Analysis of crime data is crucial for prevention and assessment of illegal activity. This paper is one of the first case studies of a crime dataset collected in New South Wales, Australia. We apply methods from complex network analysis to identify key aspects of criminal activity in the state of New South Wales. We further detect groups of local government areas and examine their dynamics over time. We represent our results by using various different visualisation techniques. The analysis of crime datasets is necessary in order to prevent and assess criminal activity [17]. Information about different types of crimes can often be found in the form of annual reports published by government bodies, but rarely in the form of publicly available datasets that may be used for research. In contrast, the New South Wales Bureau of Crime Statistics and Research in Australia has published data on criminal activity in the state of New South Wales (NSW) [4]. It contains information collected between 1995 and 2012, recording several types of offences and the local government area where they occurred.
INTRODUCTION The dataset analysed in this paper is publicly available1 and contains information about the different types of crime that took place in New South Wales between 1995 and 2012. It records the local government area where the crime occurred along with its offence category and the month and year of the crime. The New South Wales Bureau of Crime Statistics also provides a helpful visualisation tool for the dataset on their website2. It allows the user to research various basic statistics of the local government areas and offence categories.
As outlined in the introduction we use tools from complex network analysis as a means to analyse this data and represent the given information as a network. In the case of the NSW crime data, there are 155 local government areas and 49 offence categories, that can be represented by two different types of nodes. A government area can never be linked directly to another government area. Similarly, a connection cannot be established between two offence categories and hence, links are solely found between areas and offences. For example, the scenario of a person stealing from a retail store in Bourke and two people escaping custody in the local government areas, Wagga Wagga and Upper Hunter Shire, may be represented as the network depicted in Figure 1.
stealing from a retail store
escaping custody
Upper Hunter Shire
Bourke
Wagga Wagga
We are particularly interested in changes in the data between 2000 and 2012 and hence have divided the dataset into 156 networks, each covering a period of one month. Analysing each network separately and comparing the results gives valuable insights into the dynamics of criminal activity with respect to the local government areas.
IDENTIFYING CENTRAL ASPECTS TO CRIME ACTIVITY
The identification of central crime locations and offences is highly beneficial in preventing future criminal
activity [
A total of 155 local government areas form the Australian state of New South Wales. Their ranks range between 0 and 1 and are inversely proportional to the concentration. Thus, a rank close to zero shows that the concentration of a particular area was higher than the mean concentration. A rank close to 0.5 shows a similar concentration to the average, and a high rank (close to one) represents a concentration much lower than the average. Examination of our results shows that the rank of any individual area never fell below 0.3, meaning that the concentrations are skewed with many areas exhibiting concentrations below the average. We found that isolated and sparsely populated areas received extremely high ranks and did not show much variation over time. We have plotted the ranks of four government areas over time in Figure 3. Being able to make a clear connection between the rate of certain crimes in particular areas and their rank requires further work.
Leichhardt
Kogarah 20 40 60 80 100 120 140
time Unincorporated Far West 20 40 60 80 100 120 140
time Lord Howe Island
Similar to the government areas, the ranks of the 49 offence categories range between 0 and 1. We observe that more common, often less serious, crimes are ranked low (close to 0) while, offences that are less common, but more serious, are given a high rank (close to 1).
The ranking of offence categories changes from month to month, however, the observed difference in the ranking of each category is generally small. Two of the lowest ranked categories in the NSW crime dataset in the years between 2000 and 2012 are possession and use of cannabis and sexual offences. Some offences that fall under disorderly conduct and certain offences against justice procedures were also ranked low throughout the 13 year period. Figure 4 shows the change in ranking of these offences together with other similar offences that fall within the same super-category.
Other offences against justice procedures
Looking at the first plot in Figure 4, we can see that the rank of the offence category use or possession
of cannabis is much lower than that that of other listed drugs. Although Australia has seen a significant
decline in the use of drugs after the tightening of drug strategies in 1998, cannabis is still one of the most
common and frequently used drugs [
Both sexual offence categories recorded in the dataset received very low rankings throughout the 13
year period. Sexual offences are a huge issue everywhere in Australia with New South Wales having the
highest total number of sexual assaults reported to police [
Disorderly conduct is another common offence in NSW, specifically on weekends and in connection
with alcohol consumption [
On the other hand, homicide and the dealing of cocaine are two of the highest ranked categories (see
Figure 5). According to the Australian Institute of Criminology [
Clearly, the rank of offences reflects the severity of the crime and not the rate at which it occurs. All data indicates that more petty crimes such as trespassing occur more often than serious crimes such as murder.
DETECTION OF GROUPS
The detection of groups, of entities within a system, has been another field of great interest in the area of
complex networks in recent years [
1 kn 0.6 a r
1 0.8 0.4 0.2 0
Dealing or trafficking cocaine Dealing or trafficking narcotics
Dealing or trafficking cannabis Dealing or trafficking amphetamines
Dealing or trafficking ecstasy Dealing or trafficking other drugs location is not guaranteed to be successful in another. If two local government areas are classified to be in the same group they certainly have many things in common. Therefore, a prevention strategy that works in one area is more likely to work in another area that is part of the same group.
Determining groups of local government areas and offence categories respectively, requires a
simplification of the network. Without loss of generality we describe the process in terms of the local government
areas. The network is simplified in the following manner: The nodes representing the 49 different offence
categories are dropped and two areas are linked if one or more crimes of the same category occurred in
both areas. For instance, if an attempted murder occurred in the two areas Bourke and Wagga Wagga,
a connection is established between these regions. Connections are associated with an attribute that
records the number of crimes in common. Once the network is simplified, it is possible to determine the
most significant connections. Dropping all insignificant connections reveals the different groups as such
connections often occur between groups, whereas significant links usually occur within groups. Details
on how to determine the significance of a connection can be found in [
We are interested not solely in finding different groups but also in their development over time and hence have explored the dynamics in group structure of the local government areas.
We identified groups of local government areas in all 156 networks and always found two main groups as well as some smaller groups. The two largest groups usually contained government areas in the north east and south west respectively. We often found groups that only consisted of a single government area. Interestingly, such areas always received one of the highest ranks during the corresponding month.
Figure 6 shows a map of NSW and its local government areas. Areas are coloured according to group membership. The largest group is coloured in blue, the second largest in green, with the size of a group determined by the number of its members and not the total area covered. The colour grey represents missing data for that month. Examination of data from October 2000 (third map in Figure 6) reveals that the areas in the largest group, coloured in green, experienced higher crime rates of trespassing than the average for NSW during that month. Trespassing happened to be the lowest ranked crime during that month. To answer the question whether this pattern continues throughout the dataset requires more research and is left for future work.
CONCLUSION This paper has shown how tools from complex network analysis can be applied to crime data in order to describe its dynamics. We have ranked the different offence categories and local government areas in the state of New South Wales in order to gain an understanding of the underlying mechanics of criminal activity. Different visualisation techniques were used to present the results. Being able to draw clear conclusions and find causations of the results presented in this paper requires further research. Proc. of the 3rd Annual Conference of Research@Locate 35