Points on maps that stand for geographic objects such as settlements are generally connected by road networks. However, in the existing algorithms for point cluster simplification, points are usually viewed as discrete objects or their distances are considered in Euclidean spaces, and therefore the point cluster generalization results obtained by these algorithms are sometimes unreasonable. To take roads into consideration so that point clusters can be simplified in appropriate ways, the network Voronoi diagram is used and a new algorithm is proposed in this paper. First, the weighted network Voronoi diagram is constructed taking into account the weights of the points and the properties of the related road segments. Second, the network Voronoi polygons are generated and two factors (i.e., the area of the network Voronoi polygon and the total length of the dilated road segments in the polygon) are considered as the basis for point simplification. Last, a Cartesian coordinate system is built based on the two factors and the point clusters are simplified by means of the “concentric quadrants”. Our experiments show that the algorithm can effectively and correctly transmit types of information in the process of point cluster simplification, and the results are more reasonable than that generated by the ordinary Voronoi-based algorithm and the weighted Voronoi-based algorithm.

Owing to map scale reduction and other cartographic generalization operations, spatial conflicts may occur between buildings and other features in automatic cartographic generalization. Displacement is an effective map generalization operation to resolve these spatial conflicts to guarantee map clarity and legibility. In this paper, a novel building displacement method based on multipopulation genetic algorithm (BDMPGA) is proposed to resolve spatial conflicts. This approach introduces multiple populations with different control parameters for simultaneous search optimization and adopts an immigration operation to connect different populations to realize coevolution. The optimal individuals of each population are selected and preserved in the elite population through manual selection operation to prevent the optimal individuals from being destroyed and lost in the evolutionary process. Meanwhile, the least preserving generation of the optimal individuals is used as the termination basis. To validate the proposed method, urban building data with a scale of 1:10,000 from Shenzhen, China are used. The experimental results indicate that the method proposed in this paper can effectively resolve spatial conflicts to obtain better results.