Predicting households’ residential mobility trajectories with geographically localized interpretable model-agnostic explanation (GLIME)
Predicting households’ residential mobility trajectories with geographically localized interpretable model-agnostic explanation (GLIME)
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DOI：
10.1080/13658816.2023.2264921
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发表时间：
2023-10
影响因子：
5.7
通讯作者：
Chanwoo Jin;Sohyun Park;Hui Jeong Ha;Jinhyung Lee;Junghwan Kim;Johan Hutchenreuther;Atsushi Nara
中科院分区：
文献类型：
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作者：
Chanwoo Jin;Sohyun Park;Hui Jeong Ha;Jinhyung Lee;Junghwan Kim;Johan Hutchenreuther;Atsushi Nara
摘要随着计算能力的提高和高分辨率空间数据的可获得性，使用人工智能(AI)进行人类流动性分析得到了极大的关注。然而，深度学习在社会科学和人文地理学中的应用仍然有限，主要是因为对模型可解释性的担忧。在这项研究中，我们使用了一种可解释的GeoAI方法，称为地理局部化可解释模型-不可知性解释(GLIME)，以探索人类在大空间和时间范围内的流动模式。具体地说，我们开发了一个双层长期短期记忆(LSTM)模型，能够预测2012年至2019年美国各地个人层面的住宅流动模式。我们利用GLIME提供地理视角，并在州一级解释深度神经网络。结果表明，GLIME能够在空间上显式地解释不同变量造成的局地影响。我们的发现强调了在住宅流动性动态中考虑路径依赖的重要性。虽然对复杂的人类空间决策过程的预测仍然存在挑战，但这项研究证明了深度神经网络和可解释的GeoAI在支持人类动力学理解方面的有效性。它为未来进一步微调的调查奠定了基础，有望对错综复杂的流动现象有更深刻的见解。
Abstract Human mobility analytics using artificial intelligence (AI) has gained significant attention with advancements in computational power and the availability of high-resolution spatial data. However, the application of deep learning in social sciences and human geography remains limited, primarily due to concerns with model explainability. In this study, we employ an explainable GeoAI approach called geographically localized interpretable model-agnostic explanation (GLIME) to explore human mobility patterns over large spatial and temporal extents. Specifically, we develop a two-layered long short-term memory (LSTM) model capable of predicting individual-level residential mobility patterns across the United States from 2012 to 2019. We leverage GLIME to provide geographical perspectives and interpret deep neural networks at the state level. The results reveal that GLIME enables spatially explicit interpretations of local impacts attributed to different variables. Our findings underscore the significance of considering path dependency in residential mobility dynamics. While the prediction of complex human spatial decision-making processes still presents challenges, this research demonstrates the utility of deep neural networks and explainable GeoAI to support human dynamics understanding. It sets the stage for further finely tuned investigations in the future, promising deep insights into intricate mobility phenomena.