【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。更多还原

【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.更多还原