MetaCity: Data-driven sustainable development of complex cities.

Area of Science:

• Urban Planning
• Environmental Science
• Data Science

Background:

• Cities are complex systems with interconnected human and environmental elements.
• Urbanization presents opportunities but also challenges such as pollution, congestion, and inequality.
• Large-scale urban data offers potential for data-driven sustainable development solutions.

Purpose of the Study:

• To provide a comprehensive overview of data-driven urban sustainability practices.
• To conceptualize MetaCity, a framework for optimizing resource usage and allocation in cities.
• To explore data-driven technologies for addressing urban complexity and achieving sustainability goals.

Main Methods:

• Conceptualizing the MetaCity framework for data-driven urban sustainability.
• Decomposing urban sustainable goals (e.g., efficiency, resilience).
• Integrating urban problem discovery, system simulation, and decision-making within the framework.

Main Results:

• The MetaCity framework offers a cohesive approach to urban sustainability.
• Data-driven methods can address complexity in urban systems.
• Optimization of resource allocation is key to achieving sustainable development goals.

Conclusions:

• Data-driven approaches, integrated within frameworks like MetaCity, are crucial for sustainable urban development.
• Addressing urban complexity through data analysis and simulation enables better resource management.
• Achieving urban sustainability requires optimizing resource allocation for efficiency and resilience.