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About the workshop |
How can cities become spaces of symbiotic coexistence between humans and nature? How can we measure, assess, and visualize the dynamic interplay between natural and anthropogenic systems? What innovative techniques and tools can support cities as socio-ecological systems (SES), fostering sustainable urban development in a rapidly urbanizing world?
As urban areas expand and climate conditions fluctuate, cities face significant challenges in balancing human needs with environmental resilience.
ASSESS focuses on advancing spatial modeling and assessment of socio-ecological systems, with a particular emphasis on the role of green-blue infrastructures, and ecosystem and landscape services within urban environments.
The aim is to bring together researchers to present and discuss cutting-edge techniques, methodologies, and digital tools that enhance our understanding of cities as interconnected socio-ecological systems.
Therefore, ASSESS invites contributions showcasing data-driven approaches with applications in - among others:
- Geographic Information Systems (GIS)
- Multi-Criteria Decision Analysis (MCDA)
- Spatial Decision Support Systems (SDSSs), and
- Machine Learning,
to provide insights that can inform sustainable urban planning and policy-making. Key topics may include, for example, urban modeling, spatial data science, spatial econometrics, and other methodologies that evaluate the spatial impacts of urban growth on socio-ecological functions and assess the role of ecosystem services in enhancing urban resilience.
We encourage submissions that explore diverse approaches and tools that can deepen our understanding of Human-Nature Connectedness in urban environments and support cities on their journey towards sustainability and resilience.
- Spatial Assessment of Socio-Ecological Systems
- Green and Blue Infrastructures
- GIS
- MCDA
- SDSSs
- Urban Modeling
- Sustainable Urban Development
- Data-driven Approaches
- Spatial Data Analysis
- Ecosystem and Landscape Services
- Spatial Econometrics