A New Lens on Homelessness: Daily Tent Monitoring with 311 Calls and Street Images

2025-08-08

AI Summary

Current homeless encampment monitoring—e.g., point-in-time counts—is infrequent and coarse-grained spatially, failing to capture daily dynamics in tent distribution. This paper proposes the first crowdsourced, data-driven framework integrating 311 service requests with street-level imagery to enable daily, block-scale monitoring of homeless tents. Methodologically, it innovatively combines computer vision (for automated tent detection in street-view images), multi-source spatiotemporal data fusion, and a lightweight predictive model to detect short-term fluctuations and characterize spatial migration patterns. The approach significantly improves monitoring timeliness and cost-efficiency. In a San Francisco case study, it successfully identified critical changes—including pandemic-driven surges in tent counts and shifts in geographic distribution—thereby supporting evidence-based, granular policy responses.

Technology Category

• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Web Mining and Content Analysis: Web data visualization

Abstract

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.

Problem

• Monitoring homelessness with frequent, detailed spatial data
• Overcoming limitations of traditional point-in-time counts
• Tracking and forecasting homeless tent trends using public data

Innovation

• Uses 311 calls and street images
• Predicts daily tent location changes
• Tracks neighborhood-level homelessness trends