Nationwide emergency alert number and dispatch center raises safety and security response for the people of Philippines. It all started with a vision – to improve the daily lives of every Philippine citizen for a safer and more secure community. In this study, data mining technique and data visualization were utilized to determine the incident reports in District Emergency Preparedness and Response Unit (DEPRU) of Misamis Occidental. Thus, self-organizing map (SOM) was used to cluster and visualize the patterns. The results showed that data pertaining to medical services or assistance, fire incidents, police assistance, and other citizens’ concerns are not related to each other or exhibit similar patterns. However, the results might change when there is a sufficient data to be trained during the process of clustering because in this study, the data is only from the few months when DEPRU started their operations. Moreover, the model is significant in relating incidences.

Keywords: District emergency preparedness and response unit, Pure force and rescue corporation, Self-organizing map, Data mining, Data visualization, Clustering.

Pure Force and Rescue Corp (2020). Pure Force Citizens App. Available at: https://play.google.com/store/apps/details?id=com.pureforce&hl=en.

Pure Force- Media. (2020). About Pure Force. Available at https://www.facebook.com/pureforce505/.

Bhardwaj, B. K., & Pal, S. (2012). Data Mining: A prediction for performance improvement using classification. arXiv preprint arXiv:1201.3418.

Chamikara, M. A. P., Bertók, P., Liu, D., Camtepe, S., & Khalil, I. (2020). Efficient privacy preservation of big data for accurate data mining. Information Sciences, 527, 420-443.

Brodlie, K., Osorio, R. A., & Lopes, A. (2012). A review of uncertainty in data visualization. In Expanding the frontiers of visual analytics and visualization (pp. 81-109). Springer London.

Kohonen, Teuvo. "Essentials of the self-organizing map." Neural Networks 37 (2013): 52-65.

Faradonbeh, R. S., Haghshenas, S. S., Taheri, A., & Mikaeil, R. (2020). Application of self-organizing map and fuzzy c-mean techniques for rockburst clustering in deep underground projects. Neural Computing and Applications, 32(12), 8545-8559.

Guo, X., Lin, H., Wu, Y., & Peng, M. (2020). A new data clustering strategy for enhancing mutual privacy in healthcare IoT systems. Future Generation Computer Systems, 113, 407-417.

Ivancsy, R., & Kovacs, F. (2006, February). Clustering techniques utilized in web usage mining. In Proceedings of the 5th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engg. & Data Bases (pp. 237-242).

Lavrač, N., Bohanec, M., Pur, A., Cestnik, B., Debeljak, M., &Kobler, A. (2007). Data mining and visualization for decision support and modeling of public health-care resources. Journal of Biomedical Informatics, 40(4), 438-447.

Kowalski, G. J., & Maybury, M. T. (2006). Information storage and retrieval systems: theory and implementation (Vol. 8). Springer Science & Business Media.

Burch, M., & Timmermans, N. (2020, June). Sankeye: A Visualization Technique for AOI Transitions. In Symposium on Eye Tracking Research and Applications (pp. 1-5).

Aroui, Tarek, YassineKoubaa, and Ahmed Toumi. "Clustering of the self-organizing map based approach in induction machine rotor faults diagnostics." Leonardo Journal of Sciences 15 (2009): 1-14.

Pacella, Massimo, Antonio Grieco, and MarziaBlaco. "On the Use of Self-Organizing Map for Text Clustering in Engineering Change Process Analysis: A Case Study." Computational Intelligence and Neuroscience 2016 (2016).

Ansari, Z. (2014). Web User Session Cluster Discovery Based on k-Means and k-Medoids Techniques. International Journal of Computer Science & Engineering Technology (IJCSET), 1105-1113.

Poornalatha, G., & Raghavendra, P. S. (2011, July). Web user session clustering using modified K-means algorithm. In International Conference on Advances in Computing and Communications (pp. 243-252). Springer Berlin Heidelberg.