Movement analysis is distinguished by an emphasis on understanding via observation and association. However, an important component of movement from the human and computer modeling perspective is the processes that bring about movement behavior in the first place. This article contextualizes the graphical causal modeling framework (for association, intervention, and counterfactual causal analysis) in GIScience, and more specifically within movement analysis studies. This is done by modeling the movement behavior of football players, applied to spatiotemporal data generated by an agent-based simulation. The movement dataset is thoroughly analyzed to infer the statistical associations among its variables, to estimate the effect of an intervention on some of those variables, and to answer a few counterfactual questions from the observations. We conclude that causal graphs (i.e., directed acyclic graphs), if implemented correctly, can assist analysts in infering causal relations from movement data. This research suggests the integration of causal graphs and agent-based paradigms as one solution for computational movement analysis.

中文翻译：

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空间和时间的反事实推理：在计算运动分析中集成图形因果模型

运动分析的特点是强调通过观察和联想进行理解。然而，从人类和计算机建模的角度来看，运动的一个重要组成部分是首先产生运动行为的过程。本文将图形因果建模框架（用于关联、干预和反事实因果分析）置于 GIScience 中，更具体地说是运动分析研究中。这是通过对足球运动员的运动行为进行建模来完成的，并将其应用于基于代理的模拟生成的时空数据。对运动数据集进行彻底分析，以推断其变量之间的统计关联，估计干预对其中一些变量的影响，并根据观察结果回答一些反事实问题。我们得出的结论是，因果图（即有向无环图）如果正确实施，可以帮助分析师从运动数据中推断因果关系。这项研究建议将因果图和基于代理的范式整合作为计算运动分析的一种解决方案。