DYNAMIC MODEL OF URBAN RESILIENCE FOR SPATIAL POLICY DEVELOPMENT

Introduction. This article explores the concept of urban resilience as a key instrument of adaptive governance in the context of multidimensional threats and growing uncertainty. Emphasis is placed on the need to integrate the spatial dimension into resilience research, which remains insufficiently addressed in academic discourse. The aim of the study is to develop a methodological framework for a dynamic model of urban resilience that enables comprehensive analysis of the current state of the urban system and allows for the modification of its components in response to changes in the external environment. Methods. The research employs analytical methods, scenario modeling, and interdisciplinary approaches and models, including the multi-criteria decision analysis method and the DPSIR (Drivers–Pressures–State–Impact–Response) framework for comprehensive assessment of urban resilience. Results. The proposed dynamic model of urban resilience facilitates comprehensive analysis of the current state of the urban system and adaptation of its components to changes in the external environment. The model is based on the concepts of baseline, current, and prospective resilience, and utilizes mathematical modeling of diverse data to construct a composite index assessing urban system resilience. This index accounts for economic, social, governance, and environmental dimensions, and evaluates the capacity of urban systems to adapt to changes and respond to threats. An applied example is provided through the analysis of spatial indicators of resilience and the evaluation of baseline spatial resilience within the urban system’s “Greening and Public Spaces” spatial layer. Conclusions. The study reveals that the spatial characteristics of a city, in the context of building resilience, determine critical aspects such as infrastructure accessibility, efficiency of evacuation routes, responsiveness of emergency services, and the capacity of individual districts to function autonomously. Dynamic spatial modeling enables precise identification of risk zones, contextual adaptation of response scenarios, and comprehensive assessment of urban resilience as a dynamic and multidimensional process.