تلفن: ۰۴۱۴۲۲۷۳۷۸۱
تلفن: ۰۹۲۱۶۴۲۶۳۸۴

دانلود رایگان مقاله انگلیسی برداشت تفسیر داده های کشف شده در الگو های تکراری جنایات با کاوش fine grained – الزویر ۲۰۱۸

عنوان فارسی: برداشت تفسیر داده های کشف شده در الگو های تکراری جنایات با کاوش fine grained
عنوان انگلیسی: Adopting data interpretation on mining fine-grained near-repeat patterns in crimes
تعداد صفحات مقاله انگلیسی : 11 تعداد صفحات ترجمه فارسی : ترجمه نشده
سال انتشار : 2018 نشریه : الزویر - Elsevier
فرمت مقاله انگلیسی : PDF کد محصول : E6208
محتوای فایل : PDF حجم فایل : mb 3
رشته های مرتبط با این مقاله: پزشکی، علوم اجتماعی
گرایش های مرتبط با این مقاله: پزشکی قانونی، جرم شناسی
مجله: مجله پزشکی قانونی و حقوقی - Journal of Forensic and Legal Medicine
دانشگاه: College of Computer - National University of Defense Technology - Changsha - China
کلمات کلیدی: تجزیه و تحلیل جرم، اثر تکراری، تفسیر اطلاعات، الگوهای جرم و جنایت، روش دست خط-سرنخ
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چکیده

ABSTRACT

The near-repeat effect is a well-known phenomenon in crime analysis. The classic research methods focus on two aspects. One is the geographical factor, which indicates the influence of a certain crime risk on other similar crime incidents in nearby places. The other is the social network, which demonstrates the contacts of the offenders and explain ”near” as degrees instead of geographic distances. In our work, these coarse-grained patterns discovering methods are summarized as bundled-clues techniques. In this paper, we propose a knotted-clues method. Adopting a data science perspective, we make use of a data interpretative technology and discover that the near-repeat effect is not always so near in geographic or network structure. With this approach, we analyze the near-repeat patterns in all districts of the dataset, as well as in different crime types. Using open source data from Crimes in Chicago provided by Chicago Police Department, we find interesting relationships and patterns with our mining method, which have a positive effect on police deployment and decision making.

نتیجه گیری

5. Conclusions

In this paper, we propose a knotted-clues method to obtain finegrained results of the near-repeat phenomenon both in districts and in various crime types. In the view of data interpretation, we combine correlation coefficient, hierarchical clustering and frequency patterns mining in a particular order. In districts, we refine the results to specific district rather than the near range. The accuracy results may help us identify the distribution of criminal forces in real crime networks. In police deployment, it can better coordinate the allocation of resources and strengthen cooperations. When combating crime, we can get the criminal centers or sources through near-repeat fine-grained analysis, so as to concentrate resources and improve efficiency. Through our approach, we find the associate patterns of different crime types and analyze the hierarchical relationships between the patterns. In actual actions, we should try our best to avoid the occurrence of crime types in the deeper pattern, in order to protect the lives and properties of the victims.