There has been evidence of crime in the US since colonization. In this article, we analyze the crime statistics of San Francisco and its resolution of crime recorded from January to September of the year 2018. We defi...There has been evidence of crime in the US since colonization. In this article, we analyze the crime statistics of San Francisco and its resolution of crime recorded from January to September of the year 2018. We define resolution of crime as a target variable and study its relationship with other variables. We make several classification models to predict resolution of crime using several data mining techniques and suggest the best model for predicting resolution.展开更多
A good machine learning model would greatly contribute to an accurate crime prediction. Thus, researchers select advanced models more frequently than basic models. To find out whether advanced models have a prominent ...A good machine learning model would greatly contribute to an accurate crime prediction. Thus, researchers select advanced models more frequently than basic models. To find out whether advanced models have a prominent advantage, this study focuses shift from obtaining crime prediction to on comparing model performance between these two types of models on crime prediction. In this study, we aimed to predict burglary occurrence in Los Angeles City, and compared a basic model just using prior year burglary occurrence with advanced models including linear regressor and random forest regressor. In addition, American Community Survey data was used to provide neighborhood level socio-economic features. After finishing data preprocessing steps that regularize the dataset, recursive feature elimination was utilized to determine the final features and the parameters of the two advanced models. Finally, to find out the best fit model, three metrics were used to evaluate model performance: R squared, adjusted R squared and mean squared error. The results indicate that linear regressor is the most suitable model among three models applied in the study with a slightly smaller mean squared error than that of basic model, whereas random forest model performed worse than the basic model. With a much more complex learning steps, advanced models did not show prominent advantages, and further research to extend the current study were discussed.展开更多
The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation...The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation for the establishment of prevention and control systems to protect citizens’property.However,the deep-learning methods applied in the monitoring and early warning of new cyber-telecom crime platforms have some apparent drawbacks.For instance,the methods suffer from data-distribution differences and tremendous manual efforts spent on data labeling.Therefore,a monitoring and early warning method for new cyber-telecom crime platforms based on the BERT migration learning model is proposed.This method first identifies the text data and their tags,and then performs migration training based on a pre-training model.Finally,the method uses the fine-tuned model to predict and classify new cyber-telecom crimes.Experimental analysis on the crime data collected by public security organizations shows that higher classification accuracy can be achieved using the proposed method,compared with the deep-learning method.展开更多
With the method of dynamic programming, two spatial variables,the expected utility and the probability of success of each crime, are used to model the criminal's location choices in urban areas in this paper.The m...With the method of dynamic programming, two spatial variables,the expected utility and the probability of success of each crime, are used to model the criminal's location choices in urban areas in this paper.The modeling results show that a criminal optimizes his crime locations according to the expected utility and the success probability during his planned period A criminal usually commits his first offense in the district that has the highest probability of success but a lower expected utility, and commits his last crime in the district where the expected utility is the highest and success probability is lower.If a location has both an expected utility and a higher probability of success, the criminal might commit all his offenses in thes place. The model also suggests that crime prevention measures should be adopted in accordance with local conditions. 'Covering' measures, such as patrolling, should be taken in the poor residential districts or juvenile delinquency districts, while more sophisticated and advanced measures should be introduced in the richer districts or the districts where professional criminals haunt.展开更多
Poverty and crime are two maladies that plague metropolitan areas. The economic theory of crime[1]demonstrates a direct correlation between poverty and crime. The model considered in this study seeks to examine the dy...Poverty and crime are two maladies that plague metropolitan areas. The economic theory of crime[1]demonstrates a direct correlation between poverty and crime. The model considered in this study seeks to examine the dynamics of the poverty-crime system through stability analysis of a system of ordinary differential equations in order to identify cost-effective strategies to combat crime in metropolises.展开更多
This paper first examines crime situation in Benin metropolis using questionnaire to elicit information from the public and the police. Result shows that crime is on the rise and that the police are handicapped in man...This paper first examines crime situation in Benin metropolis using questionnaire to elicit information from the public and the police. Result shows that crime is on the rise and that the police are handicapped in managing it because of the obsolete methods and resources at their disposal. It also reveals that members of the public have no confidence in the police force as 80% do not report cases for fear of exposure to the informant to the criminal. In the light of these situations, the second part of the paper looks at the possibility of utilizing GIS for effective management of crime in Nigeria. This option was explored by showing the procedural method of creating 1) digital landuse map showing the crime locations, 2) crime geo-spatial database, and 3) spatial analysis such as query and buffering using ILWIS and ArcGIS software and GPS. The result of buffering analysis shows crime hotspots, areas deficient in security outfit, areas of overlap and areas requiring constant police patrol. The study proves that GIS can give a better synoptic perspective to crime study, analysis, mapping, proactive decision making and prevention of crime. It however suggests that migrating from traditional method of crime management to GIS demands capacity building in the area of personnel, laboratory and facilities backed up with policy statement.展开更多
文摘There has been evidence of crime in the US since colonization. In this article, we analyze the crime statistics of San Francisco and its resolution of crime recorded from January to September of the year 2018. We define resolution of crime as a target variable and study its relationship with other variables. We make several classification models to predict resolution of crime using several data mining techniques and suggest the best model for predicting resolution.
文摘A good machine learning model would greatly contribute to an accurate crime prediction. Thus, researchers select advanced models more frequently than basic models. To find out whether advanced models have a prominent advantage, this study focuses shift from obtaining crime prediction to on comparing model performance between these two types of models on crime prediction. In this study, we aimed to predict burglary occurrence in Los Angeles City, and compared a basic model just using prior year burglary occurrence with advanced models including linear regressor and random forest regressor. In addition, American Community Survey data was used to provide neighborhood level socio-economic features. After finishing data preprocessing steps that regularize the dataset, recursive feature elimination was utilized to determine the final features and the parameters of the two advanced models. Finally, to find out the best fit model, three metrics were used to evaluate model performance: R squared, adjusted R squared and mean squared error. The results indicate that linear regressor is the most suitable model among three models applied in the study with a slightly smaller mean squared error than that of basic model, whereas random forest model performed worse than the basic model. With a much more complex learning steps, advanced models did not show prominent advantages, and further research to extend the current study were discussed.
基金supported in part by the Basic Public Welfare Research Program of Zhejiang Province under Grant LGF20G030001.
文摘The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation for the establishment of prevention and control systems to protect citizens’property.However,the deep-learning methods applied in the monitoring and early warning of new cyber-telecom crime platforms have some apparent drawbacks.For instance,the methods suffer from data-distribution differences and tremendous manual efforts spent on data labeling.Therefore,a monitoring and early warning method for new cyber-telecom crime platforms based on the BERT migration learning model is proposed.This method first identifies the text data and their tags,and then performs migration training based on a pre-training model.Finally,the method uses the fine-tuned model to predict and classify new cyber-telecom crimes.Experimental analysis on the crime data collected by public security organizations shows that higher classification accuracy can be achieved using the proposed method,compared with the deep-learning method.
文摘With the method of dynamic programming, two spatial variables,the expected utility and the probability of success of each crime, are used to model the criminal's location choices in urban areas in this paper.The modeling results show that a criminal optimizes his crime locations according to the expected utility and the success probability during his planned period A criminal usually commits his first offense in the district that has the highest probability of success but a lower expected utility, and commits his last crime in the district where the expected utility is the highest and success probability is lower.If a location has both an expected utility and a higher probability of success, the criminal might commit all his offenses in thes place. The model also suggests that crime prevention measures should be adopted in accordance with local conditions. 'Covering' measures, such as patrolling, should be taken in the poor residential districts or juvenile delinquency districts, while more sophisticated and advanced measures should be introduced in the richer districts or the districts where professional criminals haunt.
文摘Poverty and crime are two maladies that plague metropolitan areas. The economic theory of crime[1]demonstrates a direct correlation between poverty and crime. The model considered in this study seeks to examine the dynamics of the poverty-crime system through stability analysis of a system of ordinary differential equations in order to identify cost-effective strategies to combat crime in metropolises.
文摘This paper first examines crime situation in Benin metropolis using questionnaire to elicit information from the public and the police. Result shows that crime is on the rise and that the police are handicapped in managing it because of the obsolete methods and resources at their disposal. It also reveals that members of the public have no confidence in the police force as 80% do not report cases for fear of exposure to the informant to the criminal. In the light of these situations, the second part of the paper looks at the possibility of utilizing GIS for effective management of crime in Nigeria. This option was explored by showing the procedural method of creating 1) digital landuse map showing the crime locations, 2) crime geo-spatial database, and 3) spatial analysis such as query and buffering using ILWIS and ArcGIS software and GPS. The result of buffering analysis shows crime hotspots, areas deficient in security outfit, areas of overlap and areas requiring constant police patrol. The study proves that GIS can give a better synoptic perspective to crime study, analysis, mapping, proactive decision making and prevention of crime. It however suggests that migrating from traditional method of crime management to GIS demands capacity building in the area of personnel, laboratory and facilities backed up with policy statement.
