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Regional Behavior Change Detection via Local Spatial Scan

机译:通过局部空间扫描进行区域行为变化检测

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摘要

Regional human behavior change refers to the scenarios that people in a certain area exhibit significant behavior deviation from their neighbors and their own past. This regional pattern usually reveals underlying changes of living environment, such as regional development, immigration, disease breakout; or uncovers demographic information from special events, for instance, start/end of school holidays, or religious holidays. Statistically significant behavior changes contain both temporal and spatial characteristics. In this paper, we propose local spatial scan statistic to identify regional behavior changes. To accelerate local search, spatial index is modified to provide data-driven clusters and scalable data access. Base on the restricted spatial index, we provide both exact and approximated approaches to compute local spatial scan. Simulation analysis and case studies on water bills of 15K households validated the efficiency and effectiveness of these approaches on identifying regional behavior changes.
机译:区域人类行为变化是指特定区域中的人们表现出与邻居和自己过去的行为明显偏离的场景。这种区域格局通常揭示了生活环境的根本变化,例如区域发展,移民,疾病爆发。或发现特殊事件(例如,学校假期的开始/结束或宗教假期)中的人口统计信息。具有统计意义的行为更改包含时间和空间特征。在本文中,我们提出了局部空间扫描统计量以识别区域行为变化。为了加速本地搜索,修改了空间索引以提供数据驱动的群集和可伸缩的数据访问。基于受限的空间索引,我们提供精确和近似的方法来计算局部空间扫描。通过对1.5万户家庭的水费进行的仿真分析和案例研究,验证了这些方法在识别区域行为变化方面的效率和有效性。

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