{"id":446302,"date":"2023-03-09T02:18:45","date_gmt":"2023-03-09T02:18:45","guid":{"rendered":"https:\/\/uat.esri.com\/en-us\/industries\/blog\/?post_type=blog&p=446302"},"modified":"2023-03-09T19:24:38","modified_gmt":"2023-03-09T19:24:38","slug":"enterprise-gis-supports-efficiency-and-output-at-madison-metropolitan-sewerage-district","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/en-us\/industries\/blog\/articles\/enterprise-gis-supports-efficiency-and-output-at-madison-metropolitan-sewerage-district","title":{"rendered":"Enterprise GIS Supports Efficiency and Output at Madison Metropolitan Sewerage District"},"content":{"rendered":"

By Mitchell Johnson<\/strong>, GIS Analyst, Madison Metropolitan Sewerage District<\/p>\n\n

The Madison Metropolitan Sewerage District (MMSD) in Wisconsin was established in 1930 to provide a regional approach to wastewater conveyance and treatment in order to protect the lakes and streams of the upper Yahara watershed. The district serves about 407,000 people in 26 Madison-area customer communities covering about 187 square miles. It owns and operates 145 miles of pipe and 18 regional pumping stations that convey approximately 36 million gallons of wastewater at the Nine Springs Treatment Plant daily.<\/p>\n\n

When the district migrated its enterprise geographic information system (GIS) to Esri, it moved siloed databases and paper maps to a central database, web maps, and a suite of new GIS tools. This has improved efficiency and output in utility locating, asset management, pollution prevention, and other programs at the district.<\/p>\n\n

Improving Inspection Workflow<\/h2>\n\n

Part of maintaining 145 miles of pipe includes inspecting 20 percent of our maintenance holes each year. Our inspection workflow provided us with an opportunity to use ArcGIS solutions to improve data collection, maintenance, and analysis. Field crews were using Panasonic tablets loaded with a custom access database to fill out inspection information and take photographs. However, this workflow required additional hardware in the field and manual data synchronization at the office, lacked integration with other data systems, and took poor quality photos. Additionally, our field crews were already familiar with using ArcGIS Collector paired with Trimble R2 Global Navigation Satellite System (GNSS) receivers on their iPad tablets, so it was clear that migrating the workflow to Esri applications would be a smooth transition.<\/p>\n\n

Our inspectors desired the ability to launch an inspection from web maps in the field based on their location, so we knew that the inspection data collection needed to start with a map interface using ArcGIS Field Maps. Additionally, ArcGIS Survey123 offered the most relevant set of tools to replicate the existing inspection database interface of drop-down menus, fields that can be filled in, and photo attachments.<\/p>\n\n

\"MMSD
A district utility locator marks a sanitary sewer using a Global Navigation Satellite System (GNSS) receiver and geographic information system (GIS) technology.<\/figcaption><\/figure><\/div>\n\n

Assessing Inspection Needs<\/strong><\/p>\n\n

We started with understanding what information our maintenance and engineering departments needed by conducting a series of meetings and interviews. Next, we tested whether the existing data and functionality could be migrated to Survey123. The drop-down lists and cascading logic in the existing database were easily transferred and, in some cases, improved on with Survey123 features. Lastly, we needed to figure out how to start the data collection process using a map. Fortunately, custom hyperlink pop-ups can be set up to link directly to a feature service published from Survey123 and automatically pull data from the map feature selected. This allowed for the prepopulation of data into the survey record.<\/p>\n\n

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Madison Metropolitan Sewerage District (MMSD) staff perform a maintenance hole inspection using ArcGIS Field Maps and ArcGIS Survey123.<\/figcaption><\/figure><\/div>\n\n

Implementation<\/strong><\/p>\n\n

We successfully tested and implemented a procedure in which the inspector uses ArcGIS Field Maps to view a map, select a maintenance hole, open the inspection form via a hyperlink in that maintenance hole’s pop-up window, fill out information in Survey123, and submit the survey. That survey is sent to a table in the enterprise geodatabase. A Python script runs every 30 minutes to append entries in the initial survey table to a table that is in a relationship class with our maintenance hole feature class.<\/p>\n\n

We held two, one-hour in-person trainings and provided a user guide along with an instructional video. This combination of training and documentation helped to educate inspectors on the new tool. Additionally, a dashboard was built using ArcGIS Dashboards to visualize and summarize inspection data.<\/p>\n\n

\"\"
Pictured is the Inspection Data dashboard in the District GIS Portal.<\/figcaption><\/figure><\/div>\n\n

Reviewing Results<\/h2>\n\n

The new inspection workflow was successful in more ways than one. In addition to providing the functionality that the team needed, it allowed us to examine issues with redundancy in the data collection process. After a season of inspections, we learned that the workflow required inspectors to fill out unchanged information (e.g., size, material, and location) when they revisited the maintenance hole after completing any rehabilitation work identified in the first inspection. They only needed to update the work they did during rehabilitation. In the old system, staff worked around this duplicate data entry burden by editing existing inspections, which resulted in bad data where inspections would include information about defects as well as completed repairs for those same defects. This would make it difficult for a user to interpret the state of the maintenance hole years down the road. The previous work-around was not possible in the new workflow, as we built the form to require new data points each time an inspection was generated. This was to ensure quality of the data generated as part of the inspections.<\/p>\n\n

Another thing we learned was that much of the information collected during inspections was not being effectively utilized, making data collection efforts less meaningful. So while we had a great new tool for doing routine work, we were still not taking advantage of the resultant data and analysis.<\/p>\n\n

Changing Our Approach<\/h2>\n\n

To address these new challenges, we took a step back and reevaluated each data point being collected and how that data is used. We put together a project team that included GIS staff, inspectors, asset specialists, and maintenance supervisors. We also used a project charter to agree on the terms of updating the inspection workflow as well as what the data is used for. This approach put less emphasis on the tools and more on what is going into them to reconstruct them more effectively. We updated the workflow as defined by our project charter process, automatically populating more static information\u2014such as measurements and material\u2014to avoid duplicate data entry when reinspecting assets after rehabilitation work was done.<\/p>\n\n

We decided that overall condition is not very useful for scoring maintenance holes, as this is a subjective evaluation for the inspector on that day. Instead, we focused more on what specific defects exist, what we should be vigilant of long term, and what work needs to be done in the near term. Ultimately, this reduced data entry burden on the inspectors and filtered out a lot of the noise in the data for those who make maintenance and engineering project decisions.<\/p>\n\n

Lessons Learned<\/h2>\n\n

Look at the whole process beyond the tool itself.<\/p>\n\n