The forensic information identification based on machine learning algorithms

被引：1
|
作者：
Kowalski, Piotr A. [1 ,2 ]
Kusy, Maciej [3 ]
Kocierz, Karol [1 ]
机构：
[1] AGH Univ Krakow, Fac Phys & Appl Comp Sci, Al A Mickiewicza 30, PL-30059 Krakow, Poland
[2] Polish Acad Sci, Syst Res Inst, Ul Newelska 6, PL-01447 Warsaw, Poland
[3] Rzeszow Univ Technol, Fac Elect & Comp Engn, Al Powstancow Warszawy 12, PL-35959 Rzeszow, Poland
来源：
FORENSIC SCIENCE INTERNATIONAL-DIGITAL INVESTIGATION | 2023年 / 47卷
关键词：
Crime events; Clusterization; Calinski-Harabasz index; Kernel density estimation; PARAMETERS;
D O I：
10.1016/j.fsidi.2023.301619
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Investigating crimes in selected areas, studying their occurrence and appropriate graphical visualization may an important supportive information for police units. Nowadays, the available IT tools can improve the work law enforcement agencies. For this purpose, in this work, we propose an exploration approach which allows for analyzing crime events that are recorded in the city of Baltimore, Maryland, USA. Based on the collected data, seven types of offenses are recorded. First, they are analyzed in terms of time verifying whether any type event depends on the day of the week or a period of the day. Then the distribution of the crimes is examined spatial clusterization and kernel density estimation methods. As a result of the analysis, it is shown when and where the citizens of the considered city are subject to the highest crime rate.
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收藏
页数：11
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