Abstract
In the geographic information system field, identifying and cleaning self-intersected polygons to achieve simple polygons is a basic and core issue. In traditional research, self-intersected polygons were identified by the topological relationship between the line segments that form polygons. However, in the process of map generalization or map spatial overlay calculations, the elimination of topological self-intersection is not sufficient. Elements (vertices and line segments) that are topologically separated but whose distance is smaller than the minimum visible distance on the map also need to be considered to avoid graphic conflicts. Both the above two cases are defined as visual self-intersection in this article. Aimed at monocyclic polygons, a method of classification and cleaning for visual self-intersection is proposed. First, three basic visual self-intersection patterns, intersecting, separating and collinear, were distinguished and defined and then further divided into ten refined types. Second, the automatic recognition of different visual self-intersection types was achieved by calculating the topological and distance relationships between the vertices of the polygon and the line segments of the polygon, and corresponding cleaning algorithms for different visual self-intersection types were proposed. Finally, the real topographical map data of a typical area in Jiangsu were used for validation. The experimental results indicate that all self-intersections identified by the advanced Martinez-Llario method were successfully captured by the proposed method, and it identified eight more types, accounting for 40% of the total number of visual self-intersection polygons. After human-computer interaction verification, the identification rate of the proposed method was found to reach 100%. In addition, the visual perception of human cartographers and the closure property of polygons were considered simultaneously during the cleaning for each visual self-intersection pattern, so the proposed method also obtained better cleaning results.
Highlights
In the geographic information system (GIS) field, a polygon is a closed curve formed by a set of vertices arranged in a certain direction on a plane and connected in sequence, and if there is no pair of nonconsecutive edges sharing a common point, the polygon is a simple polygon [1], [2]
Of polygons are changed by certain data operations; e.g., vertices are repeatedly added during data acquisition, spatial overlay analysis, simplification and smoothing map generalization operations [3]–[5]
In contrast to the previously mentioned results of the Martinez-Llario method, the global connection index (GCI) of all inner buffers for the polygons obtained by the proposed method is 1, which indicated that all 81 visually self-intersected polygons were transformed into simple polygons
Summary
In the geographic information system (GIS) field, a polygon is a closed curve formed by a set of vertices arranged in a certain direction (clockwise or counterclockwise) on a plane and connected in sequence, and if there is no pair of nonconsecutive edges sharing a common point, the polygon is a simple polygon [1], [2]. For polygons with visual adjacent vertices or line segments, existing methods do not detect them as self-intersection polygons These methods do not process them and cannot eliminate their influence in the subsequent operations of map generalization or map spatial calculations. According to the distances and topological relationships between the basic elements of monocyclic polygons (vertices and line segments), this article classified the visual selfintersections into three basic patterns. The third part of the recognition algorithm is determining the visual self-intersection pattern for each intersecting line segment pair based on the classification rules, whose time complexity is O(1). (4) For combined patterns: If a basic visual selfintersection type exists discontinuously more than once in a polygon, each type is treated separately as a single visual self-intersection according to the abovementioned cleaning algorithms.
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