Predicting per capita violent crimes in urban areas: an artificial intelligence approach

Castelli, Mauro; Sormani, Raul; Trujillo, Leonardo; Popovič, Aleš

doi:10.1007/s12652-015-0334-3

Cited by 21 publications

(9 citation statements)

References 18 publications

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“…Once targets are identified, intelligent surveillance systems can evaluate and track targets [93], and collect forensic evidence-such as video recording [138]. AI can also be used to better predict future crime incidents and ensure the optimal allocation of crime law enforcement [144].…”

Section: Ai In the Governance Dimension Of Smart Citiesmentioning

confidence: 99%

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

et al. 2020

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Artificial intelligence (AI) is one of the most disruptive technologies of our time. Interest in the use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban locations that are enabled by community, technology, and policy to deliver productivity, innovation, livability, wellbeing, sustainability, accessibility, good governance, and good planning—has increased the demand for AI-enabled innovations. There is, nevertheless, no scholarly work that provides a comprehensive review on the topic. This paper generates insights into how AI can contribute to the development of smarter cities. A systematic review of the literature is selected as the methodologic approach. Results are categorized under the main smart city development dimensions, i.e., economy, society, environment, and governance. The findings of the systematic review containing 93 articles disclose that: (a) AI in the context of smart cities is an emerging field of research and practice. (b) The central focus of the literature is on AI technologies, algorithms, and their current and prospective applications. (c) AI applications in the context of smart cities mainly concentrate on business efficiency, data analytics, education, energy, environmental sustainability, health, land use, security, transport, and urban management areas. (d) There is limited scholarly research investigating the risks of wider AI utilization. (e) Upcoming disruptions of AI in cities and societies have not been adequately examined. Current and potential contributions of AI to the development of smarter cities are outlined in this paper to inform scholars of prospective areas for further research.

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Section: Ai In the Governance Dimension Of Smart Citiesmentioning

confidence: 99%

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

et al. 2020

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“…Bogomolov et al (2014) used logistic regression, support vector machine (SVM), neural network, decision tree, and random forest to predict eleven types of crime hot spots in London [4]. Castelli et al (2017) used geometric semantic operators to predict the per capita growth rate of violent crime in cities [5]. Saltos and Cocea (2017) used instance-based learning, regression model and decision tree to predict the frequency of criminal activities and anti-social behavior [7].…”

Section: Crime Prediction Methodsmentioning

confidence: 99%

“…Additional factors of social environment, demographics, economics, and human flow are also used in crime predictions [12]. Castelli et al (2017) combined the socio-economic data and law enforcement data of American cities since 1990 to predict the urban crime rate and achieved good results [5]. Kang and Kang (2017) proposed a feature-level data fusion method to combine different datasets of crime statistics, demography, and meteorology to predict crimes in Chicago, Illinois [12].…”

Section: Role Of Offenders In Criminal Activitiesmentioning

confidence: 99%

“…Benefited from its general applicability and predictive ability, machine learning has been used in various disciplines, including criminology. Both scholars and practitioners have been trying to take advantage of various machine-learning algorithms to predict crime patterns and tailor situational crime prevention strategies [4][5][6][7][8]. Some of them solely use historical crime data [7,[9][10][11], while many consider additional factors for the sake of improving the accuracy of crime prediction [12].…”

Section: Introductionmentioning

confidence: 99%

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Crime Prediction with Historical Crime and Movement Data of Potential Offenders Using a Spatio-Temporal Cokriging Method

Liu

Yang

et al. 2020

IJGI

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Crime prediction using machine learning and data fusion assimilation has become a hot topic. Most of the models rely on historical crime data and related environment variables. The activity of potential offenders affects the crime patterns, but the data with fine resolution have not been applied in the crime prediction. The goal of this study is to test the effect of the activity of potential offenders in the crime prediction by combining this data in the prediction models and assessing the prediction accuracies. This study uses the movement data of past offenders collected in routine police stop-and-question operations to infer the movement of future offenders. The offender movement data compensates historical crime data in a Spatio-Temporal Cokriging (ST-Cokriging) model for crime prediction. The models are implemented for weekly, biweekly, and quad-weekly prediction in the XT police district of ZG city, China. Results with the incorporation of the offender movement data are consistently better than those without it. The improvement is most pronounced for the weekly model, followed by the biweekly model, and the quad-weekly model. In sum, the addition of offender movement data enhances crime prediction, especially for short periods.

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“…Nonetheless, these forecasting and planning processes are not always exempt from errors and subsequent disasters [81]. In these specific scenarios, the blend of IoT and robotics systems may ease the management of the emergency situation itself, providing up-to-date information and reducing human intervention in hazardous contexts and preventing human accidents thanks to the use of robots [92][93][94]. It is likewise important to make optimal use of urban resources and enhance services accessed by citizens in their daily lives: something where the concept of smart city underpinned by AI and big data shall become the norm.…”

Section: Strengths Weaknessesmentioning

confidence: 99%

A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

et al. 2021

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The 17 Sustainable Development Goals (SDGs) established by the United Nations Agenda 2030 constitute a global blueprint agenda and instrument for peace and prosperity worldwide. Artificial intelligence and other digital technologies that have emerged in the last years, are being currently applied in virtually every area of society, economy and the environment. Hence, it is unsurprising that their current role in the pursuance or hampering of the SDGs has become critical. This study aims at providing a snapshot and comprehensive view of the progress made and prospects in the relationship between artificial intelligence technologies and the SDGs. A comprehensive review of existing literature has been firstly conducted, after which a series SWOT (Strengths, Weaknesses, Opportunities and Threats) analyses have been undertaken to identify the strengths, weaknesses, opportunities and threats inherent to artificial intelligence-driven technologies as facilitators or barriers to each of the SDGs. Based on the results of these analyses, a subsequent broader analysis is provided, from a position vantage, to (i) identify the efforts made in applying AI technologies in SDGs, (ii) pinpoint opportunities for further progress along the current decade, and (iii) distill ongoing challenges and target areas for important advances. The analysis is organized into six categories or perspectives of human needs: life, economic and technological development, social development, equality, resources and natural environment. Finally, a closing discussion is provided about the prospects, key guidelines and lessons learnt that should be adopted for guaranteeing a positive shift of artificial intelligence developments and applications towards fully supporting the SDGs attainment by 2030.

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Predicting per capita violent crimes in urban areas: an artificial intelligence approach

Cited by 21 publications

References 18 publications

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

Crime Prediction with Historical Crime and Movement Data of Potential Offenders Using a Spatio-Temporal Cokriging Method

A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

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