结构化居民地群的多层次识别方法

A Multilevel Identification Approach to Structured Building Clusters

针对比例尺跨度较大（10倍甚至50倍）的情况,现有的聚类方法较难体现居民地的渐进合并过程。拟在初始数据源比例尺和综合后地图比例尺之间内插系列中间比例尺,在多层次上进行居民地群的识别。根据空间认知原理和格式塔视觉准则,将居民地群的空间结构概括为5种典型模式,并定义了各模式约束条件,提出了基于紧密性网络与典型模式相结合的结构化居民地群识别方法。首先,通过Delaunay三角网对大比例尺居民地要素进行邻近关系识别,建立紧密性网络图,判断强闭合环路、弱闭合环路和延伸线,识别群结构中的各类典型模式。然后对识别出的群结构进行综合处理,依据设定阈值处理得到中间各级比例尺数据,从而实现多尺度空间数据的连续可视化。实验表明,利用该方法识别出的结果能够体现居民地群的空间分布特征,更加符合人的认知习惯。

The traditional clustering method is inappropriate for the gradual merging process of buildings,especially for a large span between two scale datasets.In order to resolve this problem,this paper proposed a multilevel identification approach to structured building clusters through inserting series middle scale between initial scale and target scale.Based on the spatial cognition and Gestalt principles,the structures of building group are abstracted and summarized into five typical patterns,and an identification approach is presented for building groups based on compactness network diagram and five typical patterns.Firstly,the neighborhood relationship is captured and the compactness network diagram is constructed with Delaunay triangulation.And the strongly compact loops,weakly compact loops and extended lines are generated for detecting the typical patterns.Then,the middle scale dataset can be obtained under the given constraints and thresholds,so that to achieve continuous visualization of multi-scale spatial data.Finally,experiments show that the identified result reflect the spatial distribution characteristics of buildings more clearly and seems more consistent with the habit of human cognition.