融合多类别地理要素交互关系的空间交叉异常探测研究

地理空间交叉异常是多类别地理要素关系的一种重要表现形式，亦是当前地理空间数据挖掘领域广受关注的一种重要地理知识。现有的交叉异常探测主要考虑地理要素之间几何关系与属性差异程度，而未充分融合地理要素之间复杂的交互作用（如：空间邻近效应），导致探测结果辅助理解特殊地理现象的能力不足。为此，本项目深入研究多类别地理要素间复杂交互作用关系及其对交叉异常判别的内在影响机制，并将地理要素交互作用关系定量建模于异常探测过程中，发展融合多类别地理要素交互关系的空间交叉异常探测方法，主要包括：（1）多类别地理要素交互作用关系的定量描述；（2）融合多类别地理要素交互关系的空间交叉异常探测；（3）地理空间交叉异常有效性综合评价。本项目研究成果一方面将延拓地理空间异常探测的研究体系，丰富地理空间数据挖掘的理论方法；另一方面亦将为智慧城市国土空间规划、城市设施智能管理等应用领域提供信息服务与决策支持。

Geospatial cross-outlier is an important manifestation of the relationship among multi-category geographic elements, and it is also an important geographic knowledge that has attracted much attention in the field of geospatial data mining. The existing cross-outlier detection mainly considers the degree of geometric relationship and attribute difference between geographic elements, but does not fully integrate the complex interaction between geographic elements (e.g. spatial neighbourhood effect), which leads to the inadequate ability of detection results to assist in understanding special geographic phenomena. To this end, this project focuses on the research of the complex interaction relationship between multi-category geographic elements and its intrinsic influence mechanism on cross-outlier discrimination, and quantitatively models the interaction relationship between geographic elements in the process of anomaly detection, and develops a spatial cross-outlier detection method that integrates the interaction relationship of multi-category geographic elements. It mainly includes three related issues: (1) quantitative description of the interaction relationship of multi-category geographic elements; (2) detection of spatial cross-outlier combining the interaction relationship of multi-category geographic elements; (3) comprehensive evaluation of the effectiveness of geospatial cross-outliers. On the one hand, the research results of this project will extend the research system of geospatial anomaly detection, and enrich the theoretical methods of geospatial data mining and knowledge discovery; on the other hand, it will also provide information service and decision support for the application fields of land and space planning and intelligent management of urban facilities in smart cities.