Human-machine synergy in spatial decision-making: a multi-scenario SDSS prototype for high-rise buildings

Serving as towering man-made footprints piercing the skyline, high-rise buildings shape urban spatial structure and symbolize the pace of development, especially amid China's rapid urbanization. They represent a key response to limited urban land supply, contributing significantly to the transformation of urban form and the refinement of spatial development strategies. While Spatial Decision Support Systems (SDSS) have been widely applied in urban planning, the rapid advancement of technology and the surge of heterogeneous spatial data have significantly increased the complexity of their design and implementation. This study proposes a prototype of an interdisciplinary SDSS framework that integrates data processing, machine learning, scenario simulation, and interactive visualization. Emphasizing human–machine synergy, the system facilitates informed and adaptive spatial decision-making. The prototype is tested through a case study on high-rise building planning in Wuhan, China, demonstrating its potential to support planners in interpreting emerging data sources and navigating complex urban planning challenges. Furthermore, the use of a no-code visual development platform in this study significantly enhances the construction of SDSS by offering intuitive interfaces, ease of use, and powerful capabilities for rapid integration and synchronous interaction of various models，highlighting its broad applicability and scalability in future urban planning practices. • SDSS integrates multi-platform tools and multi-source data to support human–machine synergy in spatial decision-making. • No-code visual programming facilitates efficient SDSS construction with interactive visualization and intuitive workflows. • Multi-scenario design addresses diverse planning needs in complex urban spatial decision-making contexts.