Yang Tianren, Jin Ying, Fang Zhou

Keywords： Urban Systems; Artificial Intelligence; Multi-source Data; Planning Evaluation; Spatial Planning; Urban Fabric Generation

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.

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