We introduce a version of the Huff retail expenditure model, where retail demand depends on households’ access to retail centers. Household-level survey data suggest that total retail visits in a system of retail centers depends on the relative location pattern of stores and customers. This dependence opens up an important question—could overall visits to retail centers be increased with a more efficient spatial configuration of centers in planned new towns? To answer this question, we implement the model as an Urban Network Analysis tool in Rhinoceros 3D, where facility patronage can be analyzed along spatial networks and apply it in the context of the Punggol New Town in Singapore. Using fixed household locations, we first test how estimated store visits are affected by the assumption of whether shoppers come from homes or visit shops en route to local public transit stations. We then explore how adjusting both the locations and sizes of commercial centers can maximize overall visits, using automated simulations to test a large number of scenarios. The results show that location and size adjustments to already planned retail centers in a town can yield a 10% increase in estimated store visits. The methodology and tools developed for this analysis can be extended to other context for planning and right-sizing retail developments and other public facilities so as to maximize both user access and facilities usage.
Prediction of Cascading Failures in Spatial Networks