The advent of the Internet has played a key role in promoting the secure sharing of data between police departments. As public access to the Internet continues to grow, it has become a part of everyday life for many people. This has provided police departments with the ability to publically, and cost effectively, disseminate information to a large audience. Some electronic government-to-citizen services have been developed to effectively share this information while ensuring data quality (Boondao, 2003). Providing these online services can improve the performance of public services, increase governmental accountability, and still be cost effective (Boondao, 2003).
An example of a government-to-citizen server that has been developed is the idea of E-policing. E-policing is any online service with the goal of aiding in police work. E-policing can be used to both gather data for local police and also provide important information back to the public. Location-based services can be used by police to locate a mobile device in an emergency situation. Incorporating this technology can drastically reduce response times in situations where every second counts. In Thailand, Roongrasamee Boondao (2003) proposed a system that used an HTML approach to support communication
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between the local police and the public. Members of the public could authenticate with the system through a Web browser to view spatially relevant crime maps and to perform basic geoprocessing tasks. Requests were directed to a secure map server and a response was then returned to the user’s Web browser. Police could also authenticate with the system and were granted access to a higher level of data (Boondao, 2003). Other police services around the world have increased their participation in E-policing initiatives. The Royal Canadian Mounted Police (RCMP) has used E-policing to expedite the dissemination of information to the public. Moreover, with E-policing, police services can support the online reporting of criminal activities. Doing so can free-up additional police resources and personnel that would have otherwise been tied-up with processing these crime reports (LeBeuf, 2006). Additionally, when well-designed, online incident reporting can promote data accuracy by helping to focus user contributions and by ensuring that the same reporting procedures are used for every report that is submitted (LeBeuf, 2006).
When utilizing any location-based data that is associated with a specific time, privacy concerns are raised. To avoid the potential for a breach in user privacy, a two-tier system was proposed by the Thailand case study mentioned earlier for securely handling all personal information. To protect the user’s privacy, users would remain anonymous while viewing crime data and performing basic queries. Any personal information that was submitted with each request to the secure server would be immediately deleted once the processing had been completed. However, for events that were deemed to be serious, a record of the user’s personal information would be forwarded to the local police service (Boondao, 2003). The proposed system would utilize a PostgreSQL database, as well as PostGIS to handle any spatial data. The database server would support real-time updates to the data tables and store the list of authorized users (Boondao, 2003). The methods used in the Thailand case study are quite different from those used in the aforementioned Purdue University case study; however, both methods have the potential to be effective tools for protecting user privacy.
Mobile GIS can also be used to actively monitor suspects in criminal investigations. The popularity of cell phones has made it possible for police investigators to covertly track the travel patterns and activities
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of suspected criminals both in real-time, as well as retroactively (Cooper, 2007). In their 2007 presentation,
Using Mobile Phone Data Records to Determine Criminal Activity, Cooper and Schmitz discuss a system
for actively tracking suspects using their cell phones. With their system, an investigator is able to send a blind SMS to the target’s cell phone, resulting in a response message being returned to the investigator with information regarding which cellular node is being accessed. Messages are sent at regular intervals and the system calculates an approximated travel route. The route is then overlaid with street network data and snapped to the nearest logical feature (Cooper, 2007). Routes can then be spatially analyzed to determine the suspect’s proximity to points of recent criminal activity or to any suspect specific points of interest. Routes can also be created after a criminal activity has occurred as cell phone records are often stored by service providers and can be acquired with a search warrant (Cooper, 2007). Routes that are generated can be used to test the validity of suspect alibis. Although a useful tool, Schmitz recommended that the system only be used as an aid in criminal investigations, and not for prosecution purposes, since a degree of inaccuracy must be assumed.
A real-world example that showcases the benefits of using an active tracking system can be evidenced in a South African court case that resulted in the conviction of two members from a local gang. The gang of four males kidnapped a couple and stole their car and possessions. After raping the woman, the two victims were murdered and their bodies were dumped. During an eventual police shootout, the two gang members who were present at the shootout were killed. The other two suspected gang members were later arrested and when questioned, denied having any involvement in the incident (Cooper, 2002). However, the two deceased gang members had remained in contact with the suspects by calling the suspect’s cell phone from the hostage’s cell phone. The cell phone records and associated crime maps were subsequently produced and were used in court to break their alibi, eventually leading to a conviction. The map produced contained the travel patterns of both parties involved, as well as their potential activity areas. The data was accompanied by points of interest such as the location of the burned out car, the murder site of the couple, as well as the locations of witnesses (Cooper, 2002).
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The previous examples help to highlight some of the positive aspects of using mobile GIS in the field of crime mapping. However, in the book Mapping and Analyzing Crime Data: Lessons from Research
and Practice, Ken Pease (2000) states that caution should be taken when associating spatial locations with
crimes. Focusing solely on locations can take attention away from other pertinent non-spatial variables that may have had an impact on the crime committed, such as the context of the crime or the motivation of the parties involved (Pease, 2000). Pease was of the opinion that GIS should be used only as an aid to, and not a substitute for, non-spatial crime analysis approaches. However, Pease also stated his belief that incorporating GIS analysis with locational data from mobile devices has the potential to provide major benefits to the future of crime mapping (Pease, 2000).
The examples above help to support the notion that the use of locational data and spatial analysis is beneficial to the field of crime mapping. In some situations, using a GIS has improved the response time of emergency services when responding to life or death situations. Nevertheless, it is important to note that locational data can be highly sensitive and strict policies must be enforced to protect the privacy of the user.