Propagating Updates of Residential Areas in Multi-Representation Databases Using Constrained Delaunay Triangulations

• Main Authors: Xinchang Zhang, Taisheng Guo, Jianfeng Huang, Qinchuan Xin
• Format: Article
• Language: English
• Published: MDPI AG 2016-06-01
• Series: ISPRS International Journal of Geo-Information
• Subjects: cartography; geographic information system; spatial data infrastructure; Tabu Search; parallel processing
• Online Access: http://www.mdpi.com/2220-9964/5/6/80

Updating topographic maps in multi-representation databases is crucial to a number of applications. An efficient way to update topographic maps is to propagate the updates from large-scale maps to small-scale maps. Because objects are often portrayed differently in maps of different scales, it is a complicated process to produce multi-scale topographic maps that meet specific cartographical criteria. In this study, we propose a new approach to update small-scale maps based on updated large-scale maps. We first group spatially-related objects in multi-scale maps and decompose the large-scale objects into triangles based on constrained Delaunay triangulation. We then operate the triangles and construct small-scale objects by accounting for cartographical generalization rules. In addition, we apply the Tabu Search algorithm to search for the optimal sequences when constructing small-scale objects. A case study was conducted by applying the developed method to update residential areas at varied scales. We found the proposed method could effectively update small-scale maps while maintaining the shapes and positions of large-scale objects. Our developed method allows for parallel processing of update propagation because it operates grouped objects together, thus possesses computational advantages over the sequential updating method in areas with high building densities. Although the method proposed in this study requires further tests, it shows promise with respect to automatic updates of polygon data in the multi-representation databases.