to draw a simple sketch when good accuracy wasn’t obtainable, take a picture of it with the Trimble®, and attach it the GPS point to provide context to the mapper, and 3) they provided immediate QA feedback as projects were completed, which greatly mitigated the amount of repeated mistakes. These steps, along with improvements to the user interface, resulted in both a quality data collection process and a seamless workflow from field installation to posting the edits in ArcFM™.

Work Order Posting

Quality data requires a quality team of editors to translate that data to the GIS. SSP has assisted We Energies’ Maps and Records department in processing and posting gas work orders for six years. During that relationship, We Energies communicated their desire for accuracy and completeness in their data, which mirrored SSP’s own commitment to quality. When a high standard is set, the end result is a quality one.

Formerly, SSP team members reviewed As-Builts and As-Laids prior to mapping a project. SSP personnel contacted the inspector for verification/correction any time data collection errors were found, e.g., incorrect tee locations. Today, GPS projects are assigned to SSP and then tasked out to GIS Specialists for processing and posting.

When processing an order, the mapper reviews pertinent installation information and GPS point data to ensure accuracy and completeness (data collection issues are sent to the inspector for clarification/resolution). Once the data is deemed good, the mapper loads it into the GIS and reviews the location of the GPS points relative to the existing gas facilities, e.g., the main a new service is attaching to. If the main lines up with the points, then the mapper will proceed with placing the new gas facility and updating the attributes. In some instances, though, the new GPS data is more accurate than the existing gas features, which necessitates a GIS correction to the main.

Most main corrections can be accomplished by using the existing main offset locations in the GIS and using the Copy Parallel tool to create a sketch line where the main should be. Usually this sketch line will run through the GPS service tee point and the main just needs to be moved a few feet. Other main moves are more difficult. For various reasons, SSP may deem that the information in the GIS is not accurate enough to move the main to its correct location. In these cases, mappers will pull the original main As-Laids from the digital archive and use the original stationing to correct the main. There’s a certain satisfaction in seeing 50-year-old data used to make a feature line up perfectly with new GPS data.

Next Steps

With accuracy of the existing facility verified, the new service is mapped and the attributes updated using GPS data. When populating fields with data, which value the mapper focuses on is important. Just as a marksman trains his eye to focus on the front sight when shooting to hit the bull’s-eye consistently, mappers train their eyes to focus on the data that is the most reliable. For example, the installation order number is what becomes the ID for the new service and is recorded in several different places. Rather than use an order number that was keyed in by a human, SSP personnel focus on system-generated order numbers because those numbers are not susceptible to human error. It is important to know the ins and outs of a client’s data to differentiate the reliable data from that which needs to be checked for accuracy. In addition to focusing on the best data source, copy and paste are used as much as possible to mitigate potential human error. After the service has been mapped and the attributes updated, the order isn’t completed and posted until a trace of the network is run verifying that all network features in the area are connected.

SSP staff work diligently to understand and internalize their clients’ mapping rules and procedures so they can provide them with the quality data they demand.