47 - Understanding homelessness by location through data visualization

Abstract Information: Despite its prevalence across the United States, homelessness is distributed differently by State due to factors such as demographics, economic conditions, and access to services. The aim of this study is to use data visualization to compare the representation, rate of homelessness, and demographic profiles of people experiencing homelessness (PEH) in South Dakota and Massachusetts. Data visualization enables the identification of patterns and trends that may not be apparent from raw data, making it a crucial tool in comparing the proportion of different racial/ethnic groups and gender among PEH in the two states. Using state-level mapping, our analysis reveals that Native American and Hispanic/Latino populations are overrepresented among PEH in South Dakota, whereas Black and Hispanic/Latino populations are overrepresented among PEH in Massachusetts. Furthermore, visualization of demographic profiles for different racial and gender groups among PEH in these states could help identify unique challenges and needs for each group. By highlighting disparities in access to services and resources, these visualizations can inform program development and policy decisions, facilitating a better understanding of the complex nature of homelessness.

Relevance Statement: This study aligns with the Evaluation 2023 theme, The Power of Story in Evaluation. The power of storytelling using data visualization can be harnessed in evaluations by identifying patterns and trends and answering questions about differences among race and gender of people experiencing homelessness (PEH). By presenting data in a visual format, the study will highlight the stories of PEH and the communities that support them. The study will also reflect on the role of data in storytelling and how data can be used to shape policies and practices that support the most vulnerable members of society. Data on homelessness rates, demographics, and services provided to people experiencing homelessness (PEH) were collected from various sources, including government agencies, non-profit organizations, and academic research. This data then be visualized using interactive tools, such as maps, charts, and graphs. The visualizations help answering key questions related to homelessness, such as why there are over and under representation of PEH, how to view racial and ethnic disparities using rate of homelessness, understanding concentrations of service providers. By comparing South Dakota and Massachusetts, this study highlights the differences in homelessness rates and service provision, as well as how these differences impact the lives of PEH. Additionally, by focusing on two distinct regions, this study will showcase the diversity of experiences and stories of PEH, providing valuable information to inform policies and practices. Table: Summary table of homelessness between Massachusetts and South Dakota (FY 2020) Category |Massachusetts |South Dakota Overall Homelessness |18,471 |1138 Homeless Rate per 10,000 people (2022) |27.3 |13.2 Racial/Ethnic Disparities |Black and Hispanic/Latino populations are overrepresented among PEH |Native American and Hispanic/Latino populations are overrepresented among PEH Homelessness Among Veterans |985 |32 Chronic Homelessness |2,880 |136 Both states saw a slight increase in homelessness from the previous year, with South Dakota experiencing a 4.5% increase and Massachusetts experiencing a 1.4% increase. Both states saw a slight decrease in veteran homelessness from the previous year, with South Dakota experiencing a 3% decrease and Massachusetts experiencing a 1.8% decrease. In 2020, South Dakota had a poverty rate of 10.9%, while Massachusetts had a poverty rate of 9.1%. Finally, with our analysis and insightful data visualization, we can gain a more nuanced understanding of the complex factors that contribute to homelessness and develop targeted interventions that address the specific needs of different populations within a State. Sources: • Moses, J. (2019) New Data on Race, Ethnicity and Homelessness. Retrieved from https://endhomelessness.org/blog/new-data-on-race-ethnicity-and-homelessness/ • Moses, J. (2020) State of Homelessness: A look at Race and Ethnicity. Retrieved from https://endhomelessness.org/blog/state-of-homelessness-a-look-at-race-and-ethnicity/ • State of Homelessness: 2022 Edition. Retrieved from https://endhomelessness.org/homelessness-in-america/homelessness-statistics/state-of-homelessness/ • Kelkar, M., Frey, R & Shane, E. (2019) Addressing homelessness with data analytics. Retrieved from https://www2.deloitte.com/us/en/insights/industry/public-sector/homelessness-data.html