Geographic Bottlenecks and Algorithmic Boundaries in Food Delivery Platforms: Evidence from River-Crossing Constraints in Tokyo

This study examines how food delivery platforms are constrained by seemingly primitive geographic features such as rivers, despite operating through advanced digital algorithms. Using exploratory observations from the Tokyo metropolitan area—specifically the neighborhoods of Akabane (Tokyo) and Kawaguchi (Saitama), which are separated by the Arakawa River but located only one train stop apart—we document a consistent pattern in which restaurants across the river are not mutually visible on Uber Eats, while more distant restaurants located on the same side of the river are displayed. This pattern cannot be adequately explained by physical distance alone. Instead, we propose that delivery platform algorithms implicitly avoid geographic bottlenecks—such as rivers, bridges, tunnels, and other chokepoints—because these locations introduce uncertainty in delivery time. As a result, algorithmic service areas become fragmented along geographic boundaries, producing distinct “delivery culture regions” even within dense urban environments. We further argue that this mechanism has broader implications for the diffusion of ghost kitchens and delivery-oriented food businesses. In regions characterized by dense networks of small rivers and limited bridge connectivity—such as Japan—the effectiveness of delivery-based economic models may be structurally constrained. Consequently, public infrastructure and algorithmic localization emerge as critical, yet often overlooked, factors in the formation of delivery-service-based economies. This paper contributes an exploratory, reproducible observational approach based on consumer-facing platform interfaces and highlights the continued relevance of physical geography in shaping digital platform economies.