Big Data in Investigating and Preventing Crimes

Bulgakova, Elena; Bulgakov, Vladimir; Trushchenkov, Igor; Vasilev, Dmitry; Kravets, Evgeny

doi:10.1007/978-3-030-01358-5_6

Cited by 10 publications

(3 citation statements)

References 19 publications

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“…The identification and categorization of these behaviours imply an understanding of the emotional state of the suspect himself/herself and of the society which includes him/her (Umair, et al, 2015). For this reason, crime statistics are important, and models which include a range of dynamic micro or macro areas that predict crime behaviour with hypothetical and experimental crime models have been developed in order to understand crime (Snaphaan & Hardyns, 2019;Bulgakova et al, 2019). However, the main problem in crime data is that it is in the form of a complex and large data stack before analysis.…”

Section: Rulemaking From Crime Statistics and Estimation Of Future Crime Tendencymentioning

confidence: 99%

Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

Güzel¹

2021

JPLC

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Young adults engage in a disproportionately high rate of problematic behaviors such as risky sexual activities, academic dishonesty, and substance abuse. In order to understand why this occurs, two lesser-known constructs related to risktaking behavior were investigated in this study: differential identification, which has yet to be empirically studied in the context of emerging adults, and criminogenic thinking, which has only been evaluated in this context to a minimal degree. To bridge the gap between these two constructs, motivation was hypothesized to be a moderator in each of these respective relationships. Data from 309 emerging adults were recruited via Amazon's Mechanical Turk (MTurk) to complete the survey materials online. The analysis consisted of a hierarchical multiple linear regression. Results indicated that motivation did not moderate either relationship (i.e., differential identification and risk-taking behavior and criminogenic thinking and risk-taking behavior). However, a main effect was found between one aspect of differential identification and risk-taking behavior. No other main effects were found in this study. Potential explanations for the results of this study are discussed, as well as implications and directions for future research.

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Section: Rulemaking From Crime Statistics and Estimation Of Future Crime Tendencymentioning

confidence: 99%

Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

Güzel¹

2021

JPLC

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“…It can increase productivity, create new jobs, and develop new products and services (Janssen et al, 2012;Magalhaes and Roseira, 2020). It can also provide enhanced policies and public services, such as prompt response to the COVID-19 pandemic (Awotunde et al, 2021) or protecting people safe from crime (Bulgakova et al, 2019). Therefore, the ability to use data well determines the competitiveness of individuals, firms, and countries (Magalhaes and Roseira, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…It can also provide enhanced policies and public services, such as prompt response to the COVID-19 pandemic (Awotunde et al. , 2021) or protecting people safe from crime (Bulgakova et al. , 2019).…”

Section: Introductionmentioning

confidence: 99%

Government data openness and knowledge management: configurational patterns for national competitiveness

Ham

Koo

Lee

2022

IMDS

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PurposeIn the data economy era, despite the tremendous effort of governments to actively provide and use open data, its effect on national performance such as competitiveness differs widely from country to country. A sufficient knowledge base and its appropriate management are important to effectively derive the potential value from open data. A country can implement multiple and equally viable means to effectively align open data with knowledge management, which lead to high national performance. However, previous studies lack consideration of the possibility of these various configurations. To fill the research gap, this study aims to investigate the configurational patterns constituted by government data openness and knowledge management for national competitiveness.Design/methodology/approachFrom the open innovation perspective, this study collected data from the global reports of 76 countries and examined them through fuzzy-set qualitative comparative analysis (fsQCA).FindingsFour configurational patterns are identified, namely, coupled (outbound-focused)-, coupled (inbound-focused)-, inbound-focused-, and outbound-focused national competitiveness.Originality/valueThis study provides a foundation that enables researchers to build a holistic and balanced perspective that can manage open government data and develop knowledge management capability.

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Method of Acquiring the Video Conference Using the Skill in Investigative Actions

Kravets

Gladkova

Shinkaruk

et al. 2019

Communications in Computer and Information Science

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Big Data in Investigating and Preventing Crimes

Cited by 10 publications

References 19 publications

Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

Government data openness and knowledge management: configurational patterns for national competitiveness

Method of Acquiring the Video Conference Using the Skill in Investigative Actions

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