Incorporating Survey Perceptions of Public Safety and Security Variables in Crime Rate Analyses for the Visegrád Group (V4) Countries of Central Europe

Public governance has evolved in terms of safety and security management, incorporating digital innovation and smart-analytics-based tools to visualize abundant data collections. Urban safety and security are vital social problems that have many branches to be solved, simplified, and improved. Curre...

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Bibliographic Details
Main Authors: Usman Ghani, Peter Toth, Dávid Fekete
Format: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Societies
Subjects:
Online Access:https://www.mdpi.com/2075-4698/12/6/156
Description
Summary:Public governance has evolved in terms of safety and security management, incorporating digital innovation and smart-analytics-based tools to visualize abundant data collections. Urban safety and security are vital social problems that have many branches to be solved, simplified, and improved. Currently, we can see that data-driven insights have often been incorporated into planning, forecasting, and fighting such challenges. The literature has extensively indicated several aspects of solving urban safety problems, i.e., social, technological, administrative, urban, and societal. We have a keen interest in the data analysis and smart analytics options that can be deployed to enhance the presentation, promotional analysis, planning, forecasting, and fighting of these problems. For this, we chose to focus on crime statistics and public surveys regarding victimization and perceptions of crime. As we found through a review, many studies have indicated the vitality of crime rates but not public perceptions in decision-making and planning regarding security. There is always a need for the integration of widespread data insights into unified analyses. This study aimed to answer (1) how effectively we can utilize the crime rates and statistics, and incorporate community perceptions and (2) how promising these two ways of seeing the same phenomena are. For the data analysis, we chose four neighboring countries in Central Europe. We selected CECs, i.e., Hungary, Poland, Czech Republic, and Slovakia, known collectively as the Visegrád Group or V4. The data resources were administrative police statistics and ESS (European Social Survey) statistical datasets. The choice of this region helped us reduce variability in regional dynamics, regime changes, and social control practices.
ISSN:2075-4698