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

doi:10.11947/j.AGCS.2025.20240279

Abstract

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

CLC Number:

P208

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.

Figures/Tables 17

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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