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Measuring multi-spatiotemporal scale tourist destination popularity based on text granular computing.

Chi Yunxian^1,2, Li Renjie^1,2, Zhao Shuliang³

¹College of Resources and Environment Science, Hebei Normal University, Shijiazhuang, Hebei, China.

|April 10, 2020

Summary

A new algorithm, tourist destination popularity multi-spatiotemporal text granular computing (TDPMTGC), effectively identifies hidden patterns in user-generated content (UGC). This method quantifies tourist destination popularity across multiple scales, offering detailed insights into tourist behavior and preferences.

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Area of Science:

Geographic Information Science (GIScience)
Data Mining
Tourism Research

Background:

User-generated content (UGC) is a valuable data source for tourism GIScience.
Existing methods lack effective approaches for identifying spatiotemporal patterns in multi-scale, unstructured UGC.
Accurate measurement of tourist destination popularity (TDP) across various scales is challenging.

Purpose of the Study:

To develop a novel algorithm for measuring TDP from unstructured UGC.
To enable the identification of hidden spatiotemporal patterns within UGC at multiple scales.
To provide a systematic framework for quantitative description and comparison of TDP.

Main Methods:

Development of the tourist destination popularity multi-spatiotemporal text granular computing (TDPMTGC) model.
Utilizing tourism text data granules to represent landscape objects with spatial and temporal attributes.
Employing a multi-hierarchical granular computing structure for multi-spatiotemporal scale characterization and transformations.

Main Results:

TDPMTGC successfully quantifies TDP at single and multi-spatiotemporal scales.
The model reveals detailed characteristics, including contributions of scenic spots, monthly anomalies, and daily TDP variations.
A case study in Jiuzhaigou demonstrated consistency with existing studies and provided novel granular insights.

Conclusions:

TDPMTGC offers a feasible scheme for reorganizing large-scale unstructured text and constructing spatiotemporal UGC tourism datasets.
This granular computing approach is the first introduced to tourism GIScience for UGC analysis.
TDPMTGC provides a new methodology for exploring tourist behaviors and the driving mechanisms of tourism patterns.