Hotel location evaluation: A combination of machine learning tools and web GIS

Yang Yang, Jingyin Tang, Hao Luo, Chun Hung Roberts Law

Research output: Journal article publicationJournal articleAcademic researchpeer-review

62 Citations (Scopus)


The need for a reliable, unbiased, and objective assessment of hotel location has always been important. This study presents a new approach to evaluate potential sites for proposed hotel properties by designing an automated web GIS application: Hotel Location Selection and Analyzing Toolset (HoLSAT). The application uses a set of machine learning algorithms to predict various business success indicators associated with location sites. Using an example of hotel location assessment in Beijing, HoLSAT calculates and visualizes various desirable sites contingent on the specified characteristics of the proposed hotel. The approach shows considerable potential usefulness in the field of hotel location evaluation.
Original languageEnglish
Pages (from-to)14-24
Number of pages11
JournalInternational Journal of Hospitality Management
Publication statusPublished - 1 May 2015


  • Hotel location
  • Machine learning
  • Spatial decision making
  • Web GIS

ASJC Scopus subject areas

  • Tourism, Leisure and Hospitality Management
  • Strategy and Management


Dive into the research topics of 'Hotel location evaluation: A combination of machine learning tools and web GIS'. Together they form a unique fingerprint.

Cite this