Reseach activities at ACM SIGSPATIAL2024
Research presentations and organizing GeoPrivacy Workshop at ACM SIGSPATIAL2024.
Publications & Talks
International Conference · 2024
Balancing Privacy and Planning: Using Counterfactuals to Predict and Optimize Tourism in Wakayama City
Abstract
Leveraging predictive machine learning models on geo-spatial GPS data to analyze and predict tourist visits to Wakayama Castle, this study offers insights for urban design and policy-making. Employing counterfactual analysis, a novel approach in the tourism planning field, helps uncover key factors influencing visitation patterns and provide strategic recommendations for enhancing tourism. The comprehensive and targeted dataset of anonymized data, encompassing month long movement data, is used to train classification and regression models, which are compared and the best performing ones (Random Forest and CatBoost) are used in the counterfactual analysis. The counterfactual analysis demonstrates how changes in key factors, such as trip distance and start location, can significantly influence visitation decisions, allowing for targeted interventions to increase tourist activity. This research not only advances tourism management strategies but also prioritizes data privacy and user anonymity.
Research Note
Understanding visitor mobility in public spaces is essential for optimizing transportation services and improving accessibility. This study investigates mobility patterns within Wakayama Castle Park using questionnaire-based data. Participants reported both their thought destination (the locations they initially planned to visit using mobility vehicles) and their actual destination (the locations they ultimately reached using mobility vehicles). By reconstructing individual mobility trajectories from these responses, we quantify the difference in distance between intended and actual travel routes. Through statistical and counterfactual analyses, we identify key factors influencing deviations in mobility behavior, such as accessibility constraints, environmental conditions, and decision-making processes. The findings offer insights into user navigation challenges and inform the design of more effective mobility services for historical and recreational sites.
Published paper
- Mishra, M., Kala, S. M., Amano, T., & Yamaguchi, H. (2024, October). Balancing Privacy and Planning: Using Counterfactuals to Predict and Optimize Tourism in Wakayama City. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies (pp. 25-30). https://dl.acm.org/doi/abs/10.1145/3681768.3698504
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