空间O-D流模式发现与机理研究——以中国省际人口迁移流为例

空间O-D流是空间网络系统中各个节点（O/D）之间相互作用的结果，以人流、物流或信息流等有形或无形的方式存在，并在地域上呈现一定的集聚或扩散模式。传统的空间相互作用模型（如重力模型）以经典统计学为基础，侧重于表达节点要素特征和距离衰减效应，往往忽视了节点间以及O-D作用流之间的相互依赖关系，无法表达周边环境对作用流的影响，难以解释网络系统的复杂结构和功能。本项研究基于地理学第一定律，围绕空间网络系统中各节点、节点环境和节点之间作用流等诸要素之间的相互依赖关系，以网络自相关为基础，探索区域相互作用过程中O-D作用流的空间模式特征，并通过对传统模型的改造，构建能够表征区域溢出效应的空间O-D模型，定量分析空间模式形成的内在机理。本项研究通过对中国省际人口迁移流的分析与建模，以实例形式表明空间O-D流模式发现在技术与方法上的可行性，将在一定程度上深化GIS空间分析和空间数据挖掘等领域的相关研究。

Spatial origin-destination flows, whatever tangible entities such as persons and commodities or intangible ones such as capital, information or knowledge, take on some clustering or dispersal patterns across geographic space. The traditional spatial interaction modeling such as gravity model focuses on the structural characteristics of nodes (origins or destinations) and the distance decay effects between them, which ignores the spatio-temporal dependence among nodes and flows due to its classic independent assumption. Therefore, it is difficult to provide a sound interpretation of the complex spatial structure and dynamics of system evolution. Based on the first law of geography, this project attempts to explore the spatial patterns of O-D flows by using the network autocorrelation statistics and other exploratory data analysis methods.Then the appropriate spatial O-D models will be constructed by testing and incorporating spatial or temporal autocorrelation components into the traditional O-D modles. The feasibility of these techniques and methods in flow data mining and knowledge discovery will be demonstrated by a case study of Chinese interprovincial migration flows. This project in spatial O-D flows modeling and analysis will deepen the capability of GIS spatial analysis and the related spatial data mining and knowledge discovery fields. In addition, it will improve the development of quantitative geography to some extent due to the symbolic role of spatial interaction modeling in geographic research.