Abstract
A hot-spot mapping is an advanced crime detection technique which helps police personnel to identify high-crime areas and the best way to respond. However identification of crime location with less man power would be a more difficult process. In this work, Social crime data aware kernel density estimation based serial crime detection approach (SAKDESD) is implemented to group the serial and social crime data set in terms of more similarity. Social crime data set consists of various user comments about the crime happening in different locations which can provide the in-depth information about the serial crimes. The unstructured social crime data set is pre-processed to obtain meaningful structured format. This work also adapts the latent semantic approach for finding the similar topics present in the social crime data set which can lead to accurate prediction and efficient grouping of serial crimes. The experimental tests were conducted in matlab simulation environment which proves that the proposed approach SAKDESD provides a better result than the existing approach such as Modified graph cut clustering algorithm (MGCC).
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References
Colleen, M.C.: Data mining and predictive analytics in public safety and security. IEEE Comput. Soc. 8, 12–18 (2006)
Sivaranjani, S., Sivakumari, S.: A novel approach for serial crime detection with the consideration of class imbalance problem. Indian J. Sci. Technol. 8, 1–9 (2015)
Susmita, R., Sharmistha, B.: Application of fuzzy-rough oscillation on the field of data mining (special attention to the crime against women at Tripura). Procedia Comput. Sci. 45, 790–799 (2015). International Conference on Advanced Computing Technologies and Applications
Clare, S.A., Helen, M., Lucy, T., Philip, W., Christopher, G.: Neurodevelopmental and psychosocial risk factors in serial killers and mass murderers. Aggress. Violent Behav. 19, 288–301 (2014)
Duygu, S., Murat, T.: The perception analysis of cyber crimes in view of computer science students. Procedia – Soc. Behav. Sci. 182, 590–595 (2015)
Nabeela, K., Junaid, A., Muhammad, N., Khalid, Z.: The socio-economic determinants of crime in Pakistan: new evidence on an old debate. Arab Econ. Bus. J. 10, 73–81 (2015)
Omowunmi, I., Antoine, B., Sonia, B.: A revised frequent pattern model for crime situation recognition based on floor-ceil quartile function. Procedia Comput. Sci. 55, 251–260 (2015)
Tomoki, N., Keiji, Y.: Visualising crime clusters in a space-time cube: an exploratory data-analysis approach using space-time kernel density estimation and scan statistics. Trans. GIS 14, 223–239 (2010)
Wang, D., Ding, W., Lo, H., Stepinski, T., Salazar, J., Morabito, M.: Crime hotspot mapping using the crime related factors-a spatial data mining approach. Appl. Intell. J. 4, 772–781 (2013)
Devendra Kumar, T., Arti, J., Surbhi, A., Surbhi, A., Tushar, G., Nikhil, T.: Crime detection and criminal identification in India using data mining techniques. AI Soc. 30, 117–127 (2015)
Matthew, S.G.: Predicting crime using Twitter and kernel density estimation. Decis. Support Syst. 61, 115–125 (2014)
Christopher, R.H.: The dynamics of robbery and violence hot spots. Herrmann Crime Sci. 4, 33 (2015)
Sivaranjani, S., Sivakumari, S.: Mitigating serial hot spots on crime data using interpolation method and graph measures. Int. J. Comput. Appl. 126, 17–25 (2015)
Fitterer, J., Nelson, T.A., Nathoo, F.: Predictive crime mapping. Police Pract. Res. 16(2), 121–135 (2015)
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Sivaranjani, S., Aasha, M., Sivakumari, S. (2018). Hot Spot Identification Using Kernel Density Estimation for Serial Crime Detection. In: Zelinka, I., Senkerik, R., Panda, G., Lekshmi Kanthan, P. (eds) Soft Computing Systems. ICSCS 2018. Communications in Computer and Information Science, vol 837. Springer, Singapore. https://doi.org/10.1007/978-981-13-1936-5_28
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DOI: https://doi.org/10.1007/978-981-13-1936-5_28
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