Tag Archives: Crime Control.

Web Data Mining: Views of Criminal Activities (Published)

Web data mining discovers valuable information or knowledge from the web hyperlink structure, page content and usage data. Along with the swift popularity of the Internet, crime information on the web is becoming increasingly flourishing, and the majority of them are in the form of text. A major challenge facing all law-enforcement and intelligence-gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. Detecting, exploring crimes and investigating their relationship with criminals are a big challenge to the present world. The evaluation of the different dimensions of widespread criminal web data causes one of the research challenges to the researchers. Criminal web data always offer convenient and applicable information for law administration and intelligence department. The goal of crime data mining is to understand patterns in criminal behavior in order to predict crime anticipate criminal activity and stop it. This paper describes web data mining which includes structure mining, web content mining, web usage mining and crime data mining. The occurrences of criminal activities based on web data mining process is also presented in this paper. The presented information on different criminal activities can be used to reduce further occurrences of similar incidence and to stop the crime.

Keywords: Classification, Clustering., Crime Control., Crime data, Pattern Analysis, Web Mining

How the availability of private security services assist in crime control in Nairobi County, Kenya (Published)

The study was motivated by the fact that despite the presence of private security companies and the availability of their services in Nairobi city, there was still an upsurge of insecurity. This study was informed by Situational crime prevention and Crime prevention through environmental design theories. Descriptive survey research design was used, and the study utilized stratified random sampling technique to select respondents from the study population which was 500 respondents. Data were obtained using a combination of a questionnaire and interview schedule. Copies of the questionnaire were administered to 151 respondents, who were drawn from the private security companies in the area of study, and the members of staff working in the private institutions and also the area residents. The study was carried out in Karen location which is an upper market estate in Nairobi County. The study identified five categories of crime control services provided by private security companies, with providing alarm response and loss prevention being the major crime control service. The study further found that majority of the residents in the study area goes for the medium level security premium which offers alarm response and security guarding services. Findings revealed that a majority of respondents (52%) perceived the level of effectiveness by private security companies to be fairly moderate, 28% perceived them to be average and 20% of the respondents perceived them to be good. Overall, private security services, were perceived, to be relevant in crime control in Karen location.

Keywords: Crime, Crime Control., Security alarms, Surveillance, access control

Web Data Mining: Views of Criminal Activities (Published)

Web data mining discovers valuable information or knowledge from the web hyperlink structure, page content and usage data. Along with the swift popularity of the Internet, crime information on the web is becoming increasingly flourishing, and the majority of them are in the form of text. A major challenge facing all law-enforcement and intelligence-gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. Detecting, exploring crimes and investigating their relationship with criminals are a big challenge to the present world. The evaluation of the different dimensions of widespread criminal web data causes one of the research challenges to the researchers. Criminal web data always offer convenient and applicable information for law administration and intelligence department. The goal of crime data mining is to understand patterns in criminal behavior in order to predict crime anticipate criminal activity and stop it. This paper describes web data mining which includes structure mining, web content mining, web usage mining and crime data mining. The occurrences of criminal activities based on web data mining process is also presented in this paper. The presented information on different criminal activities can be used to reduce further occurrences of similar incidence and to stop the crime.

Keywords: Classification, Clustering., Crime Control., Crime data, Pattern Analysis, Web Mining