A Polygon and Point-Based Approach to Matching Geospatial Features

Abstract

A methodology for matching bidimensional entities is presented in this paper. The matching is proposed for both area and point features extracted from geographical databases. The procedure used to obtain homologous entities is achieved in a two-step process: The first matching, polygon to polygon matching (inter-element matching), is obtained by means of a genetic algorithm that allows the classifying of area features from two geographical databases. After this, we apply a point to point matching (intra-element matching) based on the comparison of changes in their turning functions. This study shows that genetic algorithms are suitable for matching polygon features even if these features are quite different. Our results show up to 40% of matched polygons with differences in geometrical attributes. With regards to point matching, the vertex from homologous polygons, the function and threshold values proposed in this paper show a useful method for obtaining precise vertex matching.

Study of NSSDA Variability by Means of Automatic Positional Accuracy Assessment Methods

“…Consequently, the assessment of the positional quality of cartographic products has become an issue of particular significance and relevance [1,2]. Positional quality is determined by positional accuracy [3] which, in turn, is evaluated by means of statistical methods based on measuring positional discrepancies between the location of "well-defined point entities" stored in a geospatial database (GDB) and their true (real world) location [1]. In this sense, there are several point-based standard methodologies (PBSM) which can be used for computing the positional accuracy of GDBs.…”

“…These approaches are based on the method of defining positional accuracy proposed by Goodchild and Hunter [16], who suggest quantifying the positional accuracy of a certain GDB by measuring the differences between the locations of spatial entities stored in that GDB (named as tested or assessed data sources) and their locations determined by another GDB (named as reference data sources) of greater accuracy. Based on this 'new' paradigm, we have developed a point-based methodology for the automatic positional accuracy assessment (APAA) of spatial data [1,9]. This methodology has allowed us not only to increase significantly the number of entities (points) used in the assessment process (by means of the PBSM application) but also to empirically verify the conclusions reached by Ariza and Atkinson [4] with regard to the variability of NSSDA estimations, without the need to generate synthetic populations of errors, when the number of check points increases.…”

“…This paper takes as its starting point our previous work [1]. Specifically, a subset of the homologous points obtained between two urban GDBs according to the automated methodology developed in it, and is focused on the second aspect mentioned above, that is, to verify that the variability of NSSDA results decreases when the number of check points increases, and their tendency to underestimate accuracy.…”

“…-3 -point-based methodology for the automatic positional accuracy assessment (APAA) of spatial data [1,9]. This methodology has allowed us not only to increase significantly the number of entities (points) used in the assessment process (by means of the PBSM application) but also to empirically verify the conclusions reached by Ariza and Atkinson [4] with regard to the variability of NSSDA estimations, without the need to generate synthetic populations of errors, when the number of check points increases.…”

“…This metric is based on a procedure developed by Arkin et al [18] which compares two polygonal curves by means of their turn angle representations. As already stated, both procedures are extensively detailed in [1]. It is, therefore, necessary to emphasize that they are not addressed here.…”