Spatial Relations Using High Level Concepts

Corcoran, Padraig; Mooney, Peter; Bertolotto, Michela

doi:10.3390/ijgi1030333

Cited by 16 publications

(10 citation statements)

References 54 publications

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“…The framework we have developed is ideally suited for this task and can be applied to essentially all approaches built on calculi using JEPD (jointly exhaustive and pairwise disjoint) relations. Examples are Renolen's basic types of change (Renolen 2000), approaches to defining perceptual topology (e.g., perceived connectedness such as a settlement, see Corcoran et al (2012)), or Brunet's chorematic modeling Brunet (1987). This would allow for responding to the call by Schultz and collegues Schultz et al (2011) to provide guidance on when to use a certain calculus (and when not to).…”

Section: Discussionmentioning

confidence: 99%

Intuitive Direction Concepts

Klippel¹,

Wallgrün²,

Yang³

et al. 2015

Baltic International Yearbook of Cognition, Logic and Communica

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Experiments in this article test the hypothesis that formal direction models used in artificial intelligence correspond to intuitive direction concepts of humans. Cognitively adequate formal models of spatial relations are important for information retrieval tasks, cognitive robotics, and multiple spatial reasoning applications. We detail two experiments using two objects (airplanes) systematically located in relation to each other. Participants performed a grouping task to make their intuitive direction concepts explicit. The results reveal an important, so far insufficiently discussed aspect of cognitive direction concepts: Intuitive (natural) direction concepts do not follow a one-size-fits-all strategy. The behavioral data only forms a clear picture after participants' competing strategies are identified and separated into categories (groups) themselves. The results are important for researchers and designers of spatial formalisms as they demonstrate that modeling cognitive direction concepts formally requires a flexible approach to capture group differences.

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Section: Discussionmentioning

confidence: 99%

Intuitive Direction Concepts

Klippel¹,

Wallgrün²,

Yang³

et al. 2015

Baltic International Yearbook of Cognition, Logic and Communica

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“…In graph theory, there exist many algorithms, such as breadth-first search, for computing topologically inequivalent paths. How humans model the world is topological on some level and therefore topological path planning has been studied extensively in the field of spatial cognition [5,6]. Topological path planning has been demonstrated to have many applications in the robotics domain.…”

Section: Related Workmentioning

confidence: 99%

Topological Path Planning in GPS Trajectory Data

Corcoran

2016

Sensors

Self Cite

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This paper proposes a novel solution to the problem of computing a set of topologically inequivalent paths between two points in a space given a set of samples drawn from that space. Specifically, these paths are homotopy inequivalent where homotopy is a topological equivalence relation. This is achieved by computing a basis for the group of homology inequivalent loops in the space. An additional distinct element is then computed where this element corresponds to a loop which passes through the points in question. The set of paths is subsequently obtained by taking the orbit of this element acted on by the group of homology inequivalent loops. Using a number of spaces, including a street network where the samples are GPS trajectories, the proposed method is demonstrated to accurately compute a set of homotopy inequivalent paths. The applications of this method include path and coverage planning.

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“…Strategies employed by these methods can be categorized into four types, including aggregation by displacing, aggregation by flooding, aggregation by sampling, and aggregation by connecting objects [11]. In this paper we explore the last of these strategies, which uses the triangles connecting objects as connectors to merge objects, which is by far the most commonly used methodology [22]. Triangles from the Delaunay triangulation can provide explicit spatial relationships between features, and can used be to guide the amalgamation process [3,[7][8][9]23].…”

Section: Introductionmentioning

confidence: 99%

Progressive Amalgamation of Building Clusters for Map Generalization Based on Scaling Subgroups

Zhang

Yang

2018

IJGI

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Map generalization utilizes transformation operations to derive smaller-scale maps from larger-scale maps, and is a key procedure for the modelling and understanding of geographic space. Studies to date have largely applied a fixed tolerance to aggregate clustered buildings into a single object, resulting in the loss of details that meet cartographic constraints and may be of importance for users. This study aims to develop a method that amalgamates clustered buildings gradually without significant modification of geometry, while preserving the map details as much as possible under cartographic constraints. The amalgamation process consists of three key steps. First, individual buildings are grouped into distinct clusters by using the graph-based spatial clustering application with random forest (GSCARF) method. Second, building clusters are decomposed into scaling subgroups according to homogeneity with regard to the mean distance of subgroups. Thus, hierarchies of building clusters can be derived based on scaling subgroups. Finally, an amalgamation operation is progressively performed from the bottom-level subgroups to the top-level subgroups using the maximum distance of each subgroup as the amalgamating tolerance instead of using a fixed tolerance. As a consequence of this step, generalized intermediate scaling results are available, which can form the multi-scale representation of buildings. The experimental results show that the proposed method can generate amalgams with correct details, statistical area balance and orthogonal shape while satisfying cartographic constraints (e.g., minimum distance and minimum area).

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Spatial Relations Using High Level Concepts

Cited by 16 publications

References 54 publications

Intuitive Direction Concepts

Intuitive Direction Concepts

Topological Path Planning in GPS Trajectory Data

Progressive Amalgamation of Building Clusters for Map Generalization Based on Scaling Subgroups

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