空间关联模式引导下的地质灾害空间因果关系发现方法

doi:10.11947/j.AGCS.2025.20240279

测绘学报 ›› 2025, Vol. 54 ›› Issue (3): 536-551.doi: 10.11947/j.AGCS.2025.20240279

空间关联模式引导下的地质灾害空间因果关系发现方法

陈炳蓉¹(), 谌恺祺¹, 邓敏¹(), 黄成²^,³, 刘青豪¹

^1.中南大学地球科学与信息物理学院，湖南　长沙　410083
^2.昆明理工大学国土资源工程学院，云南　昆明　650093
^3.云南省地质环境监测院，云南　昆明　650216

收稿日期:2024-07-08 出版日期:2025-04-11 发布日期:2025-04-11
通讯作者: 邓敏 E-mail:brchen@csu.edu.cn;dengmin@csu.edu.cn
作者简介:陈炳蓉（1997—），女，博士生，主要研究方向为时空数据挖掘、空间因果挖掘。　E-mail：brchen@csu.edu.cn
基金资助:
国家自然科学基金(42430110);云南省地质灾害时空大数据挖掘与辅助决策项目(YCZH2021-23);湖南省自然科学基金(2024JJ1009);中南大学研究生自主探索创新项目(2024ZZTS0367)

Method for discovering spatial causality in geological hazards guided by spatial association patterns

Bingrong CHEN¹(), Kaiqi CHEN¹, Min DENG¹(), Cheng HUANG²^,³, Qinghao LIU¹

^1.School of Geosciences and Info-physics, Central South University, Changsha 410083, China
^2.Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
^3.Yunnan Geological and Environmental Monitoring Institute, Kunming 650216, China

Received:2024-07-08 Online:2025-04-11 Published:2025-04-11
Contact: Min DENG E-mail:brchen@csu.edu.cn;dengmin@csu.edu.cn
About author:CHEN Bingrong (1997—), female, PhD candidate, majors in spatio-temporal big data mining and spatial causality mining.　E-mail: brchen@csu.edu.cn
Supported by:
The National Natural Science Foundation of China(42430110);Spatiotemporal Big Data Mining and Decision Support Project for Geological Hazards in Yunnan Province(YCZH2021-23);The Natural Science Foundation of Hunan Province(2024JJ1009);The Graduate Students Explore Innovative Programs of Central South University(2024ZZTS0367)

摘要/Abstract

摘要：

空间因果是认知地理现象规律的核心途径，能够揭示滑坡、泥石流等地质灾害的驱动要素与成因机制，为灾害溯源与应急管理提供重要技术支撑。现有因果发现方法非源生于地理学研究，未充分考虑空间位置约束，难以有效探测地理现象演化的空间因果关系。为此，本文从空间关联视角，提出空间因果发现方法Spatial-PC，包含因果关系和因果方向判断模型，即空间条件互信息与空间偏相关检验，有效探测空间位置约束下的空间因果关系。具体地，本文研究以云南省地质灾害为例，采用Apriori算法挖掘地质灾害空间关联模式，基于空间条件互信息筛选关联模式中的空间因果关系，利用空间偏相关检验判定因果关系的影响方向，形成空间因果图。研究成果有效揭示了云南省地质灾害的致灾机理，具有科学服务地质灾害精准防控的优势。

关键词: 空间关联模式, 空间因果, 空间条件互信息, 空间偏相关检验

Abstract:

Spatial causality is a core approach for understanding the patterns of geographic phenomena, as it can reveal the driving factors and causal mechanisms behind geological hazards like landslides and debris flows. This insight provides essential technical support for hazard tracing and emergency management. Existing causal discovery methods, not originally developed for geographic research, often overlook spatial location constraints, making them inadequate for effectively detecting spatial causal relationships in geographic phenomena. To address this gap, this paper introduces a Spatial-PC causal discovery method from a spatial association perspective, incorporating models for causality and direction determination using spatial conditional mutual information and spatial partial correlation tests. This method enables effective detection of spatial causality under location constraints. Specifically, we applied the approach to geological hazards in Yunnan province, China, utilizing the Apriori algorithm to identify spatial association patterns, then applying spatial conditional mutual information to filter out spatial causality, and spatial partial correlation tests to determine causal directions, ultimately constructing a spatial causality graph. The study's findings effectively elucidate the mechanisms driving geological hazards in Yunnan province, supporting precise hazard prevention and control.

Key words: spatial association patterns, spatial causality, spatial conditional mutual information, spatial partial correlation test

中图分类号:

P208

陈炳蓉, 谌恺祺, 邓敏, 黄成, 刘青豪. 空间关联模式引导下的地质灾害空间因果关系发现方法[J]. 测绘学报, 2025, 54(3): 536-551.

Bingrong CHEN, Kaiqi CHEN, Min DENG, Cheng HUANG, Qinghao LIU. Method for discovering spatial causality in geological hazards guided by spatial association patterns[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(3): 536-551.

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因素组合	空间因果关系	非空间因果关系	空间关联关系因素
二元	月累积最大降雨量大→滑坡	月累积最大降雨量大→滑坡	月累积最大降雨量大、滑坡
	地震密度高→崩塌	崩塌→地震密度高	坡度大、滑坡
	松散土体面积大→地面塌陷	松散土体面积大→地面塌陷	形变速率高、地面塌陷
	坡度大→滑坡	滑坡→地震密度高	峡谷区面积大、泥石流
	峡谷区面积大→泥石流	地震密度高→构造带密度高	构造带密度大、崩塌
三元	构造带密度高→地震密度高→崩塌	构造带密度大→地震密度高→滑坡	地震密度大、滑坡、坡度大
	月累积最大降雨量大→滑坡←坡度大	地震密度高→裸地面积大→崩塌	月累积最大降雨量大、泥石流、坡度大
	月累积最大降雨量大→崩塌←碳酸盐岩面积大	地震→月累积最大降雨量大→滑坡	地震密度大、地裂缝、裸地面积大
	水系密度高→崩塌←软硬岩互层面积大	坡度大→月累积最大降雨量大→滑坡	月累积最大降雨量大、坡度大、滑坡
	月累积最大降雨量大→泥石流←红壤面积大	月累积最大降雨量大→小丘陵面积大→泥石流	月累积最大降雨量大、地震密度高、滑坡

空间因果关系	提及率/（%）	调查报告来源
坡度大→滑坡←月累积最大降雨量大	79.6	云南省禄劝县地质灾害详细调查报告（2020）^[38]
坡度较大→泥石流←月平均降雨量极大	78.3	云南省禄劝县地质灾害详细调查报告（2007）^[39]
构造带密度高→地震密度高→崩塌	75.2	云南省玉龙纳西族自治县地质灾害详细调查报告（2017）^[40]
月平均最大降雨量大→滑坡	72.8	云南省河口瑶族自治县地质灾害详细调查报告（2008）^[41]
地震→崩塌	71.4	云南省红河县地质灾害详细调查报告（2009）^[42]
峡谷区面积大→泥石流	68.4	哀牢山地区元江县地质灾害详细调查报告（2010）^[43]
坡度大→滑坡	65.7	云南省金平县地质灾害详细调查报告（2009）^[44]
月累积最大降雨量大→崩塌←碳酸盐岩面积大	59.4	云南省宣威市地质灾害详细调查报告（2015）^[45]
水系密度高→崩塌←软硬岩互层面积大	50.6	云南哀牢山地区地质灾害详细调查报告（2011）^[46]

方法	TOP-2要素组合	TOP-3要素组合	TOP-4要素组合
支持向量机	54.6	57.2	78.1
地理探测器	46.2	54.4	65.2
地理加权回归	51.5	49.3	72.7
本文方法	56.7	62.5	81.6

要素	本文方法	支持向量机	地理探测器	地理加权回归
月累积最大降水量	0.547	0.572	0.843	0.589
月平均降水量	0.481	0.376	0.731	0.379
坡度	0.410	0.526	0.436	0.473
坡向	0.308	0.047	0.0259	0.192
土壤类型	0.196	0.122	0.070	0.072
高程	0.187	0.071	0.061	0.095
植被覆盖类型	0.153	0.064	0.027 8	0.144
断层构造带密度	0.228	0.076	0.033 9	0.183
地层岩性	0.167	0.102	0.058	0.117
地震点密度	0.152	0.098	0.132	0.247
地貌	0.094	0.056 2	0.052	0.062
土地利用类型	0.074	0.086 4	0.103	0.074
矿产密度	0.019 4	0.000 42	0.002 4	0.000 61
水系密度	0.005 7	0.000 8	0.002 8	0.064
路网密度	0.003 6	0.010 4	0.048	0.009 5

空间关联模式引导下的地质灾害空间因果关系发现方法

Method for discovering spatial causality in geological hazards guided by spatial association patterns

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