This study focuses on the current service/care difficulties and challenges that social institutions in Hungary are facing during their daily operations; how they can react to them utilizing their internal resources, mechanisms, and capacities; and what concrete, tangible needs and demands are emerging in terms of methodological professional support, potential forms, interventions, and direction for professional development. A total of 24 general and 55 specific service and operational problems were identified and assessed in eight different service areas (family and child welfare services, family and child welfare centers, respite care for children, care for the homeless, addiction intervention, care for people with disabilities, care for psychiatric patients, specialized care for the elderly, and basic services for the elderly). The empirical base of the study uses a database of 201 online questionnaires completed by a professional target group working for social service providers in two counties (Győr-Moson-Sopron and Veszprém), representing 166 social service providers. The questionnaires were completed between November and December of 2022. The findings will be used to develop a professional support and development problem map. Social institutions face complex and serious service/care difficulties and challenges in their daily operations. Three distinctive basic problems clearly stand out in both severity and significance from the complex set of factors assessed. The biggest problem in the social care system is clearly the complex challenge of low wages, followed by the administrative burdens in the ranking of operational difficulties, and the third key factor was the psycho-mental workload of staff.

Geographical mapping has revolutionized data analysis with the help of analytical tools in the fields of social and economic studies, whereby representing statistical research variables of interest as geographic characteristics presents visual insights. This study employed the QGIS mapping tool to create predicted choropleth maps of Visegrád Group countries based on crime rate. The forecast of the crime rate was generated by time series analysis using the ARIMA (autoregressive integrated moving averages) model in SPSS. The literature suggests that many variables influence crime rates, including unemployment. There is always a need for the integration of widespread data insights into unified analyses and/or platforms. For that reason, we have taken the unemployment rate as a predictor series to predict the future rates of crime in a comparative setting. This study can be extended to several other predictors, broadening the scope of the findings. Predictive data-based choropleth maps contribute to informed decision making and proactive resource allocation in public safety and security administration, including police patrol operations. This study addresses how effectively we can utilize raw crime rate statistics in time series forecasting. Moreover, a visual assessment of safety and security situations using ARIMA models in SPSS based on predictor time-series data was performed, resulting in predictive crime mapping.

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.

Public safety and security management projects are devised to reduce crime, fear and calamities by prevailing law and order to reduce the harm in society. In a certain context, social impact assessment is a novel way to reveal the extent of effectiveness for these projects. This case study presents an Expert System based methodology regarding social impact assessment for two urban security management projects in Pakistan. An Expert System, DoctuS is employed as a tool to build a rule-based model tool using social impact attributes (as variables) from literature and expert knowledge (Author’s own insight as being involved in projects). Case-based reasoning (CBR) method is employed for the measurement of the impact of urban security management KPIs (Key Performance Indicators) in the two case projects. The empirical findings in this case study approaches the impact assessment by quantitative analysis of crime rates in JMP statistical package and Qualitative Expert knowledge-based model in DoctuS. Thereby, the study evaluates the pre and post ante project situations in order to assess the project’s effectiveness and impact on improved urban safety and security. In the first step, the projects are briefly described, followed by the crime rate analysis under the project jurisdictions and rules between attributes are defined. The second step defines the qualitative rule-based model along with the two cases described, and validated in DoctuS tool. Some attributes and rules have changed, and a new concise model is presented. The Expert System compares the project cases and evaluates the social impact assessment based on the defined KPIs. The Expert system validation presents a novel model for social impact assessment of subject urban security management projects. We propose this methodology and model as useful in investigating social impact assessment of projects specifically dealing with urban safety and security management. However, the generalizability of the findings of this case-study based articles need more sophisticated tests as recommendation for the extension to this study