Prof. YANG2

Tourist Flows and Location

Why is this topic important? 
Understanding the spatial distribution and movement of tourists is fundamental for destination management, marketing, and infrastructure planning. Tourist flows are not random; they are shaped by destination attractiveness, transport connectivity, and spatial interactions between regions. Analyzing these patterns at both macro (inter-city) and micro (intra-attraction) levels allows stakeholders to manage overcrowding, design better itineraries, and leverage spillover benefits where a region benefits from the tourism growth of its neighbors. Furthermore, understanding location decision-making is critical for businesses, such as hotels, as location is a fixed attribute that significantly influences performance and survival.
 
What has our team done so far? 
Our work has extensively modeled tourist movements using advanced spatial econometrics and tracking technologies. We have investigated the “spillover effects” in tourism flows, confirming that inbound and domestic flows to Chinese cities are spatially autocorrelated and clustered in specific hot-spots like the Yangtze River Delta. Our team has developed a “Tourism Spillover Index” based on a two-stage distance-based model to quantify a region’s potential to benefit from multidestination travel. At a micro-level, we utilized open GPS-trajectory data to identify specific spatiotemporal behaviors in mountainous scenic areas, classifying movement into patterns like “proximity transfer” and “day-climbing”. We also creatively used user-generated travel tips to track discrepancies between “expected” (planned) and “actual” tourist flows, revealing how spatial constraints alter itineraries. Additionally, we examined the role of transport connectivity, finding that air transport generally has a greater influence on intercity flows than rail, though this varies by distance. Finally, regarding business location, we have analyzed hotel distribution patterns and the specific location factors that drive hotel selection, such as agglomeration effects and accessibility.

Recommendation

Book Chapter:

GIS Monitoring of Traveler Flows Based on Big Data

Related Publications (click for all related publication)

  • Book/Chapter
  • Global
  • Econometrics

Comparative Advantage

Mao, Zhenxing and Yang, Yang

2026

Theories and Models in Tourism and Hospitality Research

10.1079/9781800625822.0007

  • Article
  • China
  • Econometrics, Big Data

From words to growth: Unveiling government attention to tourism from natural language processing

Yang, Lisi; Yang, Yang; Huang, Xijia; Yan, Kai

2026

TOURISM MANAGEMENT

10.1016/j.tourman.2025.105264

  • Article
  • US
  • Econometrics, Big Data

Past stays and future standards: How customer-mediated knowledge transfer enhances service quality

Zhang, Xiaowei; Huang, Xingyu; Yang, Yang; Liu, Wei

2026

TOURISM MANAGEMENT

10.1016/j.tourman.2025.105341

Related Presentations

  • Presentation
  • 01/02/2025

Spatial Analytics

Workshop on Informatics, Data Science, and Economics in Hospitality and Tourism Research

University of Houston, Houston, TX

Presentation file

  • Invited Talk
  • 03/04/2024

COVID19tourism Index and its application in tourism management

University of Perpignan

Perpignan, France

Presentation file

Related Resources

  • Dataset

Government Attention to Tourism Data

This web-based application is designed to provide researchers and policymakers with an interactive platform for querying and analyzing data regarding Government Attention to Tourism (GAT) in China.

Based on the research findings of Yang, Yang, Huang & Yan (2026), this tool visually demonstrates the spatiotemporal relationships and statistical correlations between the GAT index and various tourism economic indicators. This tool supports a bilingual interface in both Chinese and English. You can switch between the two language modes in real-time by clicking the language toggle button (中文/EN) located in the top-right corner of the page.

Key Features:

Multidimensional Data Coverage: Includes GAT indices and 11 key tourism economic indicators at both the provincial and prefectural (city) levels.

Interactive Visualization: Provides trend analysis, spatial distribution maps, and statistical correlation scatter plots.

Data Resources: Supports viewing summaries of raw data and downloading data.

 

本网页应用旨在为研究人员和政策制定者提供关于中国政府旅游关注度 (Government Attention to Tourism, GAT) 的交互式数据查询与分析平台。

该工具基于 Yang, Yang, Huang & Yan (2026) 的研究成果，通过可视化手段展示了 GAT 指数与各类旅游经济指标之间的时空关系和统计关联。本工具支持中英文双语界面。点击页面右上角的语言切换按钮（中文/EN），即可在两种语言模式间实时切换。

核心功能：

  • 多维数据覆盖： 包含省级和地级层面的 GAT 指数及 11 项关键旅游经济指标。
  • 交互式可视化： 提供趋势分析、空间分布地图和统计关联散点图。
  • 数据资源： 支持查看原始数据摘要及下载。

Resource link

  • Dataset

Pulse of American Domestic Tourism

“The ‘Pulse of American Domestic Tourism’ project serves as a digital monitor for the nation’s internal mobility. By mining transportation-derived mobility data, we develop a comprehensive matrix of tourism flows connecting American MSAs. This data-driven approach unveils the rhythmic shifts in visitor demand and regional connectivity. Crucially, we ground these digital insights through extensive cross-validation with household survey data, creating a verified, high-resolution framework for understanding the evolving landscape of domestic travel.”

Key Vocabulary Used (Why it works):

  • Inter-MSA travel flows: Specific and accurate to your methodology.
  • Arterial circulation / Rhythmic shifts: Reinforces the “Pulse” metaphor without being cheesy.
  • High-granularity / Spatiotemporal precision: Highlights the “Big Data” advantage.
  • Rigorously cross-validated: Emphasizes the reliability of your model (crucial for academic trust).
  • Ground-truth metrics: A professional way to refer to the survey data as the standard of truth.

Resource link

  • Dataset

Visualization on life happiness and tourism participation of U.S. counties

This web-based application is designed to visualize the longitudinal spatial-temporal relationship between resident life happiness and tourism participation across the United States. It is built upon the empirical research of Yang, Fu, and Lin (2025), which utilized a panel dataset covering over 3,000 U.S. counties to examine how domestic and international tourism activities influence subjective well-being.

Key Features:

  • Spatial Visualization: Displays the geographical distribution of life satisfaction and tourism participation rates on an interactive map using the academic standard Albers USA projection.
  • Correlation Analysis: Dynamically illustrates the statistical relationship between the change in tourism participation and life satisfaction.
  • Distribution Analysis: Provides histograms for all variables to identify data characteristics, skewness, and outliers.
  • Interactive Exploration: Allows users to customize the view by searching for specific counties, toggling years, and adjusting color schemes.

 

Resource link

Prof. Yang Yang (YANG2)

School of Sport, Tourism and Hospitality Management, Temple University

U.S.-Asia Center for Tourism and Hospitality Research

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