Prediction tool for Crime Analysis

Regression is one of a powerful tool for predicting the future values based on previous data sets. The use of prediction in crime analysis can make crime free region.. The proposed technique can predict those regions which are sensitive to criminal activities like theft, murder, rape, Anti-social behaviour, Domestic violence etc. The scheme uses the data sets taken from the local police stations and can be used to predict the number of criminal activities in future. The data will then supplied with other factors like population, month and year to the regression model which will predict the future value for the criminal activities. This predicted number of activities will indicate that the region is high sensitive or low sensitive. If the predicted number will be high then the region is high sensitive and if low then the region is low sensitive

[1]  Kari Laasonen,et al.  Clustering and Prediction of Mobile User Routes from Cellular Data , 2005, PKDD.

[2]  Henry X. Liu,et al.  Short Term Traffic Forecasting Using the Local Linear Regression Model , 2002 .

[3]  F. Girosi,et al.  Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[4]  James S. Thorp,et al.  Decision trees for real-time transient stability prediction , 1994 .

[5]  Seung-Yeon Kim,et al.  Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method , 2005, Bioinform..

[6]  Kari Laasonen.  Route prediction from cellular data , 2005 .

[7]  J. Contreras,et al.  Forecasting next-day electricity prices by time series models , 2002 .

[8]  Kazuo Asakawa,et al.  Stock market prediction system with modular neural networks , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[9]  Lefteris Angelis,et al.  Categorical missing data imputation for software cost estimation by multinomial logistic regression , 2006, J. Syst. Softw..

[10]  J. Contreras,et al.  Forecasting Next-Day Electricity Prices by Time Series Models , 2002, IEEE Power Engineering Review.

[11]  Douglas C. Montgomery,et al.  PREDICTION USING REGRESSION MODELS WITH MULTICOLLINEAR PREDICTOR VARIABLES , 1993 .

[12]  Xiaobo Zhou,et al.  Gene Prediction Using Multinomial Probit Regression with Bayesian Gene Selection , 2004, EURASIP J. Adv. Signal Process..

[13]  Taejeong Kim,et al.  Regression-based prediction for blocking artifact reduction in JPEG-compressed images , 2005, IEEE Transactions on Image Processing.

[14]  L Strunin,et al.  Prediction of postoperative nausea and vomiting using a logistic regression model. , 1996, British journal of anaesthesia.