Airbnb, hotels, and saturation of the food industry: A multi-scale GWR approach
Clicks: 64
ID: 282977
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
18.9
/100
64 views
45 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #405 of 803 articles by views in arXiv
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 803 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
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.
| Reference Key |
ming2019airbnb
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Zahratu Shabrina; Boyana Buyuklieva; Matthew Ng Kok Ming |
| Journal | arXiv |
| Year | 2019 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.