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Abstract
Complex multi-holed-region entity scenes (i.e., sets of random region with holes) are common in spatial database systems, spatial query languages, and the Geographic Information System (GIS). A multi-holed-region (region with an arbitrary number of holes) is an abstraction of the real world that primarily represents geographic objects that have more than one interior boundary, such as areas that contain several lakes or lakes that contain islands. When the similarity of the two complex holed-region entity scenes is measured, the number of regions in the scenes and the number of holes in the regions are usually different between the two scenes, which complicates the matching relationships of holed-regions and holes. The aim of this research is to develop several holed-region similarity metrics and propose a hierarchical model to measure comprehensively the similarity between two complex holed-region entity scenes. The procedure first divides a complex entity scene into three layers: a complex scene, a micro-spatial-scene, and a simple entity (hole). The relationships between the adjacent layers are considered to be sets of relationships, and each level of similarity measurements is nested with the adjacent one. Next, entity matching is performed from top to bottom, while the similarity results are calculated from local to global. In addition, we utilize position graphs to describe the distribution of the holed-regions and subsequently describe the directions between the holes using a feature matrix. A case study that uses the Great Lakes in North America in 1986 and 2015 as experimental data illustrates the entire similarity measurement process between two complex holed-region entity scenes. The experimental results show that the hierarchical model accounts for the relationships of the different layers in the entire complex holed-region entity scene. The model can effectively calculate the similarity of complex holed-region entity scenes, even if the two scenes comprise different regions and have different holes in each region.
Highlights
The collection and maintenance of spatial data is the most time-consuming activity in Geographic Information System (GIS) engineering in the real world, data for the same objects are often repeatedly collected by different departments [1]
The experiment measuring the similarity of the two Great Lakes scenes in North America between 1986 and 2015 does not involve other scenarios that might match the map of the Great Lakes; we do not need to consider that little difference exists in the index that may exist in the similarity calculation process of the multiple matching scenarios
It is known that the similarity measurement of a complex holed-region entity scene has eight first-order indexes: the similarity of the matching micro-spatial-scene collection S’MSC, the outer contour shape similarity of the matching holed-region entities S’exshape, the similarity of the position graphs S’p_g, the transformation similarity of the center point position graph (CPPG), and the matching similarity of the holed-regions S’R_S
Summary
The collection and maintenance of spatial data is the most time-consuming activity in GIS engineering in the real world, data for the same objects are often repeatedly collected by different departments [1]. Several differences are present in the same objects on maps produced from different sources that are due to mapping errors, different applications, and interpretations [2,3,4]. These identical entities are usually inconsistent in positioning accuracy, shape, and attribute information. To reduce the cost of GIS data collected by the GIS application departments and to evaluate the quality of the existing map data, it is necessary to use standard map data as a reference to measure the similarity of two map data. These topics have attracted a great deal of research attention over the past decades [8,9]
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