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

doi:10.11947/j.AGCS.2025.20240369

Abstract

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

CLC Number:

P208

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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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