In the midst of the complex societal issue of homelessness, urban parks can often be the visible and symbolic front line. The ability to provide accessible public spaces for all means park and recreation departments across the country are stuck — between methods of empathetic compassion versus those of policy enforcement. Many communities are struggling to find the right balance, where all people feel welcomed and free to share in the joy and respite provided by public space.
Park and recreation staff are often presented with the physical and perceptual challenges associated with homelessness in city parks, yet do not have the tools or the clear charge to engage with the underlying causes. As Danielle Taylor noted in “Taboo Topic: Homeless in the Park” on NRPA’s Open Space blog, “Homelessness is an incredibly complex social issue with innumerable factors contributing to it. It’s not easy to address on a wide scale.”
At the same time, all across the country advances in technology and data have made possible new ways of looking at this “wide scale,” and cataloging, assessing and addressing all forms of urban conditions. While technology and data can’t offer a panacea on their own, access to more accurate information can help us better imagine and assess potential policy, design or cultural changes. We have seen an explosion of big data in the past 10 years, capturing everything from tweets to urban environment metrics. The design and planning practice for which I work, Sasaki Associates, develops data-analysis tools, for instance, that can help park and recreation departments evaluate things like park accessibility, value creation associated with park resources, and the effective prioritization of investment. This exploration has proven beneficial for many problems; again and again, we see that better data translates into better solutions.
At the heart of these tools is a belief that alleviating a problem necessitates knowing more about it: defining its edges, identifying its heart, understanding its complexities. Recently, we initiated a study to better understand how homelessness data can more effectively be captured and leveraged toward more customized and effective solutions.
Today, there are homelessness datasets available for public use. Most notably, the Department of Housing and Urban Development (HUD) leads an effort to collect data on homelessness. Annually, it conducts a volunteer-driven point-in-time count (PIT) of the homeless people in shelters and on streets. HUD also creates an annual inventory of housing options (HIC) that serve these populations and coordinates a Homeless Management Information System (HMIS) that stores client-level information. This data, reported by Continuum of Care (CoC) districts nationwide and compiled by HUD, is released for public use through the HUD Exchange site. HUD also releases an Annual Homeless Assessment Report (AHAR) that summarizes and analyzes this data. There’s even a site called Homelessness Analytics (last updated 2012) that maps HUD’s data against various other metrics.
HUD does a great service by collecting and sharing a considerable amount of data and information. Still, we need to build on this to paint a more complete picture of homelessness. By enhancing the level of detail collected, integrating data consistently and comprehensively, and thinking creatively about data collection methods, data could be shared and compared more easily and work toward alleviating homelessness could be better coordinated. Here are a number of dimensions to consider in building on HUD’s already robust data collection infrastructure:
Location specificity. Where current systems aggregate the count of homeless populations in varying scales (from a portion of a metro area to an entire state in some cases), more specifically geolocated data — using a tool like Fulcrum or What3Words, for example — would better provide a more nuanced and useful understanding of homeless population distribution and relationships to other urban factors.
Integration. Where current information systems are maintained by distinct CoC districts, a single nationwide system could enable services to be better coordinated and would allow for easier comparative benchmarks across geographic boundaries.
Accessibility and transparency. Where current methods include the capture of many different demographic data points and metrics that can quickly become complex to interpret, a streamlined and interactive interface could allow broader access to and easier exploration of the data.
Scope. Where current data collection is largely driven by volunteers conducting headcounts (with inevitable potential for error), strongly incentivizing self-reporting as a collection method may help to capture more complete information and sub-populations not included currently.
A powerful tool for strategic planning and resource allocation can be created through the improvement of data collection methods for homeless populations, the ability to better visualize and interpret the information, and the integration of disparate information systems into a unified whole. Most importantly, there are a number of ways these improved datasets can help park and recreation departments — in close collaboration with other departments and agencies — better address issues of homelessness and the associated conflicts and negative perceptions.
Tailored service provision. Imagine if a community could better understand the needs and challenges of a localized homeless population. Knowing, for instance, that the majority of the homeless population within a 10-block radius was comprised of single mothers, parks and recreation staff could focus on providing specialized child care or customized job-training.
Symbiotic relationships. Imagine if a community could better understand the condition, skills and backgrounds of its homeless population. Like the city of Albuquerque, that hires its homeless as city workers, park and recreation departments could create programs to seasonally or permanently employ readily available homeless workers.
Homeless addresses. Imagine if homeless individuals — traditionally migratory and hard to locate — had a geolocated “address” (for instance, by using a technology like What3Words which creates a named location for every 3 square meters on the planet). Communication and community-building could be much improved, allowing for greater collaboration.
Public engagement. Imagine if a community could better access the stories and profiles associated with existing homeless individuals or families. Care for the city’s homeless could be shared more communally if local residents were engaged, inspired and able to help in direct and tangible ways.
In addition to the myriad innovative approaches being tested across the nation, the strategies outlined here are the beginning of a data-driven approach to thinking differently about the challenges of homelessness.
Keillor’s research on homelessness was presented at the 2016 NRPA Annual Conference, in St. Louis, Missouri, as part of a presentation and panel discussion titled, ‘Stories vs. Statistics: Understanding Homelessness in Our Parks and Cities.’
Keillor is interested in the application of technology to the practice of planning, especially how technology can enable empowerment of disenfranchised populations. The resource can be accessed here beginning on October 1, 2016.
Gretchen Keillor is an Urban Planner at Sasaki Associates, Inc.
- Danielle Taylor, “Taboo Topic: Homeless in the Park,” NRPA Open Space Blog, January 22, 2014
- James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, Angela Hung Byers, Big Data: The Next Frontier for Innovation, Competition, and Productivity, McKinsey Global Institute Report, May 2011
- Array of Things website
- Brad Barnett, “Making Sense of Accessibility Metrics,” Sasaki Associates, Inc. Blog, 2016
- Using Data at Park and Recreation Agencies, National Recreation and Park Association, 2016
- HUD Exchange, Continuum of Care webpage
- HUD Exchange, 2015 AHAR: Part 1 - PIT Estimates of Homelessness in the U.S. webpage
- The 2015 Annual Homeless Assessment Report (AHAR) to Congress, The U.S. Department of Housing and Urban Development, Office of Community Planning and Development, November 2015