Continuous planning

Over the next decade, urban planning will shift from periodic, static analyses to continuous, iterative processes choreographed and augmented by interactive AI. Advanced machine learning models will enable responsive forecasting of urban planning challenges like transit accessibilty, with updates reflecting real-time shifts in supply and demand. Large language models will rapidly integrate new research into location choice models, accelerating our understanding of how people respond to urban changes. And AI-powered mapping tools with conversational interfaces will democratize geospatial analyses, making spatial planning more accessible to non-experts.

These technologies will transform urban planning from a series of infrequent, expert-driven exercises to a continuous cycle of analysis, engagement, and adaptation. This shift will be crucial for cities to respond effectively to rapidly changing conditions brought on by climate change and other complex challenges. However, the continuously-updated nature of information provided by these tools will challenge traditional planning processes and institutions to themselves become more adaptive.