Spatial planning of urban communities via deep reinforcement learning.

Effective spatial planning of urban communities plays a critical role in the sustainable development of cities. Despite the convenience brought by geographic information systems and computer-aided design, determining the layout of land use and roads still heavily relies on human experts. Here we propose an artificial intelligence urban-planning model to generate spatial plans for urban communities. To overcome the difficulty of diverse and irregular urban geography, we construct a graph to describe the topology of cities in arbitrary forms and formulate urban planning as a sequential decision-making problem on the graph. To tackle the challenge of the vast solution space, we develop a reinforcement learning model based on graph neural networks. Experiments on both synthetic and real-world communities demonstrate that our computational model outperforms plans designed by human experts in objective metrics and that it can generate spatial plans responding to different circumstances and needs. We also propose a human-artificial intelligence collaborative workflow of urban planning, in which human designers can substantially benefit from our model to be more productive, generating more efficient spatial plans with much less time. Our method demonstrates the great potential of computational urban planning and paves the way for more explorations in leveraging computational methodologies to solve challenging real-world problems in urban science.

Healthy cities initiative in China: Progress, challenges, and the way forward.

China implemented the first phase of its National Healthy Cities pilot program from 2016-20. Along with related urban health governmental initiatives, the program has helped put health on the agenda of local governments while raising public awareness. Healthy City actions taken at the municipal scale also prepared cities to deal with the COVID-19 pandemic. However, after intermittent trials spanning the past two decades, the Healthy Cities initiative in China has reached a crucial juncture. It risks becoming inconsequential given its overlap with other health promotion efforts, changing public health priorities in response to the pandemic, and the partial adoption of the Healthy Cities approach advanced by the World Health Organization (WHO). We recommend aligning the Healthy Cities initiative in China with strategic national and global level agendas such as Healthy China 2030 and the Sustainable Development Goals (SDGs) by providing an integrative governance framework to facilitate a coherent intersectoral program to systemically improve population health. Achieving this alignment will require leveraging the full spectrum of best practices in Healthy Cities actions and expanding assessment efforts. Funding: Tsinghua-Toyota Joint Research Fund "Healthy city systems for smart cities" program.

Carbon savings resulting from the cooling effect of green areas: a case study in Beijing.

Green areas cool the climate of a city, reduce the energy consumption caused by the urban heat island (UHI) effect, and bring along carbon savings. However, the calculation of carbon savings due to the cooling effect of green areas is still not well understood. We have used a Landsat Enhanced Thematic Mapper Plus (ETM+) image of Beijing, to identify the cooled areas, compute the possible energy used to maintain the temperature differences between cooled areas and their surrounding heated areas, and calculate the carbon savings owing to the avoidance of energy use. Results show that a total amount of 14315.37 tons carbon savings was achieved in the study area and the amount was related to the biomass, the size and the shape of green areas. These results demonstrate the importance of carbon savings resulting from green areas' cooling effect.