Crime data in the context of intelligence-led

Crime data in the context of intelligence-led and/or problem-oriented policing. What is “criminal intelligence”? To most people, including criminal investigators, the term conjures up images of collator-style systems used to store and retrieve the information we collect about crime and criminals. As the volume and variety of the information we collect has expanded, we have gradually introduced more and more complex systems to assist with its storage and retrieval.

Looking into the future, how might technological advancements change the type and amount of empirical evidence available to law enforcement? More specifically, technology such as biometrics, drones, stingray, artificial intelligence, enhanced shot spotter technology, enhanced video surveillance, robots, enhanced body worn cameras with increased capabilities, facial recognition etc. seem poised to significantly expand the flow of evidence and information available to law enforcement agencies.

Crime Data

Draft a memo to a superior within your organization. This memo should be 2-3 pages, standard fonts and margins and double spaced. This superior has requested that you research a technology that you believe will have the largest impact on the scope and type of data available to law enforcement in the coming 5-10 years. Describe the technology and its current and possible future functionality. Outline how this technology will likely have utility for law enforcement agencies in the context of intelligence-led and/or problem-oriented policing.

Intelligence Led Policing

Intelligence-led policing (ILP) is a managerial law enforcement model that seeks to place crime intelligence at the forefront of decision-making. This model has been widely adopted, at least notionally, in the United States, United Kingdom, Canada, and Australia. Drawing on interviews with intelligence analysts from two Australian state law enforcement agencies, this article contributes to the relatively small body of literature that has examined ILP in practice.

The article identifies three relational themes that inhibit the successful implementation of ILP: analysts and data, analysts and tools, and analysts and decision makers. Furthermore, it calls attention to the need to better understand the structure and operations within law enforcement agencies, including the similarities and differences among organizational units, in order to better understand how these nuances shape the practice of ILP.

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