Abstract
The major reason that the fully automated generalization of residential areas has not been achieved to date is that it is difficult to acquire the knowledge that is required for automated generalization and for the calculation of spatial similarity degrees between map objects at different scales. Furthermore, little attention has been given to generalization methods with a scale reduction that is larger than two-fold. To fill this gap, this article develops a hybrid approach that combines two existing methods to generalize residential areas that range from 1:10,000 to 1:50,000. The two existing methods are Boffet’s method for free space acquisition and kernel density analysis for city hotspot detection. Using both methods, the proposed approach follows a knowledge-based framework by implementing map analysis and spatial similarity measurements in a multiscale map space. First, the knowledge required for residential area generalization is obtained by analyzing multiscale residential areas and their corresponding contributions. Second, residential area generalization is divided into two subprocesses: free space acquisition and urban area outer boundary determination. Then, important parameters for the two subprocesses are obtained through map analysis and similarity measurements, reflecting the knowledge that is hidden in the cartographer’s mind. Using this acquired knowledge, complete generalization steps are formed. The proposed approach is tested using multiscale datasets from Lanzhou City. The experimental results demonstrate that our method is better than the traditional methods in terms of location precision and actuality. The approach is robust, comparatively insensitive to the noise of the small buildings beyond urban areas, and easy to implement in GIS software.
Highlights
Automated map generalization has always been both a challenge and a dream for many mapping agencies [1,2,3]
Mackaness et al [31] inferred that a cartographer’s manual solution reflects a deep knowledge of the map generalization process and the ways in which map features might be illustrated at different scales
KDEwith withdifferent differentthresholds thresholds with same refined size (10 m): (a) is the result when the search radius is 150 m; (b) is the result when the search radius size (10 m): (a) is the result when the search radius is 150 m; (b) is the result when the search radius is 200 m; (c) is the result when the search radius is 300 m; (d) is the result when the search radius is
Summary
Automated map generalization has always been both a challenge and a dream for many mapping agencies [1,2,3]. China’s 1:5000 to 1:1,000,000 vector map databases, which consist of the same areas and regions at different levels of detail [4,5], are maintained and updated manually or semi-automatically by cartographers [6]. Current map generalization is undeniably a labor-intensive process that has many disadvantages, such as the repetitive digitization and compilation of data from the same region as well as inconsistent content and relationships between the map databases at different scales [4,6]. Many advancements have been made, the full automation of map generalization is still a challenge, as the knowledge acquisition that is required for automatic generalization is still difficult. Mackaness et al [31] inferred that a cartographer’s manual solution reflects a deep knowledge of the map generalization process and the ways in which map features might be illustrated at different scales. Mustiere’s [9] proposal of a “cartographic knowledge acquisition bottleneck”
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