A New Lens on Homelessness: Daily Tent Monitoring with 311 Calls and Street Images
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866916890998210560 |
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| author | Jung, Wooyong Kim, Sola Kim, Dongwook Tabar, Maryam Lee, Dongwon |
| author_facet | Jung, Wooyong Kim, Sola Kim, Dongwook Tabar, Maryam Lee, Dongwon |
| contents | Homelessness in the United States has surged to levels unseen since the Great Depression. However, existing methods for monitoring it, such as point-in-time (PIT) counts, have limitations in terms of frequency, consistency, and spatial detail. This study proposes a new approach using publicly available, crowdsourced data, specifically 311 Service Calls and street-level imagery, to track and forecast homeless tent trends in San Francisco. Our predictive model captures fine-grained daily and neighborhood-level variations, uncovering patterns that traditional counts often overlook, such as rapid fluctuations during the COVID-19 pandemic and spatial shifts in tent locations over time. By providing more timely, localized, and cost-effective information, this approach serves as a valuable tool for guiding policy responses and evaluating interventions aimed at reducing unsheltered homelessness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06409 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A New Lens on Homelessness: Daily Tent Monitoring with 311 Calls and Street Images Jung, Wooyong Kim, Sola Kim, Dongwook Tabar, Maryam Lee, Dongwon Machine Learning Homelessness in the United States has surged to levels unseen since the Great Depression. However, existing methods for monitoring it, such as point-in-time (PIT) counts, have limitations in terms of frequency, consistency, and spatial detail. This study proposes a new approach using publicly available, crowdsourced data, specifically 311 Service Calls and street-level imagery, to track and forecast homeless tent trends in San Francisco. Our predictive model captures fine-grained daily and neighborhood-level variations, uncovering patterns that traditional counts often overlook, such as rapid fluctuations during the COVID-19 pandemic and spatial shifts in tent locations over time. By providing more timely, localized, and cost-effective information, this approach serves as a valuable tool for guiding policy responses and evaluating interventions aimed at reducing unsheltered homelessness. |
| title | A New Lens on Homelessness: Daily Tent Monitoring with 311 Calls and Street Images |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.06409 |