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 How does the fourth largest city in the United States choose its large diameter waterline alignments when dealing with stakeholders, utilities, and varying urban environments? Is there a method to take multiple project areas with multiple alignment options and sift through the data to provide measurable criteria that identify the best options and is easy to understand?

The City of Houston (COH) tasked BGE, Inc. with developing Preliminary Engineering Reports (PERs) that evaluated potential alignments for three future large-diameter waterline projects varying from 24 inches to 72 inches. Each project location is in vastly different geographic and socioeconomic areas of the COH. One of the key missions of these PERs was to develop a decision matrix that would objectively consider multiple community, engineering, and construction factors relating to the proposed waterline alignments. Qualitative data is readily available for the proposed alignment areas, and the matrix evaluation is needed to take this data and make it quantitative.

By providing a quantitative solution, the alignments can be rated numerically to identify the best-case alignment.

Based on the client goals, the matrix design needed to:

1. Communicate simply while also showing the greatest impact.

2. Illustrate the clear frontrunners on the matrix scoring. Percentages or an overall maximum number were not options.

3. Provide a large scale for differentiation. It was important the scoring reflect those decisions are made objectively using important criteria for specific communities within the Houston area but show enough spread that the best option is visible.

4. Be customizable based on community-specific factors.

5. Incorporate available Geographic Information System (GIS) data.

A list of decision factors, identified as tiers, was considered to achieve these goals for the design matrix and grouped depending on the importance of the study area.

Each of these factors was rated on a scale from 1 to 5 (1 being most important, 5 being least important). Each factor had an individual calculation rating based on the number of elements and, in certain cases, its respective length within each alignment. The scoring for each factor was then multiplied out depending on how the element was classified by tier. Factors within Tier 1 were multiplied by 4, Tier 2 factors were multiplied by 3, Tier 3 factors were multiplied by 2, and Tier 4 was multiplied by 1.

Once the decision matrix was determined, a platform was needed to take large amounts of data, process it, and display it for the decision-makers at the COH to review. Since much of the existing data needed is currently in GIS, ArcGIS Pro software was identified as the resource for data management and analytics.

A field inventory of surface features was performed using field staff and a custom ArcGIS Collector interface to ensure that the existing GIS collected was current. As data was collected, it was uploaded immediately where team members within BGE and the COH could view and filter the collected data. Additionally, a 360-degree camera was driven through each alignment for a detailed comparison of the collected data.

After completing data collection, the deciding factors were input into the software where the collected data was processed and rated. The original calculations were started in an Excel spreadsheet and easily transferred into the ArcGIS software. Once the data was calculated and processed, the results are presented in a “dashboard” interface, including a full web map that is actionable and contains specific information associated with the route. The web map is surrounded by charts, graphs, and other infographics such as gauges and lists that allow easy information filtering. Each part of the dashboard can cross filter and change the surrounding graphic or remain self-contained.

As new information comes in, the dashboard is easily updated and reprocessed to provide the data’s most up-to-date rankings. Once this data is viewable, the engineer can review it and make sure that it makes sense based on field observations and engineering experience. Any “red flags” can be assessed by viewing the GIS raw data and verifying that the data collected and processed has been appropriately tagged and calculated.

Using the decision matrix and GIS dashboard provides cost-effective alignments to the COH that are easy to view and provide the best options to minimize construction difficulty and stakeholder opposition. As viewed through the GIS dashboard, the decision matrix takes a valuable tool and presents it visually, allowing the end-user the best experience for viewing the data and making an informed decision.