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

Airbnb, hotels, and saturation of the food industry: A multi-scale GWR approach