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Review Article

A Review on a Classification Framework for Supporting Decision Making in Crime Prevention

Noor Maizura Mohamad Noor, Siti Haslini Ab Hamid, Rosmayati Mohemad, Masita @ Masila Abdul Jalil and Muhammad Suzuri Hitam
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Increasing volume of crimes has brought a serious problem to many countries across the world. Crime prevention is an important component of an overall strategy to reduce crime and to strengthen public safety. Although, Supporting Decision Making (SDM) in crime prevention is an important topic but a comprehensive literature review on the subject has yet to be implemented. Thus, this study presents a systematic and comprehensive review on a classification framework for SDM in crime prevention. Forty four journal articles on the subject published between 2000 and October, 2015 were analyzed and classified into two categories of index crime (violent crime and property crime) and six classes of data mining techniques (prediction, classification, visualization, regression, clustering and outlier detection). The results of this study clearly show that data mining especially prediction and clustering techniques have been applied most extensively in both index crime categories. The main data mining techniques used for SDM in crime prevention are Bayesian, neural network and nearest neighbor. This study also addresses the gaps between SDM in crime prevention and the needs of practitioners to encourage more researches in crime analysis. Finally, it concludes with some suggestions for future research on SDM in crime prevention.

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Noor Maizura Mohamad Noor, Siti Haslini Ab Hamid, Rosmayati Mohemad, Masita @ Masila Abdul Jalil and Muhammad Suzuri Hitam, 2015. A Review on a Classification Framework for Supporting Decision Making in Crime Prevention. Journal of Artificial Intelligence, 8: 17-34.

DOI: 10.3923/jai.2015.17.34

Received: September 01, 2015; Accepted: November 12, 2015; Published: November 20, 2015


Recently, increasing volume of crimes has brought a serious problem to many countries across the world. For an example, Malaysia is one of the fastest growing countries in Southeast Asia and one of the challenges faced is increasing of crime rate (Marzbali et al., 2012). According to the Royal Malaysia Police (RMP), incidences of crime can be categorized into two types which are index crimes and non-index crimes. Index crime is crimes that are normally reported and have sufficient significance to be considered as important as an indication towards the level of crime. On the other hand, non-index crime involves cases of crime that are not considered to be a measurement of crime stream (Abdullah et al., 2012). To handle the issue of increasing volume of crimes, the government introduced one of the Malaysian government agendas as stated in the National Key Result Area (NKRA) which is to reduce crime (Soh, 2012). The NKRA reduce crime includes the whole procedure of fighting crime, known as crime prevention, crime tracking, arrest, trial of offenders, prison and also rehabilitation. Moreover, it targets to increase community awareness and to improve service delivery by the RMP. This study focuses on crime prevention which is an important component of an overall strategy to reduce crime and to strengthen public safety. Crime prevention refers to all actions and efforts that aim to reduce crime and fear of crime (Marzbali et al., 2012). Crime prevention can be defined as an effort to prevent crime or criminal offending in the first instance before the act has been committed (Welsh and Farrington, 2012). Government and public officials conduct a comprehensive effort to enhance the effectiveness of crime prevention (Li et al., 2010).

Now-a-days, Supporting Decision Making (SDM) in crime prevention has attracted a great concern and attention. Decision making is defined as the study of identifying and choosing alternatives based on values and preferences of decision maker (Harris, 2012). Decision making is very important in crime prevention in order to decide accurate actions and law enforcement strategies. The police needs valuable information such as crime statistics in order to increase the accuracy of selection decisions and to avoid biased analysis. Over the last two decades, researchers have developed several techniques to support law enforcement activities in order to prevent criminal act (Grubesic, 2006; Noor et al., 2011) and discover patterns of crime (Kaikhah and Doddameti, 2006; Li et al., 2010; Phillips and Lee, 2012). These works come from disciplines of behavior and psychology, statistics and artificial intelligence (Li et al., 2010). However, the main challenge faced by all law-enforcement organizations and intelligence-gathering is to analyze the growing volumes of crime data accurately and efficiently. As a solution, crime analysis can make an important contribution to the delivery of an effective and efficient police services and to how the police and their partners tackle crime (Chen et al., 2004).

Crime analysis can be defined as a profession and process in which a set of quantitative and qualitative techniques are used to analyze data valuable to police agencies and their communities (Bruce et al., 2014). Crime analysis includes the analysis of crime, criminals, crime victims, disorder, quality of life issues, traffic issues, internal police operations and its result support crime prevention and reduction strategies, criminal investigation and prosecution, patrol activities, problem solving and the evaluation of police efforts. There are four major categories of crime analysis recognized by International Association of Crime Analysts (IACA) which are crime intelligence analysis, tactical crime analysis, strategic crime analysis and administrative crime analysis (Bruce et al., 2014). Each type of crime analysis has a number of processes and sub-categories (Bruce et al., 2014).

Lately, the data mining approach has been exposed to be a proactive decision-support concept in preventing and predicting crime. Based on existing knowledge, criminology is one of the most important fields to apply data mining because it deals with high volume of crime data and complexity of relationship between data (Keyvanpour et al., 2011). Data mining is a great tool that allows criminal investigators who may lack extensive training as data analysts to discover huge databases efficiently and quickly (Chen et al., 2004). Moreover, crime analysis can produce a superior result by integrating data mining technique into Decision Support System (DSS). The DSS is required in crime analysis because it has the capability to improve the quality of decision making for crime prevention. The DSS can be defined as an integrated, interactive computer system, consists of analytical tools and information management capabilities and is designed to assist decision makers in solving quite large and unstructured problems (Sieker et al., 2006). The DSS makes full use of suitable computer techniques and through the interactive human-machine model, it helps to improves the effectiveness of decision-making about semi-structures and non-structures (Zhou et al., 2008).

Fig. 1: Conceptual framework of DSS for SDM in crime prevention

In DSS, there are three fundamental components which are Model Base Management System (MBMS), Database Management System (DBMS) and Dialog Generation and Management System (DGMS) as depicted in Fig. 1. DBMS serves as a data bank for the DSS while, MBMS transforms data from DBMS into information that is useful in decision making by using Artificial Intelligence (AI) and fuzzy logic. The DGMS or user interface is used to improve the ability of the system user to utilize and benefit from DSS. For instance, monthly crime volume for each crime type that covered for one year was recorded by the police. By using this data, DBMS can store, update, delete and generate report. To transform raw crime data into information, MBMS will communicate with DBMS. All information and reports generated will be displayed using DGMS.

Currently, a number of review articles on SDM in crime prevention have also appeared in journal publication. Chen et al. (2004), for example have presented a general framework for crime data mining based on their experiences gained through Coplink project. Krishnamurthy and Kumar (2012) have surveyed data mining technique on crime data analysis that focused only on data preprocessing and clustering method. Fredrick and Patrick (2012) presented a survey of data mining methods for crime analysis and visualization. They concentrated on crime spatial analysis and visualization. Nissan (2012) surveyed on forensic applications of data mining to fraud and crime intelligence or investigation within law enforcement. Roth et al. (2013) presented a survey on current practices and unmet needs for spatiotemporal crime analysis in U.S. law enforcement agencies. Based on the previous review articles, the researchers only provide limited review on data mining technique. Nevertheless, this study presents the up-to-date and comprehensive review of data mining application to support decision making in crime prevention.

This study has three objectives: (1) Develop a framework for classifying the application of data mining for SDM in crime prevention, (2) Provide a systematic review of existing research articles on the application of data mining in crime prevention and (3) Use the review and framework for generating roadmap for researchers looking for better understand this field. The rest of the study is structured as follows: Section 2 presents the methodological framework for research. Section 3 proposes classification framework on application of data mining for SDM in crime prevention. Section 4 analyzes SDM in crime prevention research according to this classification framework. Lastly, section 5 provides conclusion and future research directions.


The methodological research framework for this study is modified from other review study (Ngai et al., 2011). This methodological research framework consists of three phases: Research definition, research methodology and research analysis as displayed in Fig. 2.

In phase 1, this study identifies the area of research, the expected research goal and the scope of research. The area of research is academic research on SDM in crime prevention using data mining techniques. The expected research goal is to create a classification framework on application of data mining for SDM in crime prevention and to suggest future research directions. The research scopes are the literature on application of data mining for SDM in crime prevention, applied index crime dataset and published for 16 year period between 2000 and 2015 which is summarized to provide knowledge for researchers in this area.

Fig. 2: Methodological framework for research

The period is important because the internet was opened to general users in 2000 and the widespread availability of information and communication technologies have played an important role in the development of methods for collecting data from online databases (Liao et al., 2012).

In phase 2, this study identifies the suitable criteria to search and select articles. This study also constructs a framework for classifying the selected articles. To provide a comprehensive listing of journal articles, this study searches online academic databases such as Science Direct, Springer-Link journals, Academic Search Premier, ProQuest and World Scientific.Net. These databases contain most academic journals in full text English version. The searching process is based on specific keywords like "Crime data mining", "Crime prevention" and "Crime analysis".

The review and classification process was done carefully and only articles related to SDM in crime prevention that applied data mining techniques and index crime dataset were selected. Firstly, the articles must have been published in academic journals with availability of full text versions. Secondly, the articles had to have been published within the year 2000 until October, 2015. Thirdly, the articles had to use index crimes dataset, apply data mining techniques and discuss their application for SDM in crime prevention. Forty four articles were chosen for classification.

In the last phase, this study analyzes the selected articles to make some conclusions and propose some future research directions. Section 4 presents the details result of the analysis based on the proposed classification framework.


In this section, this study proposes a graphical conceptual classification framework for the available literature on applications of data mining techniques for SDM in crime prevention. The classification framework, which is depicted in Fig. 3 is based on a literature knowledge on the nature of data mining research (Chen et al., 2004; Ngai et al., 2011) and index crime based on Uniform Crime Reporting (UCR) program (FBI., 2004). The proposed classification framework for index crime is based on the part 1 offenses in the UCR program published by the U.S. Federal Bureau of Investigation (FBI), because it is one of the best established frameworks for crime. Figure 3 consists of two layers. The first layer (layer A) contains two index crime categories which are violent and property crimes.

Fig. 3: Conceptual framework for classifying data mining for SDM in crime prevention

The second layer (layer B) contains six data mining classes which include prediction, classification, visualization, regression, clustering and outlier detection. This study provides a brief explanation of the proposed classification framework with references in the following sections.

Classification for index crime: Index crime is the crimes that considered quite serious, tend to be reported more reliably than others and are reported directly to the police. According to the RMP, index crime refers to types of crime that occur with sufficient regularity and significance that they collectively serve as a meaningful index to the overall crime situation (PMDU., 2010). In this study, index crime is classified into two main categories. Table 1 shows index crimes category and list of crime types for each category. The first category is property crime in which they commit offenses involving loss of property and no use of violence by the perpetrator (Soh, 2012). In this study, property crime includes burglary, theft, vehicle theft and arson where, burglary is the unlawful entry of a structure to commit a felony or a theft and theft is defined as the unlawful taking, carrying, leading or riding away of property from the possession or constructive possession of another (FBI., 2004). According to UCR program, vehicle theft is the theft or attempted theft of a motor vehicle where, motor vehicle is self-propelled and runs on land surface and not on rails. On the other hand, arson is defined as any willful or malicious burning or attempt to burn with or without intent to defraud, a dwelling house, public building, motor vehicle or aircraft and personal property of another, etc. (FBI., 2004).

The second category is violent crime which generally includes crimes of violence that are pretty common and significant in occurrences (Soh, 2012). In this study, violent crime includes murder, rape, robbery and aggravated assault where, murder is the willful (non-negligent) killing of one human being by another (FBI., 2004). West's Encyclopedia of American Law similarly define murder as unlawful killing of another person without justification or valid excuse. Rape is defined as penetration, no matter how slight of the vagina or anus with anybody part or object or oral penetration by a sex organ of another person without the consent of the victim (FBI., 2014). According to UCR program, robbery is defined as the taking or attempted taking of anything of value from the care, custody or control of a person or persons by force or treat of force or violence and/or by putting the victim in fear and aggravated assault is defined as an unlawful attack by one person upon another for the purpose of inflicting severe or aggravated bodily injury (FBI., 2004).

Classification for data mining applications and techniques: There are six data mining application classes which include prediction, classification, visualization, regression, clustering and outlier detection (Ngai et al., 2011). Prediction is based on the patterns of a crime data set to estimate numeric and ordered future values (Ngai et al., 2011). Among the most commonly used prediction techniques are neural networks and Bayesian method. Classification discovers common properties among different crime entities and organizes them into predefined classes (Chen et al., 2004).

Table 1: Classification for index crimes based on UCR (FBI., 2004)

This technique frequently used to predict crime trends. Some of the famous classification techniques are Naive Bayes classifier, decision tree and fuzzy logic techniques. Visualization refers to an easily understandable presentation of data and to methodology that converts complicated data characteristics into clear patterns to allow users to view the complex patterns or relationships uncovered in the data mining process (Chen et al., 2004). From the literature, visualization techniques have demonstrated an enormous capacity to analyze crime data.

Regression is a statistical process to estimate the relationships between variables (Ngai et al., 2011). The regression technique normally applies mathematical method such as Ordinary Least Square (OLS). Clustering known as data segmentation or partitioning is the task to discover groups and structures in the data that are in some way or another "Similar", without using known structures in the data. Some of the common clustering techniques are the K-mean and self-organizing map (Li et al., 2010; Li and Juhola, 2014a). Outlier detection uses specific measurement to study data that differs markedly from the rest of the data (Chen et al., 2004).


This study provides a review of classification framework for SDM in crime prevention. For the classification of index crime, this study sorted the articles accordingly to the categories of violent crime and property crime. Some of the selected articles apply both data on property crime and violent crime and thus this study categorized those articles as "Both category". For the data mining classification, this study identified six data mining application classes together with their techniques.

The distribution of the 44 articles classified into the proposed classification framework is depicted in Table 2. The table contains information on article number, crime category, crime type and data mining class and technique. Although, some researches using data on index crime together with non-index, this study captures only crime types that fall under index crime. Based on Table 2, it is worth noting that murder is the highest concern in violent crime while burglary is the main concern in property crime. The popular data mining application classes are prediction (34.1% or 15 of the 44 articles) and clustering (22.7% or 10 of the 44 articles). Regression is applied in four articles (9.1%), followed by classification (6.8% or 3 of the 44 articles) and visualization (4.5% or 2 of the 44 articles). Some of the articles apply combination of data mining application classes such as clustering and classification (9.1%, 4 of the 44 articles) and clustering and visualization (6.8% or 3 of the 44 articles). Outlier detection is only applied in one article (Malathi and Baboo, 2011) combined with clustering and classification techniques. The limited use of data mining technique of outlier detection may be due to the difficulty of detecting outliers (Ngai et al., 2011). Similarly, visualization also has shown a limited used and normally applied with clustering technique. Chen et al. (2004) pointed out that visualization has the highest analysis capability in crime data mining. Moreover, the main data mining techniques used in the articles are Bayesian, neural network (including self-organizing map) and nearest neighbor. Besides, decision tree, k-mean and Ordinary Least Squares (OLS) appear in more than one article.

A complete list of the 44 selected articles is presented in Table 3-5. Based on Table 3-5, it can be seen clearly that strategic crime analysis is the famous type of crime analysis among researchers (47.7%, 21 of the 44 articles) followed by tactical crime analysis (38.6% or 17 of the 44 articles), criminal intelligence analysis (6.8%, 3 of the 44 articles) and administrative crime analysis (6.8% or 3 of the 44 articles).

Table 2: Research on data mining techniques for SDM in crime prevention

Table 3: Property crime
DDS: Decision support system

Table 4: Violent crime

Table 5: Both index crime categories
DDS: Decision support system

Boba (2001) highlights the primary purposes of strategic crime analysis are to assist in the identification and analysis of long-term problems and to conduct studies to investigate or evaluate relevant responses and procedures. In addition, only 7 out of 44 articles applied DSS in their researches. Therefore, there is still a room for future research on the application of data mining technique combined with a DSS to support decision making in crime prevention.

The following subsections present further analysis of data mining techniques for SDM in crime prevention.

Distribution of articles by year: Table 6 presents the distribution of articles by index crime and publication year. Based on this Table 6, research in both category of index crime is the most prominent and only a few studies that focus on a specific category of index crimes. As depicted in Table 6, this study identified only 13 articles for violent crime (29.5%) and 10 articles for property crime (22.7%). This study believes that researchers prefer to focus on both categories (47.7%, 21 of the 44 articles) instead of a specific category of index crime and this will continue to be the case. This situation shows that both categories of index crime are more or less equally important. Moreover, according to Table 6, it shows that none of the published articles within selection criteria in year 2001, 2002, 2005 and 2007-2009. The number of published articles started to increase since year 2011 until 2014 and this study is confident that this situation will continue in year 2015 and in the future.

Distribution of articles by journal: Table 7 shows the distribution of the 44 articles by the journal in which they appeared. The articles related to the use of data mining techniques for SDM in crime prevention are distributed across 32 journals that cover a wide range of fields. This result shows that the application of such techniques for SDM in crime prevention has attracted great attention from scholars in different disciplines. Journals that contain the most relevant articles are (1) Computers, Environment and Urban Systems, (2) Expert Systems with Applications, (3) International Journal of Forecasting and (4) Science and Justice where, each of the journals contains 3 of the 44 articles (6.8%).

Table 6: Classification of articles by the categories of index crime and publication year

Table 7: Distribution of 44 articles by journal title

Next, the journals that contain the second relevant article are (1) Applied Geography, (2) Applied Mechanics and Materials, (3) Decision Support Systems and (4) Journal of Knowledge, Culture and Communication where, each of the journals contains 2 of the 44 articles (4.5%).


Crime is one of the biggest problems in cities across the world and crime prevention is one of the solutions to reduce crime. Now-a-days, SDM for crime prevention has attracted a great concern and attention from researchers. One of the solutions to support decision making for crime prevention is by using data mining technique (Keyvanpour et al., 2011). Although the importance of data mining techniques for SDM in crime prevention has been recognized, a comprehensive classification framework or a systematic review of their application for SDM in crime prevention research studies is lacking. Therefore, this study conducts a review of academic articles and propose classification framework on data mining for SDM in crime prevention.

The results of this study lead to the following conclusions. Firstly, based on 2 categories on index crime, researches in both categories have attracted the greatest attention from researchers. Secondly, the famous data mining application classes applied by many researchers are prediction and clustering. In addition, the main data mining techniques used in the articles are Bayesian, neural network (including self-organizing map) and nearest neighbor while, decision tree, Bayesian, k-mean and OLS appear in more than one articles. Thirdly, most of the selected articles in SDM in crime prevention apply data mining technique for strategic crime analysis. Finally, only several of articles applied DSS in their researches and studies.

This study predicts that the number of articles on application of data mining for SDM in crime prevention will be increased in the future due to more attention from researchers and also governments. For an example, to handle the issue of increasing volume of crime, the Malaysia government has introduced one of the agendas as stated in NKRA which is to reduce crime (Soh, 2012).

This study has two main limitations. Firstly, this review considered only articles written in english. It is believed that articles regarding application of data mining technique for SDM in crime prevention have also been published in other languages. Future research could be extended to include articles published in other languages. Secondly, this review applied several key words to search well-known online databases for articles published from 2000 until October, 2015. This study also limited for index crimes. A future review could be extended in scope in order to produce a better result.


This work was supported by Fundamental Research Grant Scheme (FRGS) under Ministry of Education (MOE) with grant number 59289. A special thank is also accorded to Criminal Investigation Department of the Royal Malaysia Police for its support and cooperation in providing information.

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