Narong Pleerux

Tourism destinations increasingly serve both domestic and international visitors whose geographic behaviors may differ substantially, yet most analytical frameworks treat visitor distributions as spatially homogeneous. Few studies compare how domestic and international tourists cluster spatially within the same destination. Those differences matter enormously for destinations where visitor segments follow distinct geographic patterns. We analyzed 1547 bilingual TripAdvisor reviews from Chanthaburi Province, Thailand (2014–2023), combining Random Forest classification (83.26% accuracy for Thai, 96.45% for English) with Incremental Spatial Autocorrelation (ISA), Global Moran’s I, and Getis-Ord Gi* hotspot analysis. International visitors clustered more intensely overall (I = 0.253 vs. 0.213), but domestic visitors spread across all six tourism areas including agrotourism, while international visitors were concentrated in heritage, coastal recreation, and nature-temple zones with agrotourism absent. Both segments clustered strongly at cultural heritage sites and at beach destinations, contradicting the common assumption that coastal areas primarily serve international visitors, while agrotourism clustered exclusively among domestic visitors despite active policy promotion. These patterns reflect differential information access rather than attraction quality. The zone-level framework is transferable to secondary heritage destinations across Southeast Asia, where platform-based monitoring offers a practical alternative to large-scale visitor surveys.

This study develops a rapid, data-driven approach to detect operational sustainability gaps in island tourism by applying machine-learning sentiment analysis to user-generated TripAdvisor reviews of Koh Lan (2015–2024). A corpus of 2,388 English- language reviews was preprocessed and classified with five supervised algorithms; a Support Vector Machine (SVM) produced the best performance (accuracy 87.29%, F1 75.38%). Although the majority of reviews were positive (91.57%), a small negative subset (90 reviews, 4.25%) contained concentrated, actionable signals. Qualitative coding of negative reviews revealed five recurring problem dimensions—Scenery (37.29%), Staff and Service (29.38%), Facility (19.21%), Safety (10.73%), and Accessibility (3.39%)—yielding 179 distinct issue mentions. Each identified problem was mapped onto specific SDG targets and assessed against the Blue Economy pillars (environmental sustainability, economic resilience, and social equity), exposing persistent weaknesses in marine and waste management, infrastructure resilience, service quality, and institutional governance. The paper translates these diagnostics into a pragmatic “Blue Tourism” policy package—zero-plastic and blue-carbon initiatives, eco-infrastructure upgrades, eco-service certification, and a participatory marine governance and risk committee—to shift Koh Lan from a short-term, extractive tourism model toward regenerative stewardship. Methodologically, the study demonstrates that ML-enabled monitoring of online reviews can provide timely, high-resolution intelligence to guide sustainable destination management.

Tourism significantly contributes to the economic development of regions with rich natural and cultural heritage, such as Chanthaburi Province, Thailand. This study leverages Natural Language Processing (NLP) and the Random Forest machine learning algorithm to analyze 1,547 tourist reviews from TripAdvisor, spanning 2014–2023. The reviews, written in Thai and English, were categorized into positive, neutral, and negative sentiments, achieving classification accuracies of 83.26% and 96.15%, respectively. Keyword frequency analysis using Term Frequency-Inverse Document Frequency (TF-IDF) highlighted key themes influencing tourist experiences, including appreciation for cultural landmarks and concerns about overcrowding and ethical issues related to wildlife performances. The findings provide valuable insights for tourism stakeholders in Chanthaburi Province to enhance visitor experiences and develop targeted improvement strategies. The study demonstrates the effectiveness of machine learning approaches in analyzing multilingual tourist feedback for destination management and marketing optimization.

This study presents the distribution of road accidents in Bangkok, Thailand, during the COVID-19 outbreak (162 days) using spatial statistics. The findings show that the number of road accidents fell by 28.53% during the lockdown period (March 26 to June 14, 2020) compared to before the lockdown period (January 5 to March 25, 2020). There was also a considerable decrease in road accidents by approximately 4.96% compared to the same period in 2019 (January 4 to June 14, 2019). High accident incidence areas were located in central and southwestern Bangkok, and this pattern was also observed before and during the lockdown as well as in 2019. The findings revealed the numbers and patterns of road accidents that occurred during the COVID-19 outbreak. This information can be useful in minimizing and limiting the impact and damage caused by road accidents in Bangkok. Keywords: COVID-19; lockdown; road accident; Moran’s I; spatial autocorrelation *Corresponding author: Tel.: (+66) 822313386 Fax: (+66) 38102379 E-mail: narong_p@buu.ac.th