Trends in Data Driven Law Enforcement
Trends in Data-Driven Law Enforcement
Law enforcement continues to undergo evolution each wake. Technological advancements demand that policing is always up to the current societal standards. New technological gadgets are always expensive when they get introduced to the market. Law enforcement agencies have to spend considerable sums each time there is a new gadget in the market (Cox, Marchionna & Fitch, 2015). Given the nature of the technological world, law enforcement has become an expensive affair; this is made worse by the shrinking budget that police agencies have to work with. In light of the expenses and the dynamics involved in law enforcement, policing has taken a data-driven approach when handling issues within its ambit (Cox, Marchionna & Fitch, 2015).
According to Ratcliffe (2006), intelligence-led policing has data at its core as the sole basis upon which decisions are made. Law enforcement officers incorporate data analysis and criminal intelligence to come up with managerial approaches to handle recidivists.
Sandler (2014) observes that one of the areas that have experienced a vast deployment of data-driven policing is the war on terror. Through data-driven approaches, the police can anticipate a crime before it could get full blown. Data-driven law enforcement must have technology at its core. However, overdependence on technology means that the exercise would be expensive while at the same time raising issues of privacy intrusion (Cox, Marchionna & Fitch, 2015). Also, there is the possibility that the community would raise confidence issues on the police and the regulatory agencies.
Intelligence-led policing is dependent on data collection to forecast and prevent crime. Fusion Centers facilitate the exchange and collection of data. The collected data is then shared with relevant authorities. Data supplied by fusion centers inform policy and tactical deployment of resources. In a way, ILP depends on fusion centers (Taylor and Davis, 2010).
Kearon (2013) asserts that data quality depends on the authenticity of the input; skewed and incomplete inputs translate to the establishment of false positives. When false positives get created, the police would direct their energies to nonexistent hotspots or on areas of low crime potential. Inaccurate data come into police possession by virtue of selective reporting of certain crimes; hence such data cannot get used to predicting the occurrence of the reported crime (Cox, Marchionna & Fitch, 2015). There is also police discretion that may alter the quality of data used to forecast crimes. When discretion comes into the picture, there is always the aspect of the subjectivity of whoever that makes the choice of what crime data is worth factoring into the prediction software.
Cox, Marchionna, and Fitch (2015) assert that it is common for discriminatory practices to go unnoticed when using predictive software under a data-driven policing model. It is a common cry that some police activities unfairly target marginalized and impoverished members of the society. Under the foregoing observation, crime data may falsely suggest that crime rates are high in some areas when the truth is converse to the data indication. There may be a likelihood that the areas assumed to be high on crime are heavily policed thus the high number of arrests. Using data from such encounters to predict future crimes will mean that the already over-policed areas will get more enforcement officers (Kearon, 2013).
Data-dependent policing means that massive data collection must occur; this raises questions over mass surveillance. Uncontrolled mass surveillance takes away the right to privacy; this leaves citizens exposed, unable to exercise self-expression (Kearon, 2013). It is common for humans to exercise restraint when they know that there someone watching. In addition to interfering with the person, a data-driven law enforcement agency would have to faceoff with suspected criminals in court battles over privacy issues (Kearon, 2013).
Riebling (2006) observes that data collection must always occur within the confines of the bill of rights. Intelligence policing requires the massive collection of data; the collection might be illegal at times or the public may disapprove the acts. People are becoming conscious of their rights, it is becoming hard than never before to collect data without opposition from the public. However, the future of policing is data dependent; proper policing can only occur in an environment where there is reasonable data access for the police.
The future of law enforcement is data-driven, but then there is much more than the policing agencies need to work on for there to exist credibility and trust. The explored socio-legal concerns require the responsible agencies to allow for the independent assessment of crime data. It is also necessary that governments put in place measures that would ensure that data collection complies with human rights laws.
Cox, S. M., Marchionna, S., & Fitch, B. D. (2015). Introduction to policing. Sage Publications.
Kearon, T. (2013). Surveillance technologies and the crises of confidence in regulatory agencies. Criminology & Criminal Justice, 13(4), 415-430.
Ratcliffe, J. (2006) Intelligence-Led Policing. New York: Routledge.
Riebling, M. (2006). The New Paradigm: Merging Law Enforcement and Intelligence Strategies. Philadelphia: Center for Policing Terrorism.
Sandler, T. (2014). The analytical study of terrorism: Taking stock. Journal of Peace Research, 51(2), 257-271.
Taylor, R. and Davis, J. (2010). Intelligence-Led Policing and Fusion Centers. Long Grove Il: Waveland Press Inc.
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