Understanding and predicting post-disaster human movements is critical for evaluating a population's vulnerability and resilience and developing plans for disaster evacuation, response and recovery. In this study, we attempt to analyze population movement by using mobile spatial statistics. In order to extract behavior patterns from the aggregated data, we use four different Latent Variable Analysis (LVA) methods - Independent Component Analysis (including FastICA and Spatial colored ICA), Non-negative Matrix Factorization (NMF), and Sparse Principal Component Analysis (SPCA) to analyze mobile statistics data of the disaster-affected area. The results indicate that each LVA methods has its pros and cons in extracting behavior patterns from the aggregated population. We conclude that, using multiple LVA methods and finding out the common patterns would be a robust way to understand and explain population dynamics. Finally, we argue that using mobile spatial statistics would be a feasible and practical option to estimate the dynamic change of human population after the occurrence of disasters.

了解和预测灾后人类活动对于评估人群的脆弱性和复原力以及制定灾难疏散，响应和恢复计划至关重要。在这项研究中，我们尝试通过使用移动空间统计来分析人口流动。为了从汇总数据中提取行为模式，我们使用了四种不同的潜在变量分析（LVA）方法-独立分量分析（包括FastICA和空间彩色ICA），非负矩阵分解（NMF）和稀疏主分量分析（ SPCA）以分析受灾地区的移动统计数据。结果表明，每种LVA方法在从总体中提取行为模式方面都有其优缺点。我们得出结论，使用多种LVA方法并找出常见模式将是理解和解释种群动态的可靠方法。最后，我们认为，使用移动空间统计数据将是估算灾难发生后人口动态变化的可行且实际的选择。