Location‐based algorithms for finding sets of corresponding objects over several geo‐spatial data sets

Abstract

When integrating geo-spatial data sets, a join algorithm is used for finding sets of corresponding objects (i.e., objects that represent the same real-world entity). This article investigates location-based join algorithms for integration of several data sets. First, algorithms for integration of two data sets are presented and their performances, in terms of recall and precision, are compared. Then, two approaches for integration of more than two data sets are described. In one approach, all the integrated data sets are processed simultaneously. In the second approach, a join algorithm for two data sets is applied sequentially, either in a serial manner, where in each join at least one of the joined data sets is a single source, or in a hierarchical manner, where two join results can be joined. For the two approaches, join algorithms are given. The algorithms are designed to perform well even when location of objects are imprecise and each data set represents only some of the real-world entities. Results of extensive experiments with the different approaches are provided and analyzed. The experiments show the differences, in accuracy and efficiency, between the approaches, under different circumstances. The results also show that all our algorithms have much better accuracy than applying the commonly used one-sided nearest-neighbor join.

Automated Matching Crowdsourcing Road Networks Using Probabilistic Relaxation

“…Beeri et al (2004) defined a probability to measure the matching confidence according to the distance between two objects. Safra et al (2010) improved it by introducing Null matching. When matching R to T, a probability of each candidate matching pair is calculated by the method of Safra et al (2010).…”

“…Safra et al (2010) improved it by introducing Null matching. When matching R to T, a probability of each candidate matching pair is calculated by the method of Safra et al (2010).…”

“…1: 1 and 1: M matching pairs can be selected from the probabilistic matrix by row or column (Safra et al 2010, Song et al 2011. However, a lot of road segments without corresponding pairs may be mismatched to the road segments with a maximum probability.…”

A Polygon and Point-Based Approach to Matching Geospatial Features

“…These authors match polygon datasets by using an iterative closest point algorithm that detects corresponding point pairs for two point sets derived from each contour of corresponding objects. Zhang and Meng [44] integrated two spatial datasets by means of an iterative matching approach, and Safra et al [45] developed a location-based join algorithm for searching for corresponding objects based on the distance proximity between their representative points. On the other hand, Huh et al [46] develop a method for detecting a corresponding point pair between a polygonal object pair with a string matching method based on a confidence region model of a line segment.…”

“…Some of them require topological properties such as a valence of each node or the directions of edges connected at a node [6,44], while others consider the vertices or locations of objects as isolated points, not as geometric primitives that form the shape of an object [42,43,45], and finally, others are restricted to a low density of objects to be matched [42,46]. Following Fan et al [30], in this last case when neighboring polygons are located immediately next to each other and are similar in shape and size, there will be error matching.…”