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Spatial clustering: Influence of urban street networks on retail sales volumes

As is often believed that the more centrally located a shop, the higher its sales volume, this paper analyzed relationships between the spatial clustering of retail stores, their respective transaction volumes, and the urban street networks to determine whether, and to what extent, the accessibility and density of a store’s location was correlated with its transaction volume. While this hypothesis is widely accepted, its veracity is underexplored and rarely validated using large-scale empirical datasets, possibly owing to the lack of access. Therefore, transaction datasets and accessibility indicators were first examined; a clear, positive correlation between density and revenue was found for specialty stores wherein people do “comparison shopping,” and for stores that complemented each other for activities such as “one-trip shopping,” the revenues were positively correlated when the stores were clustered. Generally, daily-use stores’ revenues were more sensitive to local access and those of non-daily-use stores were more sensitive to global access. In conclusion, these findings would not have been found using conventional methodology focused on the retail sector as a whole, because aggregate market mechanisms would have hidden the observed effects on specific store categories. Therefore, upon disaggregating the data, we found a distinct heterogeneity across the different store types for what concerns the relationship between revenue and location.

Environment and Planning B: Urban Analytics and City, 2020

Autori esterni: Yuji Yoshimura (Univ. of Tokyo), Juan Murillo Arias (BBVA), Siqi Zheng (MIT), Carlo Ratti (MIT)
Autori IIT:

Tipo: Contributo in rivista ISI
Area di disciplina: Computer Science & Engineering

File: EPB-2020.pdf

Attività: Algoritmica per reti wireless