流空间邻近关系约束下的流行病分布空间异常探测方法

doi:10.11947/j.AGCS.2021.20200350

测绘学报 ›› 2021, Vol. 50 ›› Issue (6): 777-788.doi: 10.11947/j.AGCS.2021.20200350

流空间邻近关系约束下的流行病分布空间异常探测方法

石岩, 王达, 陈袁芳, 陈炳蓉, 赵冰冰, 邓敏

中南大学地球科学与信息物理学院, 湖南长沙 410083

收稿日期:2020-07-27 修回日期:2020-12-07 发布日期:2021-06-28
通讯作者: 陈袁芳 E-mail:yuanfang_chen@csu.edu.cn
作者简介:石岩(1988—),男,博士,副教授,研究方向为时空数据挖掘分析及其应用。E-mail:csu_shiy@csu.edu.cn
基金资助:
国家重点研发计划（2018YFB1004603）；国家自然科学基金（42071452；41730105）；湖南省自然科学基金面上项目（2020JJ4696）；湖南省重点研发计划（2018SK2052）；空间数据挖掘与信息共享教育部重点实验室（福州大学）开放基金（2019LSDMIS05）

An anomaly detection approach from spatio distributions of epidemic based on adjacency constraints in flow space

SHI Yan, WANG Da, CHEN Yuanfang, CHEN Bingrong, ZHAO Bingbing, DENG Min

School of Geosciences and Info-physics, Central South University, Changsha 410083, China

Received:2020-07-27 Revised:2020-12-07 Published:2021-06-28
Supported by:
The National Key Research and Development of China (No. 2018YFB1004603)；The National Natural Science Foundation of China (Nos. 42071452；41730105)；The Natural Science Foundation of Hunan Province (No. 2020JJ4696)；The Key Research and Development of Hunan Province (No. 2018SK2052)；The Open Fund of Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education (Fuzhou University) (No. 2019LSDMIS05)

摘要/Abstract

摘要： 针对现有流行病空间异常探测方法在全面探测多因素导致的潜在空间异常方面的局限性，本文提出一种流空间邻近约束关系下的流行病分布空间异常探测方法。首先，基于地理探测器识别与传播中心人群流出强度因素具有显著关联关系的疫情专题属性；然后，基于流空间邻近关系度量自适应构建流空间权重矩阵；最后，构造疫情属性空间局部变化梯度变量刻画空间单元疫情态势特征，提出改进的全局和局部莫兰指数（Moran’s I）实现流空间疫情分布模式的统计判别与局部空间异常区域探测。新型冠状病毒肺炎（COVID-19）疫情的实例，验证了本文方法相比现有欧氏空间异常探测方法，能够有效识别疫情发展过程中除人群跨区域流动之外的多类潜在因素导致的疫情分布空间异常区域，有助于支持对疫情分阶段的分区分级精准防控。

关键词: 流行病, 流空间邻近, 属性局部变化梯度, 空间异常区域

Abstract: In view of the limitations of existing methods for detecting potential epidemic spatial anomalies caused by multiple driving factors, this paper proposes a spatial anomaly detection approach for epidemic distributions constrained by crowd flow similarities. Firstly, those epidemic attributes that are significantly associated with crowd outflow intensity from the spread center are identified using the geographic detector. Then, considering all pairs of spatial units, a spatial weight matrix is adaptively constructed by measuring the similarity of crowd outflow intensities from the spread center. Finally, each spatial unit is characterized using the local variation gradient of epidemic attribute values, based on which both global and local Moran’s I are modified to statistically discriminate the distribution patterns and detect local anomalous regions in flow space. Through performing comparative experiments on the spatio-temporal sequence of COVID-19, it illustrates that the proposed method can effectively detect the spatial anomalies caused by a variety of multiple potential factors. These findings can support the targeted epidemic prevention and control in different stages.

Key words: epidemic, flow space, local variation gradient, spatial anomaly regions

中图分类号:

P208

石岩, 王达, 陈袁芳, 陈炳蓉, 赵冰冰, 邓敏. 流空间邻近关系约束下的流行病分布空间异常探测方法[J]. 测绘学报, 2021, 50(6): 777-788.

SHI Yan, WANG Da, CHEN Yuanfang, CHEN Bingrong, ZHAO Bingbing, DENG Min. An anomaly detection approach from spatio distributions of epidemic based on adjacency constraints in flow space[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(6): 777-788.

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流空间邻近关系约束下的流行病分布空间异常探测方法

An anomaly detection approach from spatio distributions of epidemic based on adjacency constraints in flow space

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