Research
Homelessness is a national crisis reflecting social inequality and exacerbating health disparities in the U.S. Developing a comprehensive understanding of the spatial distribution and migration patterns of the homeless population can help policymakers and government officials design sustainable solutions and intervention policies. However, current homeless locational surveys, such as Point-In-Time (PIT) counts are conducted only once a year and require hundreds of volunteers and significant resources.
This study will create an innovative research framework (Figure 1) to combine geospatial artificial intelligence (GeoAI) tools and social science research methods (surveys and qualitative analysis) together to visualize and study the dynamic changes of the homeless population in the County of San Diego (as our case study area). Specifically, there are three research goals of this project:
Goal 1: Analyzing the spatiotemporal migration patterns of the homeless population in a U.S.-Mexico Border region (the County of San Diego) using innovative geospatial artificial intelligence (GeoAI) and Big Data Fusion methods (integrating GIS data, census data, street views, remote sensing imagery, and crowd-sourced data).
Goal 2: Understanding the socio-environmental determinants and impacts of the homeless population on local neighborhood communities with mapping and surveys, and developing possible decision support tools and suggestions for decision makers, and health care management staff in order to provide comprehensive support for the homeless population.
Goal 3: Creating a consortium of “San Diego Homeless and health EquAlity Research Team (SDHEART)” to facilitate sustainable homeless research collaboration in San Diego communities, and to enhance computational social science research capacity at San Diego State University by hosting data hackathons and workshops and creating webinars and education materials.
