Spatial Clustering based Meteorological Fields Construction for Regional Vulnerability Assessment

Taemin Lee, Woosung Choi, Jongryuel Sohn, Kyongwhan Moon, Sanghoon Byeon, Wookyun Lee, Soonyoung Jung


Chemical accidents have affected the social-environmental system. For the regional vulnerability assessment, which is the baseline work to assess the impact on the environment, a meteorological field is needed to determine how chemicals from multiple adjacent companies are propagated. In this study, we present the method of meteorological field based on the spatial cluster which is the main component of vulnerability assessment on regional chemical accident scenario. To integrate spatially dense chemical companies into a cluster, we adopt spatial clustering algorithms. Experiment result shows that DBSCAN-based approach reduces 80.5% total area of the meteorological field against brute-force algorithm, and shows good performance on the average of the overlap ratio, and utility ratio for clustering results.


spatial clustering; vulnerability assessment; meteorological field; DBSCAN

Full Text:



J. Park, Domestic and international environmental restrict and plan for reaction against chemical industry, no. 44, 2011, pp. 2–3..

Huang, P. & Zhang, J., 2015. Facts related to August 12, 2015 explosion accident in T ianjin, C hina. Process Safety Progress, 34(4), pp.313–314.

Chemical Safety Clearing-house,

J.C. Belke, Loss Prevention and Safety Promotion in the Process Industries, in: Proceeding of the 10th International Symposium, June 19–21, Stockholm, Sweden, pp. 1275–1314.

Eakin, H. & Luers, A.L., 2006. Assessing the vulnerability of social-environmental systems. Annual review of environment and resources, 31.

C. Zhang, O. Selinus, Spatial analysis for copper, lead, zinc contents in sediments of the Yangtze River basin, Sci. Total Environ. 204 (3) (1997) 251–262.

Heo, S. et al., 2017. Chemical accident hazard assessment by spatial analysis of chemical factories and accident records in South Korea. International Journal of Disaster Risk Reduction.

J. Lahr, L. Kooistra, Environmental risk mapping of pollutants: state of the art and communication aspects, Sci. Total Environ. 408 (2009) 3899–3907.

M.C. Olmo, J.A.L. Espinar, V.R. Galiano, E.P. Iguzquilza, L.C. Rivas, Categorical indicator Kriging for assessing the risk of groundwater nitrate pollution: the case of Vega de Granada aquifer (SE Spain), Sci. Total Environ. 470–471 (2014) 229–239.

Li, F. et al., 2010. Mapping human vulnerability to chemical accidents in the vicinity of chemical industry parks. Journal of hazardous materials, 179(1-3), pp.500–6.

T.Lee et al, 2016, Improvement of position accuracy of geocoded coordination based on Ensemble method, 2016 KIPS spring conference proceeding. 23 (1), pp. 818-819

Sammour, M. & Othman, Z., 2016. An Agglomerative Hierarchical Clustering with Various Distance Measurements for Ground Level Ozone Clustering in Putrajaya, Malaysia. International Journal on Advanced Science, Engineering and Information Technology, 6(6), pp.1127–1133.

Shodiq, M.N. et al., 2018. Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia. JOIV: International Journal on Informatics Visualization, 2(1), pp.37–43.

Ester, M. et al., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd. pp. 226–231.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development