I recently had the opportunity to hear about an example from Los Angeles that illustrates this well. The Los Angeles Homeless Services Authority (LAHSA), the region’s lead agency in the U.S. Department of Housing and Urban Development-funded Continuum of Care Program, presented at an Esri CIO conference on the GIS-enabled app it uses for its annual point-in-time (PIT) homelessness count. LAHSA coordinates shelter, housing and related services across the county, and the PIT count is a key input for planning and funding decisions.
While the presentation impressed me, it was a conversation after the session that drove home how useful and people-centered this technology is. It turned out that my seatmate at lunch had volunteered for LAHSA, so I asked about her experience using the QuickCapture app. “I’m faster, more accurate and feel safer when I’m out with the app,” she said, and then showed me how the tool works from a volunteer’s perspective.
The app’s data-capture fields make it easier to log observations in real time. A breadcrumb-style tracking feature records where volunteers have walked, so that no street segment is missed and no area is counted twice. GIS provides a spatial and time-based record of where each observation was made and who made it. Volunteers can see their assigned census tracts along with a summary dashboard of the data they have contributed.
For volunteers, this technology reduces confusion, increases accuracy, saves time and improves safety. LAHSA staff experience a complementary set of benefits. As Deputy Chief Analytics Officer Bevin Kuhn explains, the application serves as “a one-stop shop” for volunteers, simplifying and speeding sign-up and registration. On the operational side, mobile data collection gives site coordinators near-real-time visibility into the count's progress: They can see coverage gaps or unusually low numbers as they emerge; if an area appears undercounted or overlooked, coordinators can send volunteers back while the operation is still underway, thereby avoiding costly, less-accurate follow-up efforts.
Transparent data collection tools and immediate validation of results by volunteers also allow LAHSA to post count results that bolster public confidence. As Kuhn notes, the agency improved how it visualizes and shares count data with the community, further building trust and supporting more informed policy and resource decisions.
A few other local governments are experimenting with GIS and mobile tools to improve understanding of homelessness at the neighborhood level. Work in Portland, Ore., for instance, shows how cities can use photo submissions and shared dashboards to track street homelessness conditions. Community members and city staff can submit photos of encampments or related issues through a shared platform. Scores show locations where conditions may require cleanup or intervention, and visualizations create a shared public understanding of problems.
San Francisco’s All Street Integrated Database (ASTRID), built by the Mayor’s Office of Innovation and the city’s Department of Emergency Management, breaks down data silos and provides homelessness outreach teams with a real-time view of the people they serve. The platform integrates data from nine street teams across four city departments. Through ASTRID, outreach workers can access up-to-date information on individuals they encounter, including recent overdoses, shelter history and prior interactions with other teams. Improving the data available to caseworkers advances support services.
New technologies are also being developed by academic partners. According to researchers at the University of Michigan, environmental sensing — a combination of imagery, sensors and analytics — can help identify encampments and high-risk areas at scale. The computer vision systems can analyze satellite imagery while vehicle-mounted or fixed cameras and crowdsourced street photos detect visible encampments. These systems also can incorporate “contextual urban indicators,” such as trash accumulation; graffiti; road and parking lot deterioration; traffic levels; and building quality, as signals of areas where homelessness may be more prevalent. And Internet of Things sensors can provide continuous measurements of air quality, noise and waste pollution, highlighting environmental hazards, guiding decisions about remediation and prioritizing clinical outreach.
These technologies also give officials a better picture of how public spaces are used, how pedestrians move, where crowds gather and how people experiencing homelessness interact with these environments. That understanding can inform design, enforcement and service strategies that are more humane and more effective.
At the same time, these advances raise important ethical and governance questions. The very capabilities that help officials improve safety and sanitation and better target services can also be used primarily for code enforcement or to displace people experiencing homelessness. Local governments and nonprofit partners therefore need clear public policies that define acceptable and prohibited uses of data, protect privacy — by automatically blurring faces, for example — and ensure clear accountability and oversight.
The value of tools such as GIS and environmental sensing depends on how they are embedded in public health, social care and outreach workflows, and whether there is sufficient housing and service capacity to act on what the data reveals. But when integrated thoughtfully, these tools can significantly improve our ability to detect homelessness and its associated risks and to respond more quickly and precisely, making outreach efforts more coordinated and effective and going a long way toward improving public spaces.
Governing's opinion columns reflect the views of their authors and not necessarily those of Governing's editors or management.
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