Research

Key Research Areas

Tourist Flows and Location

Leveraging geospatial tool to predict tourist spatial behaviors and optimize destination management strategies.

AI and Big Data in Tourism

Leveraging machine learning and LLMs to predict tourist behaviors and optimize destination management strategies.

Sustainability and Resilience

Analyzing sustainability and risk on tourism and hospitality businesses and developing resilience models.

Digital Platforms and Pricing

Investigating the online behavior of tourists and organizations in digital platforms and pricing strategy.

Methodological Expertise

Big Data

Processing terabytes of unstructured data to reveal hidden patterns.

Econometrics

Advanced causal inference and time-series analysis for robust analysis.

Spatial Modeling

Geo-spatial tools to understand the spatial dimension and decision support.

Meta Analysis

Synthesizing results from different studies and understanding heterogeneity

Research Outputs

2025

Does participation in domestic and international tourism activities affect life happiness? Evidence from the US counties

Yang, Y; Fu, XX; Lin, BN

TOURISM ECONOMICS

2012

A spatial econometric approach to model spillover effects in tourism flows

Yang, Y; Wong, KKF

JOURNAL OF TRAVEL RESEARCH

2023

Why do hotels go green? Understanding TripAdvisor GreenLeaders participation

Yang, Y; Jiang, L; Wang, YW

INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT

Measuring inter-regional tourism in the United States: Integrating passenger tracking and household survey data through a gravity lens​.

Yang, Y. and Xiong, C.

International Association for Tourism Economics [IATE] 2026 Conference.

Palermo, Italy

Event desert: Identifying determinants of cultural and social inequality in community leisure.

Hwang, G. and Yang, Y.

2026 TALS Research and Teaching Conference.

Philadelphia, Pennsylvania

Feast or famine: A big and deep data approach to event impacts on restaurant revenue.

Hwang, G. and Yang, Y.

The 31st Annual Graduate Education & Graduate Student Research Conference in Hospitality & Tourism.

Auburn, Alabama

Tourism Experience and Human Well-being

2025 International Conference on Consumption Studies (ICCS)

Changsha, China (Online)

Geo-located big data and sport tourism

3rd High-Level Forum on Sports Tourism and Symposium on the Development of Sports Tourism Management

Huangshan, China

Meta-Analysis in Tourism Economics

Tourism Economics in Focus, The IATE Research Seminar Series

Online

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.

Restaurant Resilience Index

The Restaurant Resilience Index was developed to characterize the regional restaurant industry’s resilience to the COVID-19 pandemic across U.S. counties. Estimated from econometric results regarding daily restaurant demand, this index incorporates key moderating variables—specifically ethnicity, political ideology, dining habits (eat-in vs. off-premise), and restaurant diversity—that were found to influence the magnitude of demand decline caused by the pandemic and stay-at-home orders. By visualizing these data, potentially through tools like an ArcGIS dashboard, the index enables government entities and stakeholders to pinpoint geographically vulnerable areas and effectively allocate support resources, such as consumer voucher programs, to the hardest-hit local businesses.

 

Link to the Restaurant Resilience Index dashboard.

COVID19tourism Index

The COVID19tourism index was developed to monitor the pandemic’s multifaceted impact on the global tourism industry. This index comprises five distinct sub-indices designed to track the specific effects of COVID-19 across various aspects of tourism activities. By utilizing this tool, destinations are enabled to assess their recovery status, generate rigorous forecasts, and benchmark their performance against potential competitors. Sub-indices The COVID19tourism index is comprised of five distinct sub-indices. These sub-indices were designed to track the specific effects of the pandemic across different aspects of tourism activities.

Dashboard Utility The index functions as a tool that enables destinations to perform three primary functions:
• Evaluate Recovery: Destinations can use the tool to assess their current recovery status.
• Forecast: The tool allows users to produce rigorous forecasts regarding tourism trends.
• Benchmark: Destinations can use the index to benchmark their performance against potential competitors

Link to the COVID19tourism Index Dashboard

Link to download the data

Tourist Experience Simulation Tool

Tourist Experience Simulation Tool is a Web-GIS system designed to help tourism practitioners monitor and simulate tourist experiences under varying environmental conditions. This tool allows users to input specific scenarios defined by air pollution levels (specifically PM2.5), weather conditions (temperature, sun, wind, and precipitation), and date types (e.g., weekends or holidays). Utilizing a predictive algorithm derived from the sentiment analysis of geotagged social media posts, the system calculates “experience scores” to visualize the spatial distribution of tourist satisfaction across the city. This platform enables stakeholders to conduct scenario analyses, such as predicting experience fluctuations during heavy pollution events, and offers features for benchmarking specific locations and recommending itineraries that mitigate the negative impacts of poor air quality