Computing and managing cardinal direction relations

Abstract

Abstract-Qualitative spatial reasoning forms an important part of the commonsense reasoning required for building intelligent Geographical Information Systems (GIS). Previous research has come up with models to capture cardinal direction relations for typical GIS data. In this paper, we target the problem of efficiently computing the cardinal direction relations between regions that are composed of sets of polygons and present two algorithms for this task. The first of the proposed algorithms is purely qualitative and computes, in linear time, the cardinal direction relations between the input regions. The second has a quantitative aspect and computes, also in linear time, the cardinal direction relations with percentages between the input regions. Our experimental evaluation indicates that the proposed algorithms outperform existing methodologies. The algorithms have been implemented and embedded in an actual system, CARDIRECT, that allows the user to 1) specify and annotate regions of interest in an image or a map, 2) compute cardinal direction relations between them, and 3) pose queries in order to retrieve combinations of interesting regions.

A splitting line model for directional relations

“…The resulting 25 distinct direction tiles are stored in a 4-value, 5x5 matrix. Skiadopoulos et al [14,13] present an efficient implementation of the direction-relation matrix model that can handle disconnected regions and regions with holes. Chen et al [3] proposed an extension to the direction-relation matrix model which uses the overlay of two grids of directional tiles.…”

A Family of Directional Relation Models for Extended Objects

“…However, class REG excludes points, lines, and objects with emanating lines. A thorough discussion about REG Ã and the way objects are modeled in REG Ã appears in [36], [19]. To define a relation in CDR between a primary object a and a reference object b, we consider the mbb of object b and four rays originating from its four vertices.…”

Quantitative Query Technology of Direction Relationships Based on Open Shape

“…If we choose the corner stored in cor[i] as the starting point of the starting borderline before(as Fig.4, [3] cor [2] cor[0] cor [1] cor [3] cor [2] cor[0] cor [1] cor [3] cor [2] cor[0] cor [1] a) 0°≤Δθ≤90°-γ b) 90°-γ<Δθ<180°-γ c)180°-γ≤Δθ≤180°…”

“…Skiadopoulos [1] put forward improving arithmetic both in qualitative and quantitative aspects, but the quantitative arithmetic is a quantitative reasoning technology based on quantitative topological relationships, which is used to compute the quantity of the accurate position between spatial objects. Guo [2] analyzed the quantitative topological relationships, but it was not the quantitative query technology.…”