Intelligent initial map scale generation based on rough-set rules

Abstract

A proper initial map scale can help improve map legibility. However, the existing initial scale designs for electronic maps cannot make active adjustments according to the differences in the surrounding geographic information distributions, during map panning or navigation. This causes many redundant zooming operations, which reduce the reading efficiency. To solve this problem, we propose a method based on the rough set, which chooses an initial map scale according to the spatial distribution of the road network. First, the spatial distribution of the road network is evaluated using the neighborhood relation model, with Delaunay triangulations. Next, the data of the road network’s spatial distributions and the corresponding map scale data from user operations are collected at different locations. Then, the relationship rules are extracted based on rough set. Finally, an intelligent initial map scale service is developed according to the rules, and its feasibility and effectiveness are tested using an experimental system. The test results show that the intelligent initial map method can adjust the map scale adaptively and dynamically according to distribution of the road network. Consequently, the map legibility is improved significantly because of the reduction in the number of zooming operations.