制图综合知识及其应用

实现多种约束下的地图信息的负载均衡是制图综合的难点之一。在中小比例尺地图中，对于乡镇及村庄居民点进行尺度转换，需要综合考虑其行政级别、拓扑和度量关系，以使地图信息负载量在一定尺度下达到合理。提出一种基于最优信息熵约束的居民地点状要素选取方法，在最优信息熵约束下，调整度量关系约束，优先考虑语义关系，保留行政级别高的居民点，对行政级别低的居民点，如果不是道路端点，且不满足度量关系约束，则删除该点，不断迭代，直到满足最优信息熵约束。采用1∶250 000居民地点数据进行实验，实现了维护拓扑一致性、级别优先性、度量合理性的居民地点状要素选取，在有效地保持地图的负载均衡和可读性的同时，实现了地图有效信息量的最大化。采用最优信息熵约束进行居民点选取，在整体上可以保留居民点群空间分布的疏密特征，效果上能够达到图幅信息量的负载均衡。

Cartographic-Generaliaztion-Knowledge and Its Application

  Objectives  The load balancing of map information under multiple constraints is one of the difficulties in cartographic generalization. In small and medium-scale maps, it is necessary to comprehensively consider their administrative levels, topologies and metric relationships for the scale conversion of townships and village residential point features to make the map information load reasonable at a certain scale.  Methods  This paper proposes a method for selecting residential point features based on optimal information entropy constraints. Under the constraints of optimal information entropy, the metric relationship constraints are adjusted, the semantic relationships are prioritized, the residential point features with higher administrative levels are reserved, and for the residential point features with lower administrative levels, if they are not the endpoints of the road and do not satisfy the metric relationship constraint, then the points are deleted, and the process is iterated until the optimal information entropy constraint is satisfied.  Results  Experiments with 1∶250 000 residential point data have realized the selection of residential location elements that maintain topological consistency, level priority, and metric rationality. The load balancing and readability of the map are effectively maintained, meanwhile, the amount of effective information of the map is maximized based on the algorithm.  Conclusions  The optimal information entropy constraint is adopted for the selection of residential points, which can retain the density characteristics of the spatial distribution of the residential point group as a whole, and achieve the load balancing of map information in effect.