Spatiotemporal Changes, Socio-Environmental Impacts, and the Homeless Population

San Diego State University Foundation, San Diego CA

Investigators

Abstract

This project addresses the issue of homelessness in a border region by developing advanced artificial intelligence (AI) and geospatial techniques to map and analyze the encampments of the unhoused. By integrating data from street view images, remote sensing imagery, and geographic information systems, the research identifies patterns of migration by unhoused populations over the past decade. The study focuses on understanding the socio-environmental factors that influence the distribution and migration patterns, with the ultimate goal of informing policy and interventions to improve community welfare and health. Additionally, this project creates the San Diego Homeless and Health EquAlity Research Team (SDHEART) consortium to promote collaboration among researchers, stakeholders, and the community through workshops, hackathons, and educational initiatives. This project aligns with NSF’s mission to promote the progress of science and advance national health, prosperity, and welfare by addressing a critical societal challenge and fostering STEM education and diversity. The interdisciplinary research project leverage deep learning models and geospatial artificial intelligence (GeoAI) to detect and analyze the spatiotemporal patterns of encampments of unhoused persons. By integrating multiple data sources, including street views, remote sensing imagery, and GIS databases, the project will employ innovative GeoAI and Big Data Fusion methods to study migration patterns. The research aims to understand the socio-environmental determinants and impacts of homelessness on local neighborhoods through mapping, surveys, and interviews. The project also establishes the SDHEART consortium to facilitate sustainable research and policy development by hosting data hackathons, workshops, webinars, and an exhibition. This initiative enhances the research capacity at SDSU, contribute to computational social science, and provide educational opportunities for students in geography, sociology, urban studies, and related fields. The project's outcomes will offer valuable insights into the dynamics of homelessness and serve as a model for similar research in other communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.