Prof R Gupta
Civil Engineering Group, BITS-Pilani
[email protected]
<< The need for effective utilisation of IT in public safety management is increasing in today’s world. GIS can be used to identify the hotspots of crime as well as to facilitate development of investigation preference strategy for policing. The technology was effectively applied to identify the crime patterns in the district of Jhunjhunu of Rajasthan. We take a look >>
In India, Police is a critical component of civil administration. There has been an enormous increase in crime in the recent past. The cops, whose job is to catch criminals, are required to remain convincingly ahead in the eternal race between the law breakers and the law enforcers. One of the key concerns of the law enforcers is how to enhance investigative effectiveness of the police. There is an urgent need to analyse the increasing number of crimes as approximately 17 lakh Indian Penal Code (IPC) crimes and 38 lakh local and Special Law crimes are reported every year. The police should use the current technologies(1) (3) to give themselves the much-needed edge.
The traditional strategy of intelligence and criminal record maintenance has failed to meet the requirements of the present crime scenario. Manual processes neither provide accurate, reliable and comprehensive data round the clock nor does it help in trend prediction and decision support. The lack of public capability to guide and support the transition from ‘crime mapping in police’ to ‘mapping with the community’ for local policies is increasing at an alarming rate. Under these circumstances, the effective utilisation of information technology helps to change the paradigm of public safety management and is useful for community policing processes.
The use of GIS for crime mapping facilitates mapping, visualisation and analyses of crime hot spots along with other trends and patterns. It is a key component of crime analysis and the policing strategy. GIS uses geography and computer-generated maps as an interface for integrating and accessing massive amounts of location-based information. GIS allows police personnel to plan effectively for emergency response, determine mitigation priorities, analyse historical events, and predict future events.
It can also be used to provide critical information to emergency responders upon dispatch or en route an incident to assist in tactical planning and response. GIS helps crime officers determine potential crime sites by examining complex seemingly unrelated criteria and displaying them all in a graphical, layered, spatial interface or map.
The subsequent study highlights application of GIS for crime mapping in Jhunjhunu district of Rajasthan. The study utilises various crime records integrated with ancillary information to derive the hotspots of crime within an area. The research was carried out with the following objectives:
- To identify hot spots as well as driving forces for different types of crime.
- To help police to take preventive measures like deployment of forces in areas prone to crime.
- To develop a methodological framework for crime mapping using GIS.
Methodology
Geographic profiling is an investigative methodology that uses the locations of a connected series of crimes to determine the most probable area that an offender lives in2. Although it is generally applied in serial murder, rape, arson, robbery and bombing cases, geographic profiling can also be used in single crimes that involve multiple scenes or other significant geographic characteristics.
The methodology is based on a model that describes the behaviour of an offender. The criminal geographic targeting programme uses overlapping distance-decay functions centered on each crime location to produce jeopardy surfaces — three-dimensional probability surfaces that indicate the area where the offender probably lives. The distance-decay theory conveys the idea that people, including criminals, generally take more short trips and fewer long trips in the course of their daily lives, which may include criminal activities. Thus, overlapping distance-decay functions5 are sets of curves expressing this phenomenon and suggesting, for example, that it is more likely that offenders live close to the sites of their crimes than far away.
Probability surfaces can be displayed on both two- and three dimensional colour Isopleths maps, which then provide a focus for investigative efforts4. ArcGIS 9.0 software was used to achieve the objectives. Brief methodology applied in the present investigation is shown in Figure 1.
Figure 1.: Methodology of crime mapping using GIS
Results and discussions
The map showing the locations of police stations were created and the corresponding administrative areas were delineated for each station. The attribute details including crime records and socio-economic details were collected to create the geodatabase for crime analysis within Jhunjhunu district. The socio-economic as well as crime details were represented as spatial function in the geographic domain to visualise the pattern of crime within an area. The details of crime committed in the year 2006 were collected for crime analysis. Based on the distance-decay analysis, cost distance from each police station was computed. The whole area was categorised into different zones based on the analysis, and locations close to crime were identified.
To obtain a spatial function from point data of crime, Kringing method was applied. Kriging is mathematically closely related to regression analysis. Both theories derive a best linear unbiased estimator, based on assumptions on covariances, make use of Gauss- Markov theorem to prove independence of the estimate and error, and make use of very similar formulae. Based on Kriging, crime maps for kidnapping, rape, robbery, riot, theft and other crime were created.
Crime and social factors
The spatial distribution of social factors (literacy, work participation, sex ratio, etc) was also created to correlate the crime types with the social conditions of the place where the crime has been committed. Data, based on 2007 census, was collected, which pertained to employment, sex-ratio and educational standards. The tehsilwise maps of work-participation rates, literacy ratio and sex ratio data were created.
Based on the analysis, Buhana tehsil had maximum work participation rate, followed by Khetri, Surajgarh and Malsisar. On the other hand, tehsils of Jhunjhunu, Nawalgarh and Udaipurwati had the lowest work participation rate. It is justified to assume that crimerates would have a relationship with work-participation, since normally unemployment is a reason for committing crimes like theft, burglary and kidnapping.
The highest literacy rates were observed in tehsils of Buhana, Udaipurwati and Jhunjhunu. Hence, it was assumed that percentage of crime should be lower in these areas. On the other hand, literacy was lowest in Khetri, Nawalgarh, Figure 3: Comparison of kidnapping with work participation rate The black portions denote the areas with minimum work-participation rate The deep gray portions denote the zone of maximum theft. Figure 4: Comparison of theft and literacy rate The black portions denote the areas with minimum literacy rate. The deep gray portions denote the zone of maximum theft. Surajgarh and Malsisar of this district. The trends of crime cases like theft, kidnapping and crimes against women in areas where assumed to be more as literacy is lower. Especially crimes against women were expected to be more in these lower literacy zones. Sex-ratio on the whole was pretty low in the district of Jhunjhunu. Notably in Chirawa, Khetri and Malsisar have the lowest sex ratio. Khetri reported the greatest number of rape cases in the district.
Social standards of education, sex-ratio and employment were now compared with areas where crime was maximum. The aim was to derive a relationship between crime rates and lifestyle of people inhabiting the area. Crimes which are sporadic such as robbery, murder, dacoity and riots were found to have insufficient occurrences to warrant an analysis.
It was observed that although not much correlation between literacy and burglary existed, a slight overlap did exist.
However, it is to be stated that literacy is not the only factor that affects burglary. There might be other influences as well, such as work-participation and sex ratio. However, the method helps to analyse how much does a particular parameter affect a particular crime type. Based on the analysis of social factors and kidnapping cases, a good correlation between kidnapping and work participation was found (Figure 2).
Figure 2: Comparison of kidnapping with work participation rate
Black portions denote maximum work-participation. Light gray portions denote minimum work participation. Deep gray denotes zone of maximum kidnapping.
Maximum cases of kidnapping were reported in areas where work-participation was maximum. One line of reasoning believes that people from lower employment zones are responsible for kidnapping in areas where work participation is more. Also, since there is a decent probability of kidnappers being caught in either zone, they are distributed in both these zones. Again, apart from workparticipation, literacy might also play a role in these crimes. However, a definite correlation exists between theft and work-participation (Figure 3).
Figure 3: Comparison of kidnapping with work participation rate
The black portions denote the areas with minimum work-participation rate The deep gray portions denote the zone of maximum theft.
Based on the correlation between theft and literacy rate (Figure 4), it was clear that areas of low literacy rates were more prone to incidents of reported theft cases.
Figure 4: Comparison of theft and literacy rate
The black portions denote the areas with minimum literacy rate. The deep gray portions denote the zone of maximum theft.
From the above analysis, it is evident that literacy affects crime and so does work participation to a reasonable extent, whereas sex-ratio doesn’t cast such a major influence. The weighted overlay analysis of social factors with crime pattern was carried out to identify the hotspots of crime locations. The following social factors with weights were considered for the analysis.
- Sex ratio – 20 per cent
- Literacy rate – 40 per cent
- Work Participation rate – 40 per cent
Based on the weighted overlay analysis, the various hotspots of different types of crime were identified (Figure 5). The investigation preference areas (Table 1) for different types of crime were also suggested to ensure public safety.
Figure 5: The weighted overlay raster with map in the background
Table 1: Investigation preferences for each type of recurring crime
For sporadic crimes such as murder, rape, dacoity and riot, which are not so prevalent in the district of Jhunjhunu, a relationship cannot be drawn. For catching such criminals, the promptness of the police machinery is to be relied on. Also, it is advised to improve literacy, employment and sex-ratio in the district of Jhunjhunu since a direct correlation is found between these and crime-rates.
Conclusion
The representation of crime as a spatial function is of paramount importance. The crime data depicted on a spatial domain can be overlaid with education, sex and occupational data to obtain a correlation between each type of crime committed and the social conditions of the place. It leads to the identification of hotspots of different types of crime on a GIS platform. The methodological framework applied in the present investigation for crime mapping has very promising use in the current changing scenario and provides an effective method to law enforcement agencies for crime detection and crime prevention. Hence, the proposed crime analysis method can be upgraded in the user interface platform to ensure public safety through proper crime analysis.
References
- Corcoran J. J., Wilson I. D. & Ware J. A., 2003. Predicting the geo-temporal variations of crime and disorder. International Journal of Forecasting 19, 623–634.
- Johnson, C.P., 2000. Crime Mapping and Analysis Using GIS. Geomatics 2000: Conference on Geomatics in Electronic Governance, January 2000, Pune.
- Ozkan, K., 2004. Managing data mining at digital crime investigation. Forensic Science International 146, S37-S38.
- Patricio Tudela., 2004. Trends in Crime Mapping: The Challenges and Perspective of Integrating Crime Mapping In Local Safety and Governance Policies. CPRD.
- Petersen, H.S., 2001, Estimation of distance-decay parameters – GISbased indicators of recreational accessibility. ScanGIS’2001, Proceedings of the 8th Scandinavian Research Conference on Geographical Information Science, 25th – 27th June 2001.
