Academic Implementation for Firearm Injury Research with the University of Texas Medical Branch (UTMB)

Using interactive mapping and data visualization tools, the client was able to navigate large, complex datasets in a more intuitive and actionable way. By visualizing information geographically and categorically, they uncovered patterns that were difficult to detect in raw tables such as incident clusters, service deserts, or areas with limited community engagement. These insights helped guide a core question: where should the team focus resources to reduce harm and improve outcomes?

To streamline reporting and reduce manual work, we configured the system to automatically group data by county and census tract. This eliminated the need for tedious spreadsheet manipulation and gave the team clean, structured views they could immediately work from. Geographic groupings also made it easier to align findings with local services, helping the client turn raw data into place-based strategy.

We also prioritized data quality. Given the volume and variability of the inputs, it was essential to catch duplicates, inconsistencies, or questionable entries before analysis. To support this, we built a review process that flagged anomalies for staff to check. This step preserved the integrity of the dataset and ensured that findings were based on reliable, validated information.

Once the data identified high-priority regions, the team launched a targeted outreach campaign. Mailers with QR codes were sent to households in selected areas, directing residents to a brief online survey. Responses were collected and reviewed through a centralized system. Once approved, the system automatically issued digital gift cards to participants. By combining automation with careful data design, the team was able to turn insight into meaningful, community-driven action quickly, securely, and at scale.