  This paper evaluates the relationship between the food industry and the local
variations of temporary accommodation (TAs, including hotels and short-term
rentals). The aim is to capture the variance of the local statistic and
pinpoint areas where food and beverages (F&B) presence is highly related to TAs
in London. We explain the phenomena using OLS and compare the result with the
local model - Geographically Weighted Regression (GWR) and multi-scale GWR
(Fotheringham et al., 2017) allowing the use of different optimal bandwidths
instead of assuming that relationship varies at the same spatial scale. The
comparison is presented and the result shows that the GWR model shows
significant improvement over Ordinary Least Square (OLS), increasing the
R-squared from 0.28 to 0.75. MGWR further improves the model estimate,
increasing the R-squared to 0.77, indicating the relationship happens in
different spatial scales. Lastly, as an estimate for F&B, hotels appear to
perform better in a high concentration of commercial and transport links
functions, whilst Airbnb seems to perform better in highly residential areas
proximate to the mainstream tourist attractions. Overall, this paper describes
the use of the MGWR method in cases where localities is an important aspect of
the spatial analysis process.

Physics and Society