顾及空间邻域效应的多元地理要素因果模式挖掘方法

doi:10.11947/j.AGCS.2025.20240369

测绘学报 ›› 2025, Vol. 54 ›› Issue (5): 937-949.doi: 10.11947/j.AGCS.2025.20240369

顾及空间邻域效应的多元地理要素因果模式挖掘方法

石岩¹^,²^,³(), 李诗逸¹, 王达¹(), 邓敏¹^,³, 汤仲安³^,⁴

^1.中南大学地球科学与信息物理学院，湖南　长沙　410083
^2.自然资源部城市国土资源监测与仿真重点实验室，广东　深圳　518034
^3.湖南省地理空间信息工程技术研究中心，湖南　长沙　410018
^4.湖南省第三测绘院，湖南　长沙　410018

收稿日期:2024-09-05 修回日期:2025-04-11 出版日期:2025-06-23 发布日期:2025-06-23
通讯作者: 王达 E-mail:csu＿shiy@csu.edu.cn;215001023@csu.edu.cn
作者简介:石岩（1988—），男，博士，教授，研究方向为地理大数据挖掘及其在国土空间规划、城市公共安全、智慧交通管控、地质灾害预警等领域的应用。　E-mail：csu＿shiy@csu.edu.cn
基金资助:
国家自然科学基金(42371477);自然资源部城市国土资源监测与仿真重点实验室开放基金(KF-2023-08-03);国家重点研发计划(2021YFB3900904);湖南省自然科学基金(2022JJ20059);湖南省科技创新计划(2023RC3032);中南大学创新驱动计划(2023CXQD013);中南大学前沿交叉研究项目(2023QYJC002);中南大学研究生自主探索创新项目(2025ZZTS0071)

Methodology for mining causal patterns of multiple geographic elements by considering spatial neighborhood effects

Yan SHI¹^,²^,³(), Shiyi LI¹, Da WANG¹(), Min DENG¹^,³, Zhong'an TANG³^,⁴

^1.School of Geosciences and Info-physics, Central South University, Changsha 410083, China
^2.Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China
^3.Hunan Geospatial Information Engineering and Technology Research Center, Changsha 410018, China
^4.Hunan Third Institute of Surveying and Mapping, Changsha 410018, China

Received:2024-09-05 Revised:2025-04-11 Online:2025-06-23 Published:2025-06-23
Contact: Da WANG E-mail:csu＿shiy@csu.edu.cn;215001023@csu.edu.cn
About author:SHI Yan (1988—), male, PhD, professor, majors in geographical big data mining and its application of territorial spatial planning, urban public security, intelligent traffic management, geological disaster warning and so on.　E-mail: csu_shiy@csu.edu.cn
Supported by:
The National Natural Science Foundation of China(42371477);The Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources(KF-2023-08-03);The National Key Research and Development Program of China(2021YFB3900904);Hunan Provincial Natural Science Foundation of China(2022JJ20059);The Science and Technology Innovation Program of Hunan Province(2023RC3032);Central South University Innovation-Driven Research Program(2023CXQD013);The Frontier Cross Research Project of Central South University(2023QYJC002);The Graduate Students Explore Innovative Programs of Central South University(2025ZZTS0071)

摘要/Abstract

摘要：

地理空间数据挖掘旨在深入揭示多元地理要素的复杂分布规则与时空演化趋势。当前研究大多基于空间相关性依赖假设，缺乏对深层次空间因果关系的剖析，混杂的伪相关关系导致挖掘结果有偏甚至错误。本文基于因果推断理论，考虑空间邻域效应在因果关系中的影响作用，提出了一种顾及空间邻域效应的多元地理要素因果模式挖掘方法。首先，基于空间聚类算法自动建立适应地理要素分布密度的事务集；然后，融合空间邻域效应与贝叶斯网络建模思想，构建多元地理要素空间因果有向图结构；最后，基于后门准则实施干预运算，实现多元地理要素间因果效应的定量计算。试验采用深圳市和上海市城市设施空间分布数据进行实例分析，与空间关联模式挖掘方法的对比结果表明，本文方法剔除了混杂变量引起的空间伪相关关系，能够有效地得到不同类型城市功能设施间的有向因果关系与因果作用强度，更准确地揭示城市功能设施的局部集聚效应，为城市空间优化布局提供更可信的决策支持。

关键词: 因果模式, 多元地理要素, 空间邻域, 因果效应

Abstract:

Geospatial data mining aims to deeply reveal the complex distribution rules and spatio-temporal evolution trends of multiple geographic elements. Current geospatial data mining studies were mostly based on the assumption of spatial correlation dependence, which lacked the analysis of underlying spatial causalities, so the mixed pseudo-correlations would lead to biased or even erroneous mining results. In this case, based on the causal inference theory, this study proposed a multivariate geographic element causal pattern mining method by considering the influence of spatial neighborhood effects on causal relationships. First, the transaction set adapted to the distribution density of geographic elements is automatically created using the spatial clustering algorithm. Then, the spatial causal directed graph structure of multiple geographic elements is constructed by integrating the spatial neighborhood effects and the Bayesian network modeling idea. Finally, the backdoor criterion is used to implement the intervention operation to realize the quantitative calculation of causal effects among multiple geographic elements. In the experiments, the spatial distribution data of urban facilities in Shenzhen and Shanghai are utilized for case studies. The comparison results with the spatial correlation pattern mining method show that the proposed method eliminates the spurious spatial correlations caused by confounding variables, and can effectively obtain the directed causal relationships and causal strength between different types of urban functional facilities, and reveal the local aggregation effects of urban functional facilities more accurately. The mined causal patterns have the potential of providing more credible decision supports for the optimization of urban space layout.

Key words: causal patterns, multiple geographic elements, spatial neighborhoods, causal effects

中图分类号:

P208

石岩, 李诗逸, 王达, 邓敏, 汤仲安. 顾及空间邻域效应的多元地理要素因果模式挖掘方法[J]. 测绘学报, 2025, 54(5): 937-949.

Yan SHI, Shiyi LI, Da WANG, Min DENG, Zhong'an TANG. Methodology for mining causal patterns of multiple geographic elements by considering spatial neighborhood effects[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(5): 937-949.

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链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2025.20240369

http://xb.chinasmp.com/CN/Y2025/V54/I5/937

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参考文献 43

[1]	刘耀林, 刘启亮, 邓敏, 等. 地理大数据挖掘研究进展与挑战[J]. 测绘学报, 2022, 51(7): 1544-1560. DOI . doi: 10.11947/j.AGCS.2022.20220068
	LIU Yaolin, LIU Qiliang, DENG Min, et al. Recent advance and challenge in geospatial big data mining[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7): 1544-1560. DOI . doi: 10.11947/j.AGCS.2022.20220068
[2]	邓敏, 蔡建南, 杨文涛, 等. 多模态地理大数据时空分析方法[J]. 地球信息科学学报, 2020, 22(1): 41-56.
	DENG Min, CAI Jiannan, YANG Wentao, et al. Spatio-temporal analysis methods for multi-modal geographic big data[J]. Journal of Geo-information Science, 2020, 22(1): 41-56.
[3]	SHEKHAR S, HUANG Yan. Discovering spatial co-location patterns: a summary of results[M]//Advances in spatial and temporal databases. Berlin: Springer, 2001: 236-256.
[4]	蔡建南, 刘启亮, 徐枫, 等. 多层次空间同位模式自适应挖掘方法[J]. 测绘学报, 2016, 45(4): 475-485. DOI . doi: 10.11947/j. AGCS.2016.20150337
	CAI Jiannan, LIU Qiliang, XU Feng, et al. An adaptive method for mining hierarchical spatial co-location patterns[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(4): 475-485. DOI . doi: 10.11947/j. AGCS.2016.20150337
[5]	CAI Jiannan, LIU Qiliang, DENG Min, et al. Adaptive detection of statistically significant regional spatial co-location patterns[J]. Computers, Environment and Urban Systems, 2018, 68: 53-63.
[6]	邓敏, 蔡建南, 何占军, 等. 地理空间关联模式挖掘的理论与方法[M]. 北京: 科学出版社, 2023.
	DENG Min, CAI Jiannan, HE Zhanjun, et al. Theory and method of mining geo-spatial association patterns[M]. Beijing: Science Press, 2023.
[7]	YU Wenhao, AI Tinghua, HE Yakun, et al. Spatial co-location pattern mining of facility points-of-interest improved by network neighborhood and distance decay effects[J]. International Journal of Geographical Information Science, 2017, 31(2): 280-296.
[8]	ANDRZEJEWSKI W, BOINSKI P. Efficient spatial co-location pattern mining on multiple GPUs[J]. Expert Systems with Applications, 2018, 93(3): 465-483.
[9]	CHEN Yimin, CHEN Xinyue, LIU Zihui, et al. Understanding the spatial organization of urban functions based on co-location patterns mining: a comparative analysis for 25 Chinese cities[J]. Cities, 2020, 97: 102563.
[10]	HE Zhanjun, DENG Min, XIE Zhong, et al. Discovering the joint influence of urban facilities on crime occurrence using spatial co-location pattern mining[J]. Cities, 2020, 99: 102612.
[11]	LI Ling, CHENG Jianquan, BANNISTER J, et al. Geographically and temporally weighted co-location quotient: an analysis of spatiotemporal crime patterns in greater Manchester[J]. International Journal of Geographical Information Science, 2022, 36(5): 918-942.
[12]	ZHI Guoqing, MENG Bin, LIN Hui, et al. Spatial co-location patterns between early COVID-19 risk and urban facilities: a case study of Wuhan, China[J]. Frontiers in Public Health, 2024, 11: 1293888.
[13]	BARREDO ARRIETA A, DÍAZ-RODRÍGUEZ N, DEL SER J, et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI[J]. Information Fusion, 2020, 58: 82-115.
[14]	WU Chenwang, WANG Xiting, LIAN Defu, et al. A causality inspired framework for model interpretation[C]//Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Long Beach: ACM Press, 2023: 2731-2741.
[15]	PEARL J. Causal inference in statistics: an overview[J]. Statistics Surveys, 2009, 3: 96-146.
[16]	苏建宾, 陈都鑫, 郑东海, 等. 追索为什么?地球系统科学中的因果推理[J]. 中国科学：地球科学, 2023, 53(10): 2199-2216.
	SU Jianbin, CHEN Dduxin, ZHENG Donghai et al. The insight of why: causal inference in Earth system science[J]. Scientia Sinica (Terrae), 2023, 53(10): 2199-2216.
[17]	SPIRTES P, GLYMOUR C. An algorithm for fast recovery of sparse causal graphs[J]. Social Science Computer Review, 1991, 9(1): 62-72.
[18]	SHIMIZU S. Lingam: non-Gaussian methods for estimating causal structures[J]. Behaviormetrika, 2014, 41(1): 65-98.
[19]	ROSENBAUM P R, RUBIN D B. The central role of the propensity score in observational studies for causal effects[J]. Biometrika, 1983, 70(1): 41-55.
[20]	ABADIE A. Semiparametric difference-in-differences estimators[J]. The Review of Economic Studies, 2005, 72(1): 1-19.
[21]	PEARL J. Causality: models, reasoning, and inference[M]. Cambridge: Cambridge University Press, 2000.
[22]	PEARL J. Causal diagrams for empirical research[J]. Biometrika, 1995, 82(4): 669-688.
[23]	RUBIN D B. Estimating causal effects of treatments in randomized and nonrandomized studies[J]. Journal of Educational Psychology, 1974, 66(5): 688-701.
[24]	SPLAWA-NEYMAN J, DABROWSKA D M, SPEED T P. On the application of probability theory to agricultural experiments. essay on principles. section 9[J]. Statistical Science, 1990, 5(4): 465-472.
[25]	PEARL J. Causality[M]. Cambridge: Cambridge University Press, 2009.
[26]	PEARL J, GLYMOUR M, JEWELL N P. Causal inference in statistics: a primer[M]. John Wiley & Sons, 2016.
[27]	GRANGER C W. Investigating causal relations by econometric models and cross-spectral methods[J]. Econometrica, 1969, 37(3): 424-438.
[28]	GRANGER C W. Testing for causality: a personal viewpoint[J]. Journal of Economic Dynamics and Control, 1980, 2(1): 329-352.
[29]	SUGIHARA G, MAY R, YE Hao, et al. Detecting causality in complex ecosystems[J]. Science, 2012, 338(6106): 496-500.
[30]	XIAO Zhixuan, LI Chengyi, PAN Shihua, et al. Exploring the spatial impact of multisource data on urban vitality: a causal machine learning method[J]. Wireless Communications and Mobile Computing, 2022, 2022(1): 5263376.
[31]	CHEN Yimin, CHEN Jing, ZHAO Shuai, et al. Inferring the heterogeneous effect of urban land use on building height with causal machine learning[J]. GIScience & Remote Sensing, 2024, 61(1): 2321695.
[32]	CHEN Ziyue, XIE Xiaoming, CAI Jun, et al. Understanding meteorological influences on PM_2.5 concentrations across China: a temporal and spatial perspective[J]. Atmospheric Chemistry & Physics, 2018, 18(8): 5343-5358.
[33]	GAO Bingbo, YANG Jianyu, CHEN Ziyue, et al. Causal inference from cross-sectional earth system data with geographical convergent cross mapping[J]. Nature Communications, 2023, 14(1): 5875.
[34]	ANKERST M, BREUING M M, KRIEGEL H P, et al. OPTICS: ordering points to identify the clustering structure[J]. ACM SIGMOD Record, 1999, 28(2): 49-60.
[35]	AKBARI K, WINTER S, TOMKO M. Spatial causality: a systematic review on spatial causal inference[J]. Geographical Analysis, 2023, 55(1): 56-89.
[36]	DIGITALE J C, MARTIN J N, GLYMOUR M M. Tutorial on directed acyclic graphs[J]. Journal of Clinical Epidemiology, 2022, 142: 264-267.
[37]	TENNANT P W G, MURRAY E J, ARNOLD K F, et al. Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations[J]. International Journal of Epidemiology, 2021, 50(2): 620-632.
[38]	PETERS J, JANZING D, SCHÖLKOPF B. Elements of causal inference: foundations and learning algorithms[M]. Cambridge: The MIT Press, 2017.
[39]	ZHENG Xun, DAN Chen, ARAGAM B, et al. Learning sparse nonparametric dags[C]//Proceedings of 2020 International Conference on Artificial Intelligence and Statistics. [S.l.]: IEEE, 2020: 3414-3425.
[40]	王靖涵, 艾廷华, 吴昊, 等. 基于图结构的空间同位模式挖掘[J]. 测绘学报, 2024, 53(4): 724-735. DOI . doi: 10.11947/j. AGCS.2024.20230012
	WANG Jinghan, AI Tinghua, WU Hao, et al. Spatial co-location pattern mining based on graph structure[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(4): 724-735. DOI . doi: 10.11947/j. AGCS.2024.20230012
[41]	YU Wenhao. Spatial co-location pattern mining for location-based services in road networks[J]. Expert Systems with Applications, 2016, 46(3): 324-335.
[42]	TRAN V, WANG Lizhen, CHEN Hongmei, et al. MCHT: a maximal clique and hash table-based maximal prevalent co-location pattern mining algorithm[J]. Expert Systems with Applications, 2021, 175: 114830.
[43]	卢雨蓉, 邓建锋, 韩贵锋, 等. 城市公园的多维可达性动态评估研究[J]. 中国园林, 2022, 38(5): 92-97.
	LU Yurong, DENG Jianfeng, HAN Guifeng, et al. Research on dynamic evaluation of multidimensional accessibility to urban park[J]. Chinese Landscape Architecture, 2022, 38(5): 92-97.

一级类别	二级类别
学校	学校（中类）
餐厅	中餐厅、快餐厅、外国餐厅
商超	商场、超级市场
酒店住宿	宾馆酒店、住宿服务相关
公交站点	公交车站、地铁站
娱乐场所	娱乐场所（中类）
运动场馆	运动场馆（中类）

POI类别	数量
学校	74 422
餐厅	7717
商超	7547
酒店住宿	6453
公交站点	4564
娱乐场所	4163
运动场馆	3393

POI类别	数量
学校	85 085
餐厅	17 017
商超	12 829
酒店住宿	11 076
公交站点	8860
娱乐场所	7727
运动场馆	7632

城市功能设施集聚因果关系	P（Y=1｜do（X=1））	P（Y=1｜do（X=0））	集聚因果效应
｛“商超”→“娱乐场所”｝	0.452 94	0.105 12	0.347 82
｛“商超”→“餐厅”｝	0.391 66	0.096 92	0.294 74
｛“娱乐场所”→“餐厅”｝	0.455 64	0.098 62	0.357 02
｛“学校”→“运动场馆”｝	0.330 69	0.167 54	0.163 15
｛“酒店住宿”→“餐厅”｝	0.402 28	0.159 99	0.242 29
｛“运动场馆”→“商超”｝	0.347 69	0.190 86	0.156 83
｛“运动场馆”→“餐厅”｝	0.304 39	0.131 00	0.173 39

城市功能设施集聚因果关系	P（Y=1｜do（X=1））	P（Y=1｜do（X=0））	集聚因果效应
｛“商超”→“娱乐场所”｝	0.265 02	0.124 44	0.140 58
｛“商超”→“餐厅”｝	0.326 75	0.083 29	0.243 46
｛“娱乐场所”→“酒店住宿”｝	0.160 70	0.048 08	0.112 62
｛“娱乐场所”→“餐厅”｝	0.377 73	0.112 05	0.265 68
｛“酒店住宿”→“餐厅”｝	0.333 50	0.151 08	0.182 42
｛“运动场馆”→“娱乐场所”｝	0.394 30	0.132 07	0.262 23
｛“运动场馆”→“酒店住宿”｝	0.204 80	0.048 65	0.156 15
｛“运动场馆”→“餐厅”｝	0.328 40	0.132 64	0.195 76

顾及空间邻域效应的多元地理要素因果模式挖掘方法

Methodology for mining causal patterns of multiple geographic elements by considering spatial neighborhood effects

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参考文献 43

相关文章 15

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因果关系	100	300	500	700	900
｛“商超”→“公交站点”｝	√	√	√	√	√
｛“商超”→“娱乐场所”｝	√	√	√
｛“商超”→“酒店住宿”｝		√	√	√	√
｛“商超”→“餐厅”｝	√	√	√	√	√
｛“商超”→“学校”｝	√	√		√	√
｛“商超”→“运动场馆”｝		√	√	√
｛“运动场馆”→“娱乐场所”｝	√	√	√
｛“运动场馆”→“餐厅”｝	√	√	√	√	√
｛“运动场馆”→“酒店住宿”｝	√	√	√	√	√
｛“运动场馆”→“学校”｝	√	√	√	√	√
｛“运动场馆”→“公交站点”｝	√	√	√	√
｛“酒店住宿”→“餐厅”｝	√	√	√	√	√
｛“酒店住宿”→“学校”｝		√
｛“娱乐场所”→“餐厅”｝	√	√	√	√	√
｛“娱乐场所”→“酒店住宿”｝	√	√	√	√	√
｛“娱乐场所”→“学校”｝	√	√		√	√
｛“娱乐场所”→“公交站点”｝	√	√	√	√
｛“公交站点”→“学校”｝		√		√	√
｛“公交站点”→“餐厅”｝	√	√	√	√	√
｛“学校”→“餐厅”｝	√	√	√	√	√
｛“学校”→“公交站点”｝	√		√
｛“学校”→“酒店住宿”｝			√	√	√
｛“公交站点”→“酒店住宿”｝			√	√	√

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