模型生成的城市扩张场景合理性的图灵检验

A. Hagen‐Zanker, Jingyan Yu, Susan Hughes, N. Santitissadeekorn
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引用次数: 2

摘要

预计未来城市扩张的情景是合理的:它们必须多样化以反映未来的不确定性,但在描述城市扩张过程时是现实的。我们研究了一种新的数据驱动模拟方法得出的场景的合理性。在一个类似图灵的测试中,专家们完成了一个测试,要求他们在三个模型生成的场景中识别显示真实城市扩张的地图。在从紧凑型到分散型的各种扩张模式中,专家们没有显著的能力来识别真正的模式。研究结果支持了这样一种假设,即所调查的情景是可信的,因此,对估计的动态模型进行聚类分析是产生未来城市扩张情景的可行方法。
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A Turing Test of the Plausibility of Model-Generated Urban Expansion Scenarios
Scenarios of future urban expansion are expected to be plausible: they must be diverse to reflect future uncertainty, yet realistic in their depiction of urban expansion processes. We investigated the plausibility of scenarios derived from a novel data-driven simulation approach. In a Turing-like test, experts completed a quiz in which they were asked to identify the map showing true urban expansion amidst three model-generated scenarios. Across diverse expansion patterns, ranging from compact to dispersed, the experts had no significant ability to identify the true pattern. The results support the hypothesis that the investigated scenarios are plausible and hence that cluster analysis of estimated dynamic models is a viable method for producing scenarios of future urban expansion.
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