A computational model for direction relations between spatial objects in GIS

Wang, Ke

doi:10.1016/j.ijleo.2014.07.097

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…The third method focuses on the computation of the direction relation, which has always been the focus of many scholars. A histogram of angles is proposed to calculate the relative directions of objects [38]. To overcome the drawbacks of a histogram of angles, which has computational cost and is only suited for raster data, Matsakis [39] utilized a histogram of force.…”

Section: Spatial Relations 1) Direction Relationmentioning

confidence: 99%

Indoors Positioning Based on Spatial Relationships in Locality Description

Wang

Xiu²,

Fan

et al. 2020

IEEE Access

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Spatial relationships exist in our daily communication and provide many locality clues, but minimal attention has been devoted to positioning localities indoors with spatial relationships extracted from locality descriptions. Locality description generally contains spatial relationships (i.e., topological, distance, and direction relations) and reference objects (ROs). For locality description positioning, distance and direction relation convey more clue than topology and combined together can provide much clue to positioning. Explicit and implicit features are inherent characteristics of locality descriptions. Based on the analysis of locality descriptions, a classification scheme of indoor locality descriptions is provided. To address the positioning locality with spatial relationships, we propose a two-state method. First, the located region is obtained according to the fuzzy region of spatial relations in locality description. Second, the probability distribution is determined, which is a joint probability function that consists of relative direction and distance (quantitative or qualitative distance) membership functions. The relative direction membership function is conducted based on human's direction cognition is related to angular information. The trapezoid quantitative distance membership function is based on indoor cognitive experiment. The qualitative distance membership function for indoor ROs is based on minimum Euclidean distance and stolen area. From the cognition and computation perspectives, the visible boundary met some restrictions is provided and a sampling method is defined. To obtain the unique positioning locality, a principle is proposed. We evaluate our method by conducting an indoor cognitive experiment and demonstrate that a positioning accuracy of 3.50 m can be achieved with semantically derived spatial relationships.

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Section: Spatial Relations 1) Direction Relationmentioning

confidence: 99%

Indoors Positioning Based on Spatial Relationships in Locality Description

Wang

Xiu²,

Fan

et al. 2020

IEEE Access

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Analyzing reach planform changes based on metric relations

Leng

Fan

2017

2017 6th International Conference on Agro-Geoinformatics

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A Combinatorial Reasoning Mechanism with Topological and Metric Relations for Change Detection in River Planforms: An Application to GlobeLand30’s Water Bodies

Leng

Yang

Chen

2017

IJGI

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Changes in river plane shapes are called river planform changes (RPCs). Such changes can impact sustainable human development (e.g., human habitations, industrial and agricultural development, and national border security). RPCs can be identified through field surveys-a method that is highly precise but time-consuming, or through remote sensing (RS) and geographic information system (GIS), which are less precise but more efficient. Previous studies that have addressed RPCs often used RS, GIS, or digital elevation models (DEMs) and focused on only one or a few rivers in specific areas with the goal of identifying the reasons underlying these changes. In contrast, in this paper, we developed a combinatorial reasoning mechanism based on topological and metric relations that can be used to classify RPCs. This approach does not require DEMs and can eliminate most false-change information caused by varying river water levels. First, we present GIS models of river planforms based on their natural properties and, then, modify these models into simple GIS river planform models (SGRPMs) using straight lines rather than common lines to facilitate computational and human understanding. Second, we used double straight line 4-intersection models (DSL4IMs) and intersection and difference models (IDMs) of the regions to represent the topological relations between the SGRPMs and used double-start-point 8-distance models (DS8DMs) to express the metric relations between the SGRPMs. Then, we combined topological and metric relations to analyse the changes in the SGRPMs. Finally, to compensate for the complexity of common river planforms in nature, we proposed three segmentation rules to turn common river planforms into SGRPMs and used combinatorial reasoning mechanism tables (CRMTs) to describe the spatial relations among different river planforms. Based on our method, users can describe common river planforms and their changes in detail and confidently reject false changes. Future work should develop a method to automatically or semi-automatically adjust the segmentation rules and the combinatorial reasoning mechanism.

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A computational model for direction relations between spatial objects in GIS

Cited by 3 publications

References 16 publications

Indoors Positioning Based on Spatial Relationships in Locality Description

Indoors Positioning Based on Spatial Relationships in Locality Description

Analyzing reach planform changes based on metric relations

A Combinatorial Reasoning Mechanism with Topological and Metric Relations for Change Detection in River Planforms: An Application to GlobeLand30’s Water Bodies

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