Abstract
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.
Highlights
Spatial data infrastructures provide valuable information to the academic community, the industrial and private sectors, and the government [1]
Since we focus on residential areas in this study, common types of the object-matching relationships often denoted by the ratio between the object numbers in the large-scale and the small-scale maps include 0:1, 1:0, 1:1, m:1 (m > 1), 1:n (n > 1), and m:n (m > 1 and n > 1) matching of objects
Irregular parts in the polygon are considered unsuitable and require further processing; (3) Rectangularity: due to the spatial characteristics of the residential areas, updated polygons if having near-rectangular interior angles are expected to have rectangular interior angles and need to be processed to a rectangle; (4) Minimum representable area: each object in a topographic map needs to have an area greater than a minimum representable area to be viewable and meaningful; otherwise, exaggeration or deletion needs to be applied to important or unimportant objects, respectively
Summary
Spatial data infrastructures provide valuable information to the academic community, the industrial and private sectors, and the government [1] Mapping agencies, such as the National Administration of Surveying Mapping and Geo-information in China, produce and maintain large amounts of topographic maps and geospatial datasets that characterize the same geographic areas in varied themes and at different scales. One way to update MRDBs is to update each individual data layer at its own scale, a process which is often time-consuming and labor-intensive Another way is to update objects in the large-scale map (i.e., a map that shows small areas with detailed features) and use cartographical generalization methods to produce new objects in the small-scale map (i.e., a map that shows large areas with generalized features) [8]. We first describe our methodological principles and present a case study to assess the performance of our methods
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