Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations

Perry, W. L. M.; McInnis, Brian; Price, Carter C.; Smith, Susan; Hollywood, John S.

doi:10.7249/rr233

Cited by 421 publications

(315 citation statements)

References 2 publications

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“…Some data science-based works have been proposed in literature for predicting impending conflicts or crises. As an example, Perry et al have proposed predictive policing mechanisms for crime forecasting as a tool for riot/violence mitigation (Perry et al 2013). Predictive policing involves statistical analyses in order to provide indicators to law enforcement agencies that they can intervene a potential criminal situation or solve a past crime.…”

Section: Ai-based Predictive/context-aware Crisis Analyticsmentioning

confidence: 99%

Crisis analytics: big data-driven crisis response

Qadir

Ali

Rasool

et al. 2016

Int J Humanitarian Action

115

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Disasters have long been a scourge for humanity. With the advances in technology (in terms of computing, communications, and the ability to process, and analyze big data), our ability to respond to disasters is at an inflection point. There is great optimism that big data tools can be leveraged to process large amounts of crisis-related data (in the form of user generated data in addition to traditional humanitarian data) to provide an insight into the fast-changing situation and help drive an effective disaster response. This article introduces the history and the future of big crisis data analytics, along with a discussion on its promise, enabling technologies, challenges, and pitfalls.

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Section: Ai-based Predictive/context-aware Crisis Analyticsmentioning

confidence: 99%

Crisis analytics: big data-driven crisis response

Qadir

Ali

Rasool

et al. 2016

Int J Humanitarian Action

115

View full text Add to dashboard Cite

show abstract

“…This enabled the police to able to respond to the predicted threats before a criminal act is committed. The crime rate in the Memphis area was reduced by 31 since the deployment of the system [5].…”

Section: A) Incident and Accident Preventionmentioning

confidence: 99%

“…Memphis, Tennessee use to be ranked as one of the most dangerous cities in the USA by Forbs Magazine in 2009. The Memphis Police Department deployed a statistical data analysis technology called Blue CRUSH, which allows analysts to rapidly evaluate incoming patrol data against historical trends [5]. This enabled the police to able to respond to the predicted threats before a criminal act is committed.…”

Section: A) Incident and Accident Preventionmentioning

confidence: 99%

Intelligent multimedia surveillance system for safer cities

Arikuma

Mochizuki

2016

SIP

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“…These developments are reasonably well documented in the academic and policy literature [2,3,18,59]. The predictive policing mantra is closely associated with 'geographic criminology' [28] and the new 'crime scientists' who challenge 'mainstream criminologists' to engage with law enforcement practitioners in understanding the spatial and temporal factors that underlie and shape criminal opportunity structures (cf.…”

Section: Predictive Policing Big Data and Crime Controlmentioning

confidence: 99%

“…These techniques can help departments address crime problems more effectively and efficiently. They are used across the United States and elsewhere, and these experiences offer valuable lessons for other police departments as they consider the available tools to collect data, develop crime-related forecasts and take action in their communities [18].…”

Section: Predictive Policing Big Data and Crime Controlmentioning

confidence: 99%

Policing, crime and ‘big data’; towards a critique of the moral economy of stochastic governance

Sanders

Sheptycki

2017

Crime Law Soc Change

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The paper defines 'stochastic governance' as the governance of populations and territory by reference to the statistical representations of metadata. Stochastic governance aims at achieving social order through algorithmic calculation made actionable through policing and regulatory means. Stochastic governance aims to improve the efficiency and sustainability of populations and territory while reducing costs and resource consumption. The algorithmic administration of populations and territory has recourse to 'Big Data'. The big claim of Big Data is that it will revolutionize the governance of big cities and that, since stochastic governance is data driven, evidence-led and algorithmically analysed, it is based on morally neutral technology. The paper defines moral economy -understood to be the production, distribution, circulation and use of moral sentiments emotions and values, norms and obligations in social space -through which it advances a contribution to the critique of stochastic governance. In essence the argument is that certain technological developments in relation to policing, regulation, law and governance are taking place in the context of a neo-liberal moral economy that is shaping the social outcomes of stochastic governance. Thinking about policing in both the narrow sense of crime fighting and more broadly in its Foucaldian sense as governance, empirical manifestations of 'policing with Big Data' exhibit the hallmarks of the moral economy of neo-liberalism. This suggests that a hardening of the socio-legal and technical structures of stochastic governance has already largely taken place.

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Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations

Cited by 421 publications

References 2 publications

Crisis analytics: big data-driven crisis response

Crisis analytics: big data-driven crisis response

Intelligent multimedia surveillance system for safer cities

Policing, crime and ‘big data’; towards a critique of the moral economy of stochastic governance

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