High-End Hotel Location Evaluation and Prediction in Nanjing City: A Data-Driven Approach Using Multi-Source Spatial Data and Machine Learning

Yifeng Liang (), Muzaffer Uysal and Irem Onder
Additional contact information
Yifeng Liang: University of Massachusetts at Amherst
Muzaffer Uysal: University of Massachusetts at Amherst
Irem Onder: University of Massachusetts at Amherst

A chapter in Information and Communication Technologies in Tourism 2025, 2025, pp 141-152 from Springer

Abstract: Abstract This study presents a data-driven approach to evaluating and predicting high-end hotel locations in Nanjing City using multi-source spatial data and machine learning. By analyzing the spatial patterns of high-end hotels in Nanjing and constructing a feature matrix from 27 spatial factors, the Random Forest model predicts suitable future hotel locations. The model's performance demonstrates high predictive accuracy (AUC = 0.989). Results indicate that high-end hotels are clustered in central districts, particularly Xuanwu and Gulou districts. Furthermore, the suitability maps generated by the model identified several hotspot areas where new high-end hotels are likely to thrive. The model’s predictions also highlighted potential locations in emerging districts where current hotel density is low but future demand is expected to grow. This integrative approach enhances strategic planning in the hospitality industry, which offers practical insights for investors and urban planners seeking to optimize hotel locations in rapidly growing cities. The research contributes to bridging the gap between spatial data analysis and predictive modeling in hotel site selection.

Keywords: Hotel location; Location prediction; Spatial analysis; GIS; Machine learning; Random forest model (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-83705-0_12

Ordering information: This item can be ordered from
http://www.springer.com/9783031837050

DOI: 10.1007/978-3-031-83705-0_12

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().