Strategic placement of urban agriculture: A spatial optimization approach. Issue 1 (13th December 2021)

• Record Type: Journal Article
• Authors: Thapa, Bhuwan; Banerjee, Aniruddha; Wilson, Jeffrey; Hamlin, Samantha
• Is Part Of: Urban agriculture & regional food systems. Volume 6:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-13
• Subjects: Urban agriculture -- Periodicals; Agriculture -- Social aspects -- Periodicals; Agriculture -- Economic aspects -- Periodicals; Agriculture -- Economic aspects; Agriculture -- Social aspects; Urban agriculture; Periodicals; 338.17
• DOI: 10.1002/uar2.20019
• Languages: English
• ISSNs: 2575-1220

Abstract

Abstract: Strategic placement of urban agriculture such as community gardens can expand alternate food supply, support physical activity, and promote social interactions. While social and health benefits are critical priorities when planning new urban agriculture locations, no widely accepted site selection methods have been established. We developed a spatial optimization model to identify new urban agriculture locations in the City of Indianapolis, Marion County, Indiana. Considering block groups with vacant parcels as potential locations, the study uses p ‐median optimization to identify the 25 best locations that would minimize travel from any block group in the city to potential garden locations. We weighted each block group based on food access and prevalence of obesity, where food access was characterized on three dimensions: economic, geographical, and informational. The model was simulated for three policy scenarios with equal, stakeholder‐driven, and obesity‐driven weights, and the results were compared with randomly selected locations. We found that optimally selected locations were 52% more efficient than randomly chosen locations in terms of the average distance traveled by residents based on the p ‐median solution. However, there was no significant difference in travel distance among the three policy scenarios. The spatial optimization model can help policymakers and practitioners strategically locate urban agriculture sites. Core Ideas: The study uses p ‐median optimization to identify new urban agriculture locations. p ‐Median optimization minimizes travel distance while incorporating parameters of food access and obesity. Optimally selected locations were 52% more efficient than randomly chosen locations. No significant differences were noted in travel distance among the policy scenarios.