Estimating small-area demand of urban tourist for groceries: The case of Greater London

Tourist retail demand within urban areas brings both opportunities and challenges to the local economy. Taking Greater London as the study area, this paper integrates conventional statistics and survey datasets with novel crowdsourcing big data sources to identify and estimate four types of tourist grocery demand at the small-area scale: travellers staying with Airbnb, tourists using traditional commercial accommodation, guests staying with relatives or friends and day trip visitors. Based on this combined tourist retail demand layer we show the spatial variations at the small-area level and as an illustration of the demand uplift, we estimate additional grocery expenditure that is associated with this tourist demand. Thus, the paper indicates the neighbourhoods with significant grocery demand uplift from tourist stays. We argue that the new retail demand layer has tremendous potential to be used as an additional input to retail location modelling tools to support new store revenue estimation and store performance evaluation within the grocery retail sector.