Crime reporting using attribute based algorithm
Abstract : Data mining is an important technique used in many fields with the purpose of acquiring valuable information from big data. This study aims to reveal the relations between the attributes of independent criminal records. The NIBRS database, which includes criminal records in USA that are recorded in 2013, is used in this study. The association rules created by the Apriori algorithm have been used to extract the relationships between features of criminal records. The experimental results show that the association rules created by the Apriori algorithm are useful for criminal analysis. This study reveals the relationships between the attributes of different criminal records and allows the authorities to establish relationships between new and old incidents.
? Existing methods have used these databases to identify crime hotspots based on locations.
? On the other hand, the previous related work and their existing methods mainly identify crime hotspots based on the location of high crime density without considering either the crime type or the crime occurrence date and time.
? It was focusing on the existence of multi-scale complex relationships between both space and time.
? Other existing works explore relationships between the criminal activity and the socio-economics variables such as education, ethnicity, income level, and unemployment.
? Crimes are a common social problem affecting the quality of life and the economic growth of a society.
? Because criminal cases have become more complicated than they were before, using new and up-to-date approaches in the analysis of cases and detailed detection of similarities between crime records constitute an important problem for criminology.
? Our study aims to find spatial and temporal criminal hotspots using a set of real-world datasets of crimes. We will try to locate the most likely crime locations and their frequent occurrence time.
? We intend to provide an analysis study by combining our findings of a particular crimes dataset with its demographics information.
• The proposed study consists of five steps mainly as follows: data pre-processing, data encoding, creating transactions in the dataset, creating frequent itemsets which provide minimum support value and creating association rules which provide minimum confidence value out of the created itemsets.
• The purpose of this study is to predict the unknown characteristics of a specific case such as offender profile, crime weapon, victim profile and geographical zone by taking into consideration the known characteristics of past criminal cases.
• Our proposed approach aimed to focus on three main elements of crimes data, which are the type of crime, the occurrence time and the crime location.
? It can help in the distribution of police at most likely crime places for any given time, to grant an efficient usage of police resources.
? Advantages of technology and analyzing the components of crime with the support of computer sciences have become important in solving the crime and detecting the offenders.
? We plan to apply more classification models to increase crime prediction accuracy and to enhance the overall performance.
? On the other hand, police forces can use this solution to increase the level of crime prediction and prevention.
? While our selected crime features have an independent effect on each other, this classifier was an ideal choice.
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