Criminal identification and forecast method

Abstract

The invention provides a criminal identification and forecast method. The method adopts a data pre-processing method in data mining; aiming at criminal information such as data, street address, criminal police zone, week, criminal type, criminal description and sentence processing, attribute reconstruction, feature extraction and feature selection are performed, the correlation between the criminal information is mined, a characteristic factor with maximum difference is generated, and the correlation between the characteristics factor and a criminal result, namely the criminal type is generated; and then a model integrating Gaussian Naive Bayes, a neural network, Logistic regression, regularized regression, K neighbor, random forest, a support vector machine and an XGBoost learning algorithm is built to obtain an element classifier based on a weighted voting classifier having highlight classification and favorable clustering effect, reconstructed data is analyzed, processed and identified, a criminal condition of a city in future is forecasted, an individual criminal map of the city is drawn, and the effects of promoting and regulating city public security and management are further achieved.

Description

technical field [0001] The invention relates to the field of data mining, and more specifically, to a crime identification and prediction method. Background technique [0002] In recent years, with the continuous deepening of the reform and opening up that has brought outstanding results and the continuous development of socialism with Chinese characteristics, China's economic development level has continued to improve, social civilization has continued to improve, and people's lives have continued to become better. A stable social security guarantee is needed. However, keeping up with the trend of the times are also high housing prices, shortage of housing in school districts, high medical costs, poor-quality food, counterfeit and shoddy goods, second-child problems, employment problems, environmental pollution problems, leftover men and women problems, pension problems, etc. All of these have led to a continuous increase in the crime rate and planted the seeds of crime by...

AI Technical Summary

Problems solved by technology

For criminal big data, on the one hand, only a single logistic regression, regular regression, nearest neighbor, random deep forest, support vector machine SVM, etc. are used for classification prediction, because the model can only learn numerical data, because the category labels need to be numerically encoded , which will increase computational complexity and weaken its effectiveness in dealing with textual crime data

On the other hand, only mining the crime street address and the crime police district, which are related to the location, is not enough to express the connection between the crime and the geographic address, which will reduce the effect of crime prediction and the guidance of the results.

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