Natural disaster evacuation modeling: the dichotomy of fear of crime and social influence
Natural disaster evacuation modeling: the dichotomy of fear of crime and social influence
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DOI：
10.1007/s13278-021-00839-8
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发表时间：
2021-11
影响因子：
2.8
通讯作者：
C. Kuhlman;A. Marathe;A. Vullikanti;Nafisa Halim;Pallab Mozumder
中科院分区：
文献类型：
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作者：
C. Kuhlman;A. Marathe;A. Vullikanti;Nafisa Halim;Pallab Mozumder
邻里效应对家庭的疏散决策有重要影响。由于同伴的影响，邻居的撤离可以激励一个家庭撤离。矛盾的是，如果很多邻居撤离，那么由于害怕犯罪和抢劫，个人或家庭决定撤离的可能性就会降低。这种行为不能用传染在网络上传播的标准模型来捕捉，例如阈值、独立级联和线性阈值模型。在这里，我们提出了一个新的基于阈值的图动力系统模型，2mode-threshold，它捕捉到了这种二分法。我们从理论上研究了不同网络中二模阈值的动态特性，发现了与标准阈值模型的显著差异。我们构建并描述了弗吉尼亚州弗吉尼亚海滩的小世界网络，其中节点是城市中的地理位置家庭（家庭），边缘是家庭对之间的互动。我们通过在这些小世界网络上的基于代理的模拟来证明我们的行为模型的实用性。我们用它来了解该地区的疏散率，并评估建模参数对疏散决策动力学的影响。具体来说，我们量化了(1)网络生成参数的影响，(2)社会网络生成过程中的随机性，(3)模型类型（2mode-thresholdvs）的影响。标准阈值模型)、(4)2模型阈值模型参数、(5)和初始条件对计算的疏散率及其变异性的影响。一个说明性的示例结果表明，不存在洗劫效应可以将疏散率高估多达50%。
Neighborhood effects have an important role in evacuation decision-making by a family. Owing to peer influence, neighbors evacuating can motivate a family to evacuate. Paradoxically, if a lot of neighbors evacuate, then the likelihood of an individual or family deciding to evacuate decreases, for fear of crime and looting. Such behavior cannot be captured using standard models of contagion spread on networks, e.g., threshold, independent cascade, and linear threshold models. Here, we propose a new threshold-based graph dynamical system model,2mode-threshold, which captures this dichotomy. We study theoretically the dynamical properties of2mode-thresholdin different networks, and find significant differences from a standard threshold model. We build and characterize small world networks of Virginia Beach, VA, where nodes are geolocated families (households) in the city and edges are interactions between pairs of families. We demonstrate the utility of our behavioral model through agent-based simulations on these small world networks. We use it to understand evacuation rates in this region, and to evaluate the effects of modeling parameters on evacuation decision dynamics. Specifically, we quantify the effects of (1) network generation parameters, (2) stochasticity in the social network generation process, (3) model types (2mode-thresholdvs. standard threshold models), (4)2mode-thresholdmodel parameters, (5) and initial conditions, on computed evacuation rates and their variability. An illustrative example result shows that the absence of looting effect can overpredict evacuation rates by as much as 50%.