IDENTIFYING THE HAZARDOUS LOCATIONS USING GEOGRAPHIC INFORMATION SYSTEM NETWORK ANALYSIS: A CASE STUDY OF BUENG KAN PROVINCE, THAILAND
Identifying the Hazardous Locations: Case study of Bueng Kan Province
DOI:
https://doi.org/10.55766/sujst9950Keywords:
Spatial analysis, Kernel Density Estimation, Chi-square test, Local policy, Practical implicationsAbstract
This study aims to identify high - crash - risk locations on local roads in Bueng Kan province, Thailand, using Geographic Information System (GIS) - based spatial analysis. Despite being Thailand’s newest and one of its least populated provinces, Bueng Kan consistently reports one of the highest fatality rates per capita, highlighting the urgency of evidence-based road safety planning. The study employed Kernel Density Estimation (KDE) to detect spatial concentrations of road crashes and a Chi-square test to assess whether crash severity proportions differed significantly across districts. Crash data from 2018 to 2020 were collected, georeferenced using linear referencing techniques, and analyzed in ArcMap. KDE was applied to generate crash density surfaces. The KDE results revealed spatially distinct crash hotspots in five districts, with Seka district showing the highest crash density despite Mueang Bueng Kan recording the highest number of incidents. This underscores the importance of considering spatial overlap-not just frequency-in identifying hazardous areas. Chi-square test found no statistically significant difference in the proportion of fatal versus non-fatal crashes among districts (p = 0.403), suggesting that crash severity is spatially uniform. This supports the need for province-wide interventions rather than district-specific responses. The integrated use of KDE and statistical testing provides a robust analytical framework for supporting local traffic safety strategies. Recommendations include incorporating exposure data and advanced modeling techniques in future studies to improve risk prediction and policy development.
References
Agyemang W., Adanu E.K. and Jones S. (2021) Understanding the Factors That Are Associated with Motorcycle Crash Severity in Rural and Urban Areas of Ghana. Journal of Advanced Transportation, 2021(1):6336517. https://doi.org/10.1155/2021/6336517
Amiri AM, Navid N, Vahid K, and Shams M (2021) GIS-based crash hotspot identification: a comparison among mapping clusters and spatial analysis techniques. International Journal of Injury Control and Safety Promotion, 28(3):325-338. https://doi.org/10.1080/17457300.2021.1925924
Anderson T.K. (2009) Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention, 41(3):359-364. https://doi.org/10.1016/j.aap.2008.12.014
Chayphong S. and Iamtrakul P. (2023). Effects of Age on Urban Road Traffic Accident Using GIS: A Case Study of Bangkok Metropolitan Area, Thailand. Paper presented at the Proceedings of the 2nd International Civil Engineering and Architecture Conference, Singapore.
Division of Injury Prevention. (2025). Road accident death situation in Thailand. Retrieved from https://dip.ddc.moph.go.th/new/. (accessed on 2 April 2025).
Gedamu W.T., Plank-Wiedenbeck U. and Wodajo B.T. (2024) A spatial autocorrelation analysis of road traffic crash by severity using Moran’s I spatial statistics: A comparative study of Addis Ababa and Berlin cities. Accident Analysis & Prevention, 200:107535. https://doi.org/10.1016/j.aap.2024.107535
Harirforoush H. and Bellalite L. (2019) A new integrated GIS-based analysis to detect hotspots: A case study of the city of Sherbrooke. Accident Analysis & Prevention, 130:62-74. https://doi.org/10.1016/j.aap.2016.08.015
Iamtrakul P., Chayphong S., and Zhang J. (2025) Spatially heterogeneous impacts of built environment factors on traffic accidents: A multi-year analysis (2015–2021) in Suburban Bangkok, Thailand. Transportation Engineering 20:100324. https://doi.org/10.1016/j.treng.2025.100324
Karacasu M. and Er A. (2011) An analysis on distribution of traffic faults in accidents, based on driver's age and gender: Eskisehir case. Procedia-Social and Behavioral Sciences 20:776-785. 10.1016/j.sbspro.2011.08.086.
King T. (1997) A Guide to Chi-Squared Testing. Technometrics 39(4):431-431. 10.1080/00401706.1997.10485175.
Le K.G., Pei L., and Lin L-T. (2022) Traffic accident hotspot identification by integrating kernel density estimation and spatial autocorrelation analysis: a case study. International Journal of Crashworthiness, 27(2):543-553. https://doi.org/10.1080/13588265.2020.1826800
Loo, B.P.Y., Yao, S., and Wu, J. (2011). Spatial point analysis of road crashes in Shanghai: A GIS-based network kernel density method. 19th International Conference on Geoinformatics, Shanghai, China, 2011, pp. 1-6. https://doi.org/10.1109/GeoInformatics.2011.5980938
Ministry of Transport. (2022). Accidents Statistics on the road network. Retrieved from https://datagov.mot.go.th/dataset/roadaccident
Mohaymany, A.S., Shahri, M., and Mirbagheri, B. (2013). GIS-based method for detecting high-crash-risk road segments using network kernel density estimation. Geo-Spatial Information Science, 16(2):113-119. https://doi.org/10.1080/10095020.2013.766396
Oluwadiya KS, Dele OO, Oladiran AO, Charles M, and Popoola OS (2016) Vulnerability of motorcycle riders and co-riders to injuries in multi-occupant crashes. International Journal of Injury Control and Safety Promotion 23(2):189-196. http://dx.doi.org/10.1080/17457300.2014.969280
Provincial Labour Office Buengkan. (2025). Map of Bueng Kan Province. Retrieved from https://buengkan.mol.go.th/en/overall/province-map (accessed on 2 April 2025).
Pulugurtha S.S., Krishnakumar V.K., and Nambisan S.S. (2007) New methods to identify and rank high pedestrian crash zones: An illustration. Accident Analysis & Prevention, 39(4):800-811. https://doi.org/10.1016/j.aap.2006.12.001
Thakali L., Kwon T.J., and Fu L. (2015) Identification of crash hotspots using kernel density estimation and kriging methods: a comparison. Journal of Modern Transportation, 23(2):93-106. https://doi.org/10.1007/s40534-015-0068-0
