Decision-making for Urban Planning and Design with Multi-source Data: Applications with Urban Systems Models and Artificial Intelligence

Abstract

With the emergence of multi-source urban data, urban systems models and artificial intelligence play a fundamental role in unraveling the relationships(e.g., nonlinear and causal) between different urban sectors. Using spatial equilibrium frameworks and generative adversarial networks as examples, this paper summarizes the theoretical foundations, strengths and weaknesses, and application scenarios of the two types of models. Urban systems models are suitable for large-scale applications, including planning evaluation(through counterfactual simulations) and spatial planning(through scenario forecast). Comparatively, artificial intelligence is more useful to support plan-making(based on examples and planning guidance) at a finer spatial level. A complementary use of the two models can derive quantifiable and explainable evidence for urban decision-making that considers the trade-offs across urban sectors and geographic units. This paper identifies the constrained use of multi-source data in analyzing past trends and advocates their potential usages to explain urban issues and optimize spatial strategies based on model development. 随着城市多源数据兴起,城市系统模型与人工智能技术成为建立数据间内在关联（如非线性与因果性关系）的核心基础。本文以两者的典型代表——'空间均衡模型'与'对抗生成网络'为例,总结梳理两者在城市研究与实践中的理论基础、优势与局限以及应用场景。城市系统模型更适用于支持大尺度的规划实施评估（通过反事实模拟）与空间规划编制（通过情景预测）,而人工智能技术则更适用于基于现状案例与规划指引的小尺度城市空间形态生成。基于两者的优势互补性,跨尺度的模型耦合可以为探索因地制宜、多维度共赢的城市决策提供可量化、可解释的科学依据。本文解释了多源数据在规划与设计中囿于现状描绘的局限与原因,挖掘了其在模型支撑下辨析城市问题与优化空间决策的应用潜力。