Integrating transit data and travel time into food security analysis: A case study of San Diego, California

Abstract

Research suggests an important role for planners and policymakers in enhancing neighborhood access to healthy food. Spatial analysis is often used to explore relationships between proximity to sources of food and health outcomes such as food security and risk for diet-related disease. Spatial models often account for proximity based on walking distance or automobile but advancements in geospatial technologies now allow for estimates of access by transit as well as travel time to understand neighborhood level food security. One approach for addressing this gap is to employ real-time travel estimates over transit and road networks to more precisely measure food access. In this paper, we compare the Google Distance Matrix API calculating both the distance and time between origins and destinations, with other spatial approaches to understanding grocery store and full-service supermarket access in this case study of San Diego, California. Results suggest that distances between residents and food outlets are constant across modes of transport but travel times are highly differentiated by mode. Trips to stores by transit are on average, three times longer than trips by car. This paper offers a new understanding of the potential for incongruence between spatial proximity and true accessibility in the built environment.