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Computer databases are an effective tool manage, organize and catalog inputs. Computers databases reduce required workspace and allows the user to retrieve data once certain fields in the filters meet the specified parameters. This is very effective to “data mine”. One can search through a series of parameters that are met to find what data they needed to retrieve (Oatley et al, 2006). Database that are linked by an information network can essentially allow the user to “data mine” over multiple databases to broaden or narrow their results. Also, information networks linked to different databases such as financial institutions can find links between persons that may not have been known otherwise such as with organized crime (Oatley et al, 2006).
As we improve filters on databases, we can reset values on those databases to improve them. One such method for use is in the field of identity. There is a database for those who willingly hand over their identification card or drivers license to an officer. That individual who may not be currently carrying that on them can also give the officer his or her date of birth, their social security number and their name and be found in this database. Xu et al, sees that this database can be improved as have other researchers (2007). Others have theorized with some success that for those who provide fake names, date of births and social security numbers cannot successfully develop an original fake identity and that the provided fake information is relatively close enough to identify a suspect (Xu et al, 2007). Xu et al, expands on this by adding additional algorithms to pick up an identity based on the social statuses or identities that the individual maintains unwittingly (2007). This concept will enable officers to find networks of criminals (Xu et al, 2007).
Crime mapping and geospatial technology can be a tremendous tool in the intelligence led policing (ILP) model. Commonly called geographic information systems (GIS) can function as a key source to “hot spot” policing. The useful ness of the GIS is dependent on the functionality of the filters that the user can apply combined with the data that the analysist places into the system. The less user friendly the filters are, the less likely that it will be an effective tool that the department’s officers would like to use. The more user friendly the interface the more likely they will incorporate it into their duties. This concept also applies to the filters.
If using the ILP method of policing to find “hot spots” for a certain type of crime the GIS cannot filter for to demonstrate what the targeted crime is then again, the tool is ineffective. Departments have had some successes with GIS and have at times tried to improve officers’ interactions with the citizens in their communities through use of more mobile computer systems (Taniguchi & Gill, 2018). Some officers may not realize that although the system is in place for them to input data as they collect it, it does not always translate to actionable intelligence (Taniguchi & Gill, 2018).
Therefore, it is imperative for departments who want to use the ILP method to maintain an analyst or analysts in their department to analyze the data and provide that intelligence. What I think is a most effective use of the GIS is with the correct data entered, it does not just help police to combat targeted crimes in hotspots; it is can also be used to forecast or predict potential “hot spots” so that they can be proactive in their defense of their citizens (Fitterer et al, 2015).
Fitterer, J., Nelson, T., & Nathoo, F. (2015). Predictive crime mapping. Police Practice & Research, 16(2), 121–135. https://doi.org/10.1080/15614263.2014.972618
Oatley, G., Ewart, B., & Zeleznikow, J. (2006). Decision support systems for police: Lessons from the application of data mining techniques to “soft” forensic evidence. Artificial Intelligence and Law, 14(1-2), 35–100. https://doi.org/10.1007/s10506-006-9023-z
Taniguchi, T., & Gill, C. (2018). The mobilization of computerized crime mapping: a randomized controlled trial. Journal of Experimental Criminology, 1–13. https://doi.org/10.1007/s11292-018-9328-4
Xu, J., Wang, G., Li, J., & Chau, M. (2007). Complex Problem Solving: Identity Matching Based on Social Contextual Information. Journal of the Association for Information Systems, 8(10), 524–535,537–544. https://doi.org/10.17705/1jais.00141
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