Surrounds in partitions

Dube, Matthew P.; Egenhofer, Max J.

doi:10.1145/2666310.2666380

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…These formalisms enable to capture complex, dynamic, region-based structures, with the component regions inhabited by a multiplicity of dynamic objects. [23,62] have worked to define qualitative spatial terminology in graph-based embeddings. [46,24] have also proposed such models to represent complex spatial objects (but again they do not represent changes).…”

Section: Spatio-temporal Pattern Miningmentioning

confidence: 99%

Mining evolutions of complex spatial objects using a single-attributed Directed Acyclic Graph

et al. 2020

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Directed Acyclic Graphs (DAGs) are used in many domains ranging from computer science to bioinformatics, including industry and geoscience. They enable to model complex evolutions where spatial objects (e.g., soil erosion) may move, (dis)appear, merge or split. We study a new graph-based representation, called attributed DAG (a-DAG). It enables to capture interactions between objects as well as information on objects (e.g., characteristics or events). In this paper, we focus on pattern mining in such data. Our patterns, called weighted paths, offer a good trade-off between expressiveness and complexity. Frequency and compactness constraints are used to filter out uninteresting patterns. These constraints lead to an exact condensed representation (without loss of information) in the single-graph setting. A depth-first search strategy and an optimized data structure are proposed to achieve the efficiency of weighted path discovery. It does a progressive extension of patterns based on database projections. Relevance, scalability and genericity are illustrated by means of qualitative and quantitative results when mining various real and synthetic datasets. In particular, we show how such an approach can be used to monitor soil erosion using remote sensing and Geographical Information System (GIS) data.

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Section: Spatio-temporal Pattern Miningmentioning

confidence: 99%

Mining evolutions of complex spatial objects using a single-attributed Directed Acyclic Graph

et al. 2020

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“…From the literature already reviewed, we also note that our "engulfs" relation corresponds directly to the "surrounds" relations in [Randell et al 1992;Cohn et al 1997;Donnelly 2005;Schultz et al 2006;Bittner et al 2008; Hahmann and Brodaric 2013; ?]. The "surrounds" relation in definition 3.3 has an indirect correspondence to the sur-roundsAttach relation of Dube and Egenhofer [Dube and Egenhofer 2014]. In short, a region r that is surrounded by a set of regions…”

Section: Properties Of Engulfs and Surroundsmentioning

confidence: 99%

Identifying Surrounds and Engulfs Relations in Mobile and Coordinate-Free Geosensor Networks

Both

Duckham

Worboys

2018

ACM Trans. Spatial Algorithms Syst.

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This paper concerns the definition and identification of qualitative spatial relationships for the full and partial enclosure of spatial regions. The paper precisely defines three relationships between regions, "surrounds," "engulfs," and "envelops," highlighting the correspondence to similar definitions in the literature. An efficient algorithm capable of identifying these qualitative spatial relations in a network of dynamic (mobile) geosensor nodes is developed and tested. The algorithms are wholly decentralized, and operate innetwork with no centralized control. The algorithms are also "coordinate-free," able to operate in distributed spatial computing environments where coordinate locations are expensive to capture or otherwise unavailable. Experimental evaluation of the algorithms designed demonstrates the efficiency of the approach. Although the algorithm communication complexity is dominated by an overall worst-case O(n 2) leader election algorithm, the experiments show in practice an average-case complexity approaching linear, O(n 1.1).

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“…As important spatial relations, topological relations are concerned with aspects such as connectivity, intersection, and containment. In recent years, research on topological relations has focused on topological invariants (Shen, Zhou, & Chen, 2017), surrounds relations (Dube & Egenhofer, 2014;Worboys & Duckham, 2021), the deriving of topological relations (Dube, 2017(Dube, , 2021, topological changes (Jiang, Worboys, & Nittel, 2011;Worboys, 2012Worboys, , 2013, topological relations between complex objects (Dube, Egenhofer, Lewis, Stephen, & Plummer, 2015;Lewis & Egenhofer, 2014;Shen, Zhang, & Chen, 2020), and related research has produced the foundation for viable information systems (Dube & Egenhofer, 2020). Therefore, for a spatial scene, topological relations between geographic objects are particularly important.…”

Section: Introductionmentioning

confidence: 99%

Exploring the construction and application of spatial scene knowledge graphs considering topological relations

Shen

Shi

2022

Transactions in GIS

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A spatial scene consists of a large number of geographic objects and their spatial relations and attributes. It is important to explore an effective method to organize spatial scenes so that they are more readable by humans and machines. The construction of a spatial scene knowledge graph considering topological relations, which can play important roles in spatial query, spatial analysis, and spatial data modeling, is explored in this article. To construct the spatial scene knowledge graph considering topological relations, the definitions of the spatial scene knowledge graph schema, topological relation knowledge acquisition and storage, and spatial scene knowledge graph visualization are explored. Two typical applications, including knowledge reasoning and spatial neighbor analysis based on spatial scene knowledge graphs, are presented. A case study is designed, and the construction and application of a spatial scene knowledge graph of the Three Gorges Reservoir Region is shown. Nine contains relations, 18 crosses relations, and 47 touches relations are marked; and topological relation reasoning and spatial neighbor relation analysis based on a successfully constructed knowledge graph are performed. The results show the following: (1) the spatial scene knowledge graph considering topological relations is an effective and intuitive way to express the topological relations between geographic objects; and (2) the spatial scene knowledge graph may play important roles in graph‐based application areas, such as topological relation reasoning and spatial neighbor analysis.

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Surrounds in partitions

Cited by 12 publications

References 27 publications

Mining evolutions of complex spatial objects using a single-attributed Directed Acyclic Graph

Mining evolutions of complex spatial objects using a single-attributed Directed Acyclic Graph

Identifying Surrounds and Engulfs Relations in Mobile and Coordinate-Free Geosensor Networks

Exploring the construction and application of spatial scene knowledge graphs considering topological relations

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