Spatial Modeling
Recommendation
Book Chapter:
- Article
- China
- Econometrics, Spatial Modeling
Zhou, Bo; Li, Zhao Rui; Yang, Yang
- Article
- China
- Spatial Modeling, Big Data
Wang, Shenghong; Yang, Yang; Tan, Yuwei; Chen, Jiaqi; Hu, Sike; Liu, Jun
- Article
- China
- Econometrics, Spatial Modeling
Tian, Fengjun; Wen, Zhihong; Yang, Yang
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
- Invited Talk
- 03/04/2024
COVID19tourism Index and its application in tourism management
University of Perpignan
Perpignan, France
- Invited Talk
- 03/08/2023
Machine Learning and Artificial Intelligence Research in Tourism and Hospitality
University of Macau
Macau (Online)
- Invited Talk
- 09/17/2021
Tourist behavior analysis using online user generated data
Kyung Hee University
Seoul, Korea (Online)
Related Resources
- 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.
- Dataset
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.
- Tool
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
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