Mining crime instance records of Philippine National Police District Vi âA ¸S˘ Province of Cavite, Philippines: An Exploratory Study to Enhance Crime Prevention Programs
Keywords:
Data Envelopment Analysis, Data Mining, Clustering Techniques, Crime Management
Abstract
Aim: This analysis aimed to assess the effectiveness, productivity, and management of one of the country’s national police forces. In this study, especially in the data envelopment analysis, crime management was taken into account as the primary function of the police, which necessitated the use of resources that were viewed as decision-making units.
Methodology: To identify productive DMUs, the study combined a Rate-to-Scale (RTS) metric based on scale efficiency with an input-oriented radial measure of efficiency. In the analytical tasks, clustering was used as part of a structured approach to planning data mining activities known as CRISP-DM.
Findings: Municipality C is the most efficient DMU based on the 3-year scale efficiency result.
Implications/Novelty: Given the current push by the government and the PNP to eradicate criminality and illegal activity in the Philippines, this research couldn’t come at a better time. This research aided police departments and crime scene investigators in identifying patterns of criminal activity and performing geospatial analyses.
References
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Charnes, A., Cooper, W. W., and Rhodes, E. 1978. “Measuring the Efficiency of Decision Making Units.” European Journal of Operational Research 2(6): 429-444.
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Kinaci, H., Najjari, V., and Alp, I. 2016. “Using Data Envelopment Analysis and Stochastic Frontier Analysis Methods to Evaluate Efficiency of Hydroelectricity Centers.” Gazi University Journal of Science 29(1): 167-176.
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Narimatsu, H., Nakata, Y., Nakamura, S., Sato, H., Sho, R., Otani, K., ... and Yamashita, H. 2015. “Applying Data Envelopment Analysis to Preventive Medicine: A Novel Method for Constructing a Personalized Risk Model of Obesity.” PloS One 10(5): 1-11.
Ni, Y., Engstrom, C., Malyarenko, A., and Wallin, F. 2014. “Investigating the Added Values of High Frequency Energy Consumption Data using Data Mining Techniques.” Presented at the AIP Conference Proceedings (pp. 734-743), July 15-18, Narvik, Norway. Norman, M., and Stoker, B. 1991. Data Envelopment Analysis: The Assessment of Performance. New York, NY: John Wiley & Sons, Inc.
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Tran, T. T. H., and Bhaiyat, F. 2016. “Evaluating the Efficiency of Vietnamese Commercial Banks using Data Envelopment Analysis.” Presented at the 6th Annual International Conference on Accounting and Finance, May 30-31 Singapore.
Tsai, B. H., and Chou, S. B. 2015. “Application of Multiple Output Data Envelopment Analysis in Interpreting Efficiency Improvement of Enterprise Resource Planning in Integrated Circuit Firms.” The Journal of Developing Areas 49(1): 285-304.
Vyver, A. G. D. V., and Gillies, D. 2017. “Analysing public perceptions of international events by using geo-located twitter data.” International Journal of Humanities, Arts and Social Sciences 3(2): 64-70.
Wu, Y., Duan, H., and Du, S. 2015. “Multiple Fuzzy C-means Clustering Algorithm in Medical Diagnosis.” Technology and Health Care 23(s2): S519-S527.
Yamuna, S., and Bhuvaneswari, N. S. 2012. “Data Mining Techniques to Analyze and Predict Crimes.” The International Journal of Engineering and Science 1(2): 243-247.
Yang, C. C. 2014. “Evaluating the Performance of Banking Under Risk Regulations: A SlacksâA˘ Rbased Data Envelopment Analysis Assessment Framework.” Expert Systems 31(2): 176-184.
Yu, C. H., Ward, M. W., Morabito, M., and Ding, W. 2011. “Crime Forecasting using Data Mining Techniques.” Presented at the IEEE 11th International Conference on Data Mining Workshops (ICDMW), (pp. 779-786), December 11 Vancouver, Canada.
Banu, M. N., and Gomathy, B. 2014. “Disease Forecasting System Using Data Mining Methods.” Presented at the International Conference on Intelligent Computing Applications (ICICA), (pp. 130-133), March 06-07 Coimbatore, India.
Baran, J., Wysokinski, M., Stas, D., Samolejova, A., and Lenort, R. 2016. “Efficiency of Polish Metallurgical Industry based on Data Envelopment Analysis.” Metalurgija 55(2): 245-248.
Baser, P., and Saini, J. R. 2013. “A Comparative Analysis of Various Clustering Techniques used for Very Large Datasets.” International Journal of Computer Science & Communication Networks 3(5): 271-275.
Becker, G. S. 1968. “Crime and Punishment: An Economic Approach.” Journal of Political Economy 76(2): 169-217.
Bini, B. S., and Mathew, T. 2016. “Clustering and Regression Techniques for Stock Prediction.” Procedia Technology 24: 1248-1255.
Boussofiane, A., Dyson, R. G., and Thanassoulis, E. 1991. “Applied Data Envelopment Analysis.” European Journal of Operational Research 52(1): 1-15.
Caballero, E. R., Cadavid, V. D., and Toloza, A. J. M 2015. “Efficiency in the Public Educational Institutions of the City of Santa Marta (Colombia) Through Enveloping Data Analysis.” Ingeniare, Chilean Engineering Journal 23(4): 579-593.
Ceccato, V., and Uittenbogaard, A. C. 2014. “Space-time Dynamics of Crime in Transport Nodes.” Annals of the Association of American Geographers 104(1): 131 150.
Chakraborty, J. 2016. “Efficiency Analysis of Indian Life Insurance Firms: A DEA Investigation.” SCMS Journal of Indian Management 13(3): 5-24.
Charnes, A., Cooper, W. W., and Rhodes, E. 1978. “Measuring the Efficiency of Decision Making Units.” European Journal of Operational Research 2(6): 429-444.
Chhikara, K. S., and Rani, S. 2012. “Data Envelopment Analysis: A Study Measuring Efficiency of Public Sector Banks in India by using Data Envelopment Analysis: A Study.” Journal of Institute of Public Enterprise 35(3/4): 2-15.
Devi, P. A., and Rajagopalan S. P. 2012. “Analysis of Customer Behavior using Clustering and Association Rule.” International Journal of Computer Applications (IJCA) 43(23): 19-26.
Estrada, S. A., Song, H. S., Kim, Y. A., Namn, S. H., and Kang, S. C. 2009. “A Method of Stepwise Benchmarking for Inefficient DMUs based on the Proximity-based Target Selection.” Expert Systems with Applications 36(9): 11595-11604.
Ferreira, L. N., and Zhao, L. 2015. “A Time Series Clustering Technique based on Community Detection in Networks.” Procedia Computer Science 53: 183-190.
Fixler, T., Paradi, J. C., and Yang, X. 2014. “A Data Envelopment Analysis Approach for Measuring the Efficiency of Canadian Acute Care Hospitals.” Health Services Management Research 27(3-4): 57-69.
Francisco, K. 2016. “PNP: Quezon City has the Highest Number of Index Crimes.” Rappler. Retrieved 19 June 2016. (https://goo.gl/lCSSNr).
Groff, E. R., and La Vigne, N. G. 2002. “Forecasting the Future of Predictive Crime Mapping.” Crime Prevention Studies 13: 29-58.
Herrera, F., Sosa, R., and Delgado, T. 2015. “GeoBI and Big VGI for Crime Analysis and Report.” Presented at the 3rd International Conference on Future Internet of Things and Cloud (FiCloud), (pp. 481-488), August 24-26 Rome, Italy.
Hu, J. L., Tung, Y. Y., Shieh, H. S., and Lo, K. 2011. “A Meta-Frontier Efficiency Analysis of Regional Police Agencies in Taiwan.” Journal of Management Research 11(3): 168-182.
Izhar, T. A. A., Baharuddin, M. F., Mohamad, A. N., . . .Wan Hasnol, W. M. H. (2016). “Using ontology for goal-based query to evaluate social media data.” Journal of Advances in Humanities and Social Sciences 2(2): 108-118.
Kaur, E., and Krishan, K. 2013. “Cluster Analysis of Behavior of E-learners.” International Journal of Soft Computing and Engineering (IJSCE) 3(2): 344-346.
Kazan, H., and Kitapci, H. 2013. “Performance Measurement with Data Envelopment Analysis in Service Industry: Banking Application.” Business Management Dynamics 3(5): 37-50.
Kinaci, H., Najjari, V., and Alp, I. 2016. “Using Data Envelopment Analysis and Stochastic Frontier Analysis Methods to Evaluate Efficiency of Hydroelectricity Centers.” Gazi University Journal of Science 29(1): 167-176.
Kruchten, P. (2004). The Rational Unified Process: An Introduction. Boston, MA: Addison-Wesley Professional.
Liu, C. Y. A., and Liu, W. H. 2010. “Performance Evaluation on Private Higher Education using Data Envelopment analysis.” Presented at the Industrial Engineering Research Conference, December 07-10 Macao, China.
Maimon, O., and Rokach, L. 2005. Data Mining and Knowledge Discovery Handbook. New York, NY: Springer.
Mitrovic, Z., Vujosevic, M., and Savic, G. 2017. “Data Envelopment Analysis for Evaluating Serbia’s Health Care System.” Management: Journal of Sustainable Business and Management Solutions in Emerging Economies 20(75): 39-46.
Narimatsu, H., Nakata, Y., Nakamura, S., Sato, H., Sho, R., Otani, K., ... and Yamashita, H. 2015. “Applying Data Envelopment Analysis to Preventive Medicine: A Novel Method for Constructing a Personalized Risk Model of Obesity.” PloS One 10(5): 1-11.
Ni, Y., Engstrom, C., Malyarenko, A., and Wallin, F. 2014. “Investigating the Added Values of High Frequency Energy Consumption Data using Data Mining Techniques.” Presented at the AIP Conference Proceedings (pp. 734-743), July 15-18, Narvik, Norway. Norman, M., and Stoker, B. 1991. Data Envelopment Analysis: The Assessment of Performance. New York, NY: John Wiley & Sons, Inc.
Okamoto, S. 2015. “Analyzing Instability of Industrial Clustering Techniques.” Environmental Economics and Studies 17(3): 389-406.
Peckham, T., and McCalla, G. 2012. “Mining Student Behavior Patterns in Reading Comprehension Tasks.” Presented at the proceedings of the 5th International Conference on Educational Data Mining. June 19, 2012 - June 21, 2012 Panorama Hotel, Chania, Greece.
Reckless, W. C. 1967. The Crime Problem. New York, NY: Appleton-Century-Crofts.
Reich, B. J., and Porter, M. D. 2015. “Partially Supervised Spatiotemporal Clustering for Burglary Crime Series Identification.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 178(2): 465-480.Renushe, H. N., Joshi, M. J., and Rasal, P. R. (2010). “Importance of Data Mining Time Series Technique in Crime and Criminal Investigation: A Case Study of Pune Rural Police Stations.” International Journal of Computer Applications 30(9): 38-42.
Tayebi, M. A., Ester, M., Glasser, U., and Brantingham, P. L. 2014. “Crimetracer: Activity Space Based Crime Location Prediction.” Presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), (pp. 472-480), August 17-20 Beijing, China.
Tran, T. T. H., and Bhaiyat, F. 2016. “Evaluating the Efficiency of Vietnamese Commercial Banks using Data Envelopment Analysis.” Presented at the 6th Annual International Conference on Accounting and Finance, May 30-31 Singapore.
Tsai, B. H., and Chou, S. B. 2015. “Application of Multiple Output Data Envelopment Analysis in Interpreting Efficiency Improvement of Enterprise Resource Planning in Integrated Circuit Firms.” The Journal of Developing Areas 49(1): 285-304.
Vyver, A. G. D. V., and Gillies, D. 2017. “Analysing public perceptions of international events by using geo-located twitter data.” International Journal of Humanities, Arts and Social Sciences 3(2): 64-70.
Wu, Y., Duan, H., and Du, S. 2015. “Multiple Fuzzy C-means Clustering Algorithm in Medical Diagnosis.” Technology and Health Care 23(s2): S519-S527.
Yamuna, S., and Bhuvaneswari, N. S. 2012. “Data Mining Techniques to Analyze and Predict Crimes.” The International Journal of Engineering and Science 1(2): 243-247.
Yang, C. C. 2014. “Evaluating the Performance of Banking Under Risk Regulations: A SlacksâA˘ Rbased Data Envelopment Analysis Assessment Framework.” Expert Systems 31(2): 176-184.
Yu, C. H., Ward, M. W., Morabito, M., and Ding, W. 2011. “Crime Forecasting using Data Mining Techniques.” Presented at the IEEE 11th International Conference on Data Mining Workshops (ICDMW), (pp. 779-786), December 11 Vancouver, Canada.
Published
2017-06-30
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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