Abstract:

Current global challenges in crisis management driven by the increasing frequency and complexity of emergency events necessitate a transformation in higher education toward digital intelligence and data-driven decision-making. In response to these needs, this article presents the interim results of a research project aimed at developing an innovative data model and a streamlined data management process tailored specifically for risk mapping and disaster mitigation. The core of this scientific contribution involves the development of conceptual and logical models using Unified Modeling Language and entity-relationship diagrams. These models optimize data flow for crisis infrastructure, addressing three critical temporal dimensions: current situational awareness, short-term forecasting, and strategic response. The research methodology is based on a systemic analysis of the current level of digitalization within crisis infrastructure, identifying critical shortcomings in the interoperability and timeliness of data sources within the Slovak Republic. The specific methodological procedure includes the Extraction, Transformation, and Loading of geographic data into advanced Geographic Information Systems. Within this environment, spatial multi-criteria decision-making algorithms and statistical methods are applied to calculate territory vulnerability indices. This process enables the transformation of static legislative frameworks into dynamic predictive models of extraordinary event impacts.

Furthermore, the article details the integration of this scientific knowledge into the "Crisis Management" study program at the Faculty of Security Engineering, University of Žilina. Through close synergy with practical application, students in core subjects—such as Modern Information Technologies, Risk Management, and International Crisis Management, transition from passive map reading to the active design of data architectures and scenario simulations. A significant component of this contribution is a comparative analysis of current educational standards versus future requirements, shifting the focus from rote memorization of static legislation to dynamic predictive modeling. The pedagogical goal is to enable students to precisely distinguish between threat and vulnerability: while a threat is the phenomenon itself (e.g., a flood), vulnerability refers to the presence of sensitive elements (e.g., nursing homes or hospitals) within an exposed area. The model requires students to justify their conclusions with precision; they learn that a "safe area" is defined not merely by the absence of a threat, but by the combination of low probability and low object vulnerability. Ultimately, students acquire technical competencies in digital infrastructure and spatial analysis. The article emphasizes that the outcome is not merely software proficiency, but the ability to logically bridge technical data with crisis planning. This makes the model a universal tool applicable throughout Europe. The resulting integrated data and pedagogical model is a modular educational tool that simulates real-world decision-making through practical laboratory exercises, fundamentally improving the training of future experts for the Slovak state administration and the broader European landscape.

Keywords:

Education, Innovation, Educational process, Crisis Management, Data Modeling.