Artificial Intelligence Adoption in Predictive Policing to Predict Crime Mitigation Performance

Hind Rashed Saleh Al Shamsi, Su'aidi Safei
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Abstract

The global adoption of AI-powered predictive policing, utilizing big data, is becoming a prevalent strategy for crime control and law enforcement enhancement. Recognizing its potential, Abu Dhabi Police places emphasis on officer training and collaborative efforts for crime prevention. As the integration of predictive policing continues within Abu Dhabi Police, the importance of exploring the value of training and collaborative learning becomes even more crucial (Abu Dhabi Police GHQ, 2020). This study's objective is to uncover the intricate relationship between crime mitigation performance and key factors, encompassing Predictive Policing Adoption, Specialised Technology Training, Innovative Officer Performance, and Collaborative Learning. Questionnaire survey was used to collect data from participants who are employees of the Abu Dhabi Crime Scene Department. A total of 316 valid responses were used in the development of multi-linear regression model to predict crime mitigation performance. By utilizing the developed multi-linear regression model, stakeholders can forecast Crime Mitigation Performance (CMP) by substituting the values of Predictive Policing Adoption (PPA), Specialised Technology Training (STT), Innovative Officer Performance (IOP), and Collaborative Learning (CL) into the formula. This predictive tool offers the Abu Dhabi Crime Scene Department a valuable resource to proactively assess and plan for crime mitigation outcomes, enhancing their strategic decision-making capabilities and fostering a more effective approach to law enforcement operations
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在预测警务中采用人工智能来预测减少犯罪的表现
全球采用利用大数据的人工智能预测警务,正在成为控制犯罪和加强执法的普遍策略。阿布扎比警方认识到其潜力,将重点放在警官培训和预防犯罪的合作努力上。随着阿布扎比警方继续整合预测性警务,探索培训和协作学习价值的重要性变得更加重要(阿布扎比警察GHQ, 2020)。本研究的目的是揭示减少犯罪绩效与关键因素之间的复杂关系,包括预测性警务采用、专业技术培训、创新警官绩效和协作学习。问卷调查是用来收集数据的参与者谁是阿布扎比犯罪现场部门的雇员。在开发多线性回归模型以预测减少犯罪绩效时,共使用了316份有效答复。通过利用开发的多元线性回归模型,利益相关者可以通过将预测性警务采用(PPA),专业技术培训(STT),创新官员绩效(IOP)和协作学习(CL)的值替换为公式来预测犯罪缓解绩效(CMP)。这一预测工具为阿布扎比犯罪现场部提供了一种宝贵的资源,以主动评估和规划减轻犯罪的成果,增强其战略决策能力,并促进更有效的执法行动方法
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CiteScore
0.90
自引率
20.00%
发文量
25
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