The Growth and Shifting Spatial Distribution of Tent Encampments in Oakland, California

Abstract

Tent encampments have become an especially common form of homelessness in West Coast cities like Oakland, California, where the number of people living in tent encampments increased by 130 percent between 2017 and 2019. Living in tent encampments provides residents both benefits and risks, depending on the encampments’ location, size, and stability. Using data from Google Street Views, I document the growth and spatial dynamics of tent encampments in west and central Oakland over the last decade. The number and size of tent encampments rapidly increased between 2014 and 2019, varying widely in their stability. City interventions like the city’s outdoor transitional housing sites displaced several large tent encampments. Combined with overall tent encampment growth, these displacements dispersed the encampments throughout nearby neighborhoods and other parts of the city.

Purity, Danger, and Patriotism: The Struggle for a Veteran Home during the COVID-19 Pandemic

“…While the dialectic of the dual encampments ended with the encampments ultimately being collapsed into one, the process of debate and negotiation between Veterans and the VA continues. Encampments can provide residents benefits, including safety, autonomy, and stability, as well as an opportunity for health and housing services to engage with vulnerable communities [ 53 , 54 ]. It is also important to recognize that there is no single approach that fits everyone, as different approaches suit individuals differently depending on the circumstances [ 48 ].…”

Racialized Reshuffling: Urban Change and the Persistence of Segregation in the Twenty-First Century

“…Finally, the proliferation of large-scale visual data, such as satellite and street-level imagery, offers new opportunities for studying neighborhood change and urban spaces. Street-level imagery has been used to measure neighborhood characteristics related to gentrification, SES, infrastructural improvement, and homelessness encampments (Finnigan 2021, Hwang & Sampson 2014, Naik et al 2017, and others have used satellite imagery to identify infrastructure and poverty, particularly where data are scarce, such as in rural environments ( Jean et al 2016). Combining these data with computational methods like web scraping, natural language processing, and computer vision allows researchers to examine many of these new data sources at unprecedented scales.…”

Curating Training Data for Reliable Large-Scale Visual Data Analysis: Lessons from Identifying Trash in Street View Imagery

“…The next generation of street audit studies employed GSV street-level imagery, which eliminated the costs of deploying observers or cars with camcorders. Researchers have utilized a similar approach to systematically observe selected blocks or neighborhoods in various cities (Odgers et al 2012;Hwang and Sampson 2014;Mooney et al 2014;Bader et al 2015;Langton and Steenbeek 2017;Finnigan 2021).…”

“…Large-scale longitudinal visual data from GSV can enhance research in urban sociology and social stratification by providing richer measures of neighborhood environments, including the conditions of urban landscapes and their deterioration or improvement over time. Linked with other data sources on children's wellbeing and neighborhood-level demographics, GSV data have already advanced social science research on neighborhood effects (e.g., Odgers et al 2012), gentrification (e.g., Hwang and Sampson 2014), and homelessness (e.g., Finnigan 2021), but scholars have not taken full advantage of its spatial and temporal coverage. For example, Finnigan (2021) examined the growth of homelessness encampments in a section of Oakland, CA over a 3-year period by manually examining GSV; documenting such changes across multiple cities and over a longer period would allow for a richer understanding of housing inequality, homelessness, and heterogeneity across contexts.…”

“…Linked with other data sources on children's wellbeing and neighborhood-level demographics, GSV data have already advanced social science research on neighborhood effects (e.g., Odgers et al 2012), gentrification (e.g., Hwang and Sampson 2014), and homelessness (e.g., Finnigan 2021), but scholars have not taken full advantage of its spatial and temporal coverage. For example, Finnigan (2021) examined the growth of homelessness encampments in a section of Oakland, CA over a 3-year period by manually examining GSV; documenting such changes across multiple cities and over a longer period would allow for a richer understanding of housing inequality, homelessness, and heterogeneity across contexts. GSV data can help scholars better understand the mechanisms by which neighborhoods shape individual and community well-being (Sampson and Raudenbush 1999; Sharkey and Faber 2014); how disorder, perceptions of it, and crime are linked (Sampson and Raudenbush 2004; O’Brien, Farrell, and Welsh 2019); and how gentrification's timing affects displacement (Hwang and Ding 2020), for example.…”