Objective This research aims to leverage the advances of computer algorithms to provide scientific analytical methods for spatial simulation and offer a more objective approach to reflect the natural operational laws of interdependencies between elements in the real physical world for landscape design. Multi-source data characterized by high-density distribution can reflect urban features and spatial patterns at a macro scale. This research employs agent-based models (ABM) to simulate spatial information and explore the spatial function distribution of planned sites under urban spatial function penetration, thereby guiding the structural morphology of scheduled sites.
Methods This research adopts a spatial network analysis design method using slime mold intelligence based on mapping point of interest (POI) data. Firstly, POI data and urban basic information data within the Yizhuang New City area of Beijing are obtained through the application programming interface (API) of Gaode Map to construct the spatial network foundation of the research area. The POI data are grouped into eight types: data on residential areas, hotel accommodations, road traffic, education and culture, medical care, business offices, commercial services, and green space tourism. Secondly, a slime mold agent model is constructed using the NetLogo platform, with the behavioral rules of cytoplasm agents, nuclei agents, and patch agents being defined to simulate the growth behavior of slime molds. By adjusting model parameters, the foraging paths of slime molds in the designed site are simulated, and their spatial function distribution is analyzed. The simulation environment is based on multi-source data analysis and involves setting particle growth points, food locations, and environmental obstacles. Six particle growth points represent the main entry points to the designed site, while food locations are placed at significant building entrances and preserved landscape nodes. The environmental obstacles consist of planned buildings and river channels. Finally, the model incorporates these elements (growth points, food locations, obstacles) to analyze and optimize spatial functions.
Results The research results indicate that the design method for spatial network analysis using slime mold intelligence based on POI data mapping is highly feasible in the landscape design of small and medium-sized sites. The ABM simulation of slime mold growth behavior can effectively reflect the infiltration of surrounding functional information into the designed site, forming a spatial function zoning map with path texture. The path simulation results provide valuable insights into path connections, functional layouts, and crowd-gathering patterns, closely aligning with actual site usage requirements. Specifically, the simulation demonstrates how different functional areas and their respective uses can be distributed across the designed site, allowing for a more informed and responsive design process. Additionally, the simulation results show that the slime mold agent model can rapidly generate foraging networks in complex environments due to its efficient organization and structural resilience, as well as optimize path selection according to behavioral objectives.
Conclusion ABM analysis, supported by advanced algorithms, considers the basic spatial information of the site and simulates individual behavior patterns using distributed computation rules, thereby providing a new paradigm for landscape design thinking. Integrating multi-source data allows ABM to simulate urban spaces' self-organizing behavior and enhance the matching between designed and actual site conditions. 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 the paradigm shift in urban spatial planning, offering new theoretical and practical support for the design of urban park systems. Applying slime mold intelligence through ABM can provide a robust framework for analyzing and designing urban spaces. This approach allows for the simulation of complex spatial interactions and the emergence of spatial patterns that reflect the dynamicity of urban environments, which may, by leveraging multi-source data, offer a comprehensive tool for landscape architects to design functional, responsive, and sustainable urban spaces. The findings underscore the potential of ABM to transform urban landscape design by providing a detailed and data-driven understanding of spatial dynamics and functional distribution, thus helping make more informed and effective design decisions. This expanded approach underscores the feasibility of using slime mold intelligence in landscape design, demonstrating its potential to reshape traditional design methodologies. Integrating POI data with ABM can provide a high level of accuracy in reflecting real-world conditions and enhance the adaptability and sustainability of landscape design. As cities continue to evolve and expand, the methodologies explored in this research will become increasingly relevant, providing landscape architects with the tools necessary to create urban environments that are both functional and harmonious with their natural and social contexts. The research paves the way for future research and application in urban planning, emphasizing the importance of data-driven, adaptive design processes in creating resilient and livable urban spaces.
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