Territorial and spatial-based modeling and simulation can provide valuable insights into various real-world systems, for example, by highlighting spatial patterns and uncovering non-linear or hidden relationships. These models use techniques such as cellular automata, agent-based modeling, geographic information systems, discrete event simulation, or spatial statistics, allowing researchers and stakeholders to undertake scenario analysis, impact assessment, and policy evaluation. Widespread access to autonomous unmanned air vehicles (UAVs) and earth satellite data allows these models to be fed with real-time, recent, and/or historical data, fostering the use of machine learning and data fusion methods to promote verification and validation, ensuring the accuracy and reliability of models, instilling confidence in decision-making scenarios like sustainable land management, disaster preparedness, or urban planning.

The simulation of land and/or water-based territorial areas offers a path to uncover complex interactions and relationships that shape our surroundings, capturing the behavior of various entities within a spatial context. Research in this field is vital for refining our knowledge of spatial systems and fostering risk mitigation and sustainable development. The goal of this Research Topic is to explore spatial and territorial simulation using advanced modeling techniques to understand the intricate dynamics of our environment and society. Through this Research Topic, we aim to showcase advancements in spatial simulation techniques across domains while inspiring further research and collaboration to address the challenges of our interconnected world.

This Research Topic aims to bring together academics, experts, and stakeholders to discuss the latest advances and challenges in the field of territorial and spatial-based simulation, seeking studies on (but not limited to) the following subjects:

• Research on land-use patterns, hydrological processes, and the interplay between territories, that can shed light on natural and built environments.
• Fire spread models for aiding in the development of wildfire prediction and effective management strategies.
• Coastal modeling strategies focusing on tsunami or storm disaster preparedness and impact assessment.
• The integration of spatial simulation with epidemiological models to enhance disease spread comprehension and inform on optimal disease control strategies.
• Simulation of natural disasters like hurricanes, earthquakes, and floods, promoting an understanding of their consequences and potential impacts of possible pre-disaster mitigations.
• Transportation models to optimize routes and schedules and reduce the associated carbon footprint.
• Spatially-aware social models for simulating the interactions, behaviors, and dynamics of individuals or social groups within a spatial context.
• In-silico simulation of UAV swarms oriented towards path optimization and collision avoidance in the context of higher-level spatial models.
• Data-driven spatial models, leveraging the prevalent access to data from UAVs, satellites, and other sources. Special interest will be given to studies using data for verification and validation purposes, data fusion, as well as using machine learning techniques for facilitating these goals.

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Territorial and Spatial-Based Simulation