JOURNAL ARTICLE

POLICE ACTIONS IN INVESTIGATIONS INTO THEFT IN THE LIGHT OF RESEARCH.

  • Published In: Police Review / Przegląd Policyjny, 2024, v. 154, n. 2. P. 320 1 of 3

  • Database: Central & Eastern European Academic Source 2 of 3

  • Authored By: MARDZEJOWSKI, WIESLAW; REDSZKI, LUKASZ 3 of 3

Abstract

Theft is the most common form of property crime, and the effi ciency of its detection signifi cantly affects public perceptions of safety. The conducted research indicates that in theft cases, police offi cers use a wide range of activities, depending on the initial assessment of the form of the crime committed. The selection of actions should focus on identifying the perpetrator and gathering evidence to determine whether the perpetrator's actions meet the criteria for an offense under art 278 of the Penal Code. Depending on the unit handling the investigation, the preferred actions include witness interviews, detailed inspection of the crime scene, securing traces, and obtaining and analyzing surveillance footage. There is a notable underutilization of data from police databases and information gathered through operational and reconnaissance activities. In this situation, it is recommended to attempt to develop and implement an algorithm for the actions police offi cers should take in cases involving various forms of theft. This could lead to the fuller use of all available sources of information, improve the quality of the collected evidence, and reduce the number of mistakes made during the investigation. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Police Review / Przegląd Policyjny. 2024/04, Vol. 154, Issue 2, p320
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2024
  • ISSN:0867-5708
  • DOI:10.5604/01.3001.0054.8527
  • Accession Number:183632555
  • Copyright Statement:Copyright of Police Review / Przegląd Policyjny is the property of Police Academy in Szczytno and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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