Abstract
As an important part of spatial data, the point feature has always been an essential element in web maps and navigation maps. With the development of location-based services and the rapid increase in volunteered geographic information and social media data, the amount of point data is increasing day by day, resulting in inevitable problems of overlay and congestion during visualization. Map generalization provides multiple algorithms that can be used to select, aggregate and make typification of points or point clusters. For the generalization of point data, however, the traditional stand-alone computing environment has difficulty with real-time realization. Currently, the rapid development of cloud computing technology provides a powerful support for improving the efficiency of map generalization. However, compared with the stand-alone environment, the data decomposition and the real-time display of point generalization in the cloud platform imposes higher requirements on the point generalization constraints, which play an important role in point-generalized process control. Based on the computational characteristics of the cloud platform, this paper analyzes the changes in point generalization constraints. In addition, our work proposes the constraints of point generalization based on the cloud platform and its construction method, builds a prototype system based on the Hadoop cloud platform. Our prototype system is tested using typical experimental data. Its efficiency and the quality of its results is examined. The results show that the efficiency and quality of point selection can be significantly improved by controlling the point generalization process with the generalization constraints in the cloud computing environment proposed in this paper. This paper provides a possible way for the realization of map generalization in the cloud computing environment. Its usability with real data and with many users accessing it will be the focus of further research.
Highlights
Point information is an important feature that needs to be visualized in current web maps, mobile maps and even maps with special purposes, such as crisis management [1]
To analyze the operating efficiency and the quality of the point generalization results, the circle growth algorithm was realized with the experimental data of Nanjing, Xi’an and Beijing in stand-alone station and in the cloud platform with the number of reserved points calculated with constraints suggested in this paper
The combination of the cloud platform and point generalization can effectively solve the various problems of massive point data visualization, such as overlay, congestion and other issues
Summary
Point information is an important feature that needs to be visualized in current web maps, mobile maps and even maps with special purposes, such as crisis management [1]. When performing zooming operations, crowding and overlay between symbols can occur. This outcome is even more important in the case of maps for visually impaired users, where the usable map load is much more limited [3]. Using a smaller version of map symbols is a solution only in the case of a small difference between the original and target scale. Online filtering alleviates this problem to some extent, but it only acts as a solution for computer problems and cannot reflect the spatial relationship between the features
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