In the heart of Beijing, a groundbreaking approach to urban landscape design is emerging, one that could revolutionize how we plan and build our cities. Led by Huizi Kong from the School of Landscape Architecture at Beijing Forestry University, this innovative research leverages the power of agent-based models (ABM) and multi-source data to create more functional, responsive, and sustainable urban spaces. The study, published in Fengjing Yuanlin, which translates to “Landscape and Forestry,” is set to reshape traditional design methodologies and offer new insights into urban planning.
At the core of this research is the use of slime mold intelligence, a biological phenomenon where slime molds create efficient networks to find food. Kong and her team have adapted this natural process into a spatial network analysis design method, using point of interest (POI) data to map out urban features and spatial patterns. “The slime mold agent model can rapidly generate foraging networks in complex environments,” Kong explains. “This efficient organization and structural resilience allow us to optimize path selection according to behavioral objectives, providing a robust framework for analyzing and designing urban spaces.”
The research focuses on the Yizhuang New City area of Beijing, where POI data and urban basic information were obtained through the API of Gaode Map. The data were categorized into eight types, including residential areas, hotel accommodations, road traffic, education and culture, medical care, business offices, commercial services, and green space tourism. Using the NetLogo platform, the team constructed a slime mold agent model to simulate the growth behavior of slime molds, adjusting model parameters to analyze the spatial function distribution of planned sites.
The results are striking. The simulation effectively reflects the infiltration of surrounding functional information into the designed site, forming a spatial function zoning map with path texture. This map provides valuable insights into path connections, functional layouts, and crowd-gathering patterns, closely aligning with actual site usage requirements. “The simulation demonstrates how different functional areas and their respective uses can be distributed across the designed site,” Kong notes, “allowing for a more informed and responsive design process.”
The implications for the energy sector are significant. As cities continue to grow and evolve, the demand for efficient and sustainable urban planning will only increase. This research offers a data-driven approach to designing urban spaces that are not only functional but also harmonious with their natural and social contexts. By integrating multi-source data and ABM, landscape architects can create urban environments that are resilient and livable, reducing energy consumption and enhancing the overall quality of life.
Moreover, this method provides a scientific analytical approach for landscape design from macro to micro scales, helping form spatial structure optimization schemes based on bottom-up design concepts. The combined application of ABM and multi-source data will contribute to a paradigm shift in urban spatial planning, offering new theoretical and practical support for the design of urban park systems.
As cities around the world grapple with the challenges of urbanization, this research paves the way for future developments in the field. It emphasizes the importance of data-driven, adaptive design processes in creating resilient and livable urban spaces. For the energy sector, this means opportunities to integrate sustainable practices into urban planning, reducing energy consumption and promoting a more sustainable future. The findings of this research, published in Fengjing Yuanlin, are set to inspire a new generation of landscape architects and urban planners, driving innovation and sustainability in the field.