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Language and Communication
Published in Christopher D. Wickens, Justin G. Hollands, Simon. Banbury, Raja. Parasuraman, Engineering Psychology and Human Performance, 2015
Christopher D. Wickens, Justin G. Hollands, Simon. Banbury, Raja. Parasuraman
Text and pictures should logically be tailored to their respective strengths. Pictures or graphics can best convey analog spatial relations, and complex spatial patterns. Verbal material (whether print or text) can best convey more abstract information, including action verbs that do not have a strong spatial component (e.g., “read,” “comply”). If verbal information is lengthy, it should be visual (text) rather than auditory (speech), because of the greater permanence of visual information, and the higher working memory demands required to understand speech. While there is some evidence for advantages of providing different media to individuals with different cognitive strengths (e.g., spatial graphics for those with higher spatial abilities), the strength of this effect does not appear to be great (Yallow, 1980; Landaur, 1995; Pashler et al., 2008), and it is better to choose the medium as a function of the material, and the task, based on an understanding of how people learn from words and pictures (Mayer, 2012 in press).
Increasing Urban Sustainability using GIS
Published in Rachel Beth Egenhoefer, Routledge Handbook of Sustainable Design, 2017
Luiz Felipe Guanaes Rego, Maria Fernanda Campos Lemos, Luís Carlos Soares Madeira Domingues
GIS employs a variety of tools (arithmetic, spatial relations, editing) to construct map layers reflecting the different types of information that provide visual solutions to spatial questions. Spatial representations range from simple maps that can answer a single question to a complex set of map layers constructed to answer a set of questions.
Detecting visually salient scene areas and deriving their relative spatial relations from continuous street-view panoramas
Published in International Journal of Digital Earth, 2020
Fangli Guan, Zhixiang Fang, Tao Yu, Mingxiang Feng, Fan Yang
Traditionally, three types of methods have been used to detect the relative spatial relation of objects, including digital map-based methods, sensor-based methods, and visual methods. In the digital map-based approach, the relative spatial relation (including the relative azimuth and relative distance between objects) is calculated using geotagged coordinates based on absolute positioning systems. In the sensor-based approach, lasers (Xu and Zhang 2017), radar (Liang and Fielding 2017), gyroscopes (Martyushev and Li 2019), optical measurement technology (Oh et al. 2016), and the control point method (Li et al. 2009) are used to detect relative azimuth and distance. Additionally, the multi-sensor cooperative computing method (Fang, Jiang, et al. 2018; Hu, Huang, and Chen 2017) has been used to acquire relative spatial relation data. In the visual approach, the Monocular Vision System has been used to estimate the relative position of cooperative space targets using a modified gravity model and multiple target tracking methods (Pan, Huang, and Qin 2013). The Binocular Stereo Vision System can estimate the relative state by tracking the feature points of the target and employing the observation model from an EKF filtering scheme (Segal, Carmi, and Gurfil 2011). The Panoramic Vision System can estimate the attitude using an inertial Attitude Heading Reference System (Tehrani, Garratt, and Anavatti 2012). The visual-inertial system has been used for inspecting the relative relation of unknown and non-cooperative objects in space (Fourie et al. 2013).
Topological relations between spherical spatial regions with holes
Published in International Journal of Digital Earth, 2020
Jingwei Shen, Lan Zhang, Min Chen
Since Digital Earth was proposed, the related technologies of capturing, storing, processing and displaying geographical information have made considerable progress (Gore 1998; Vilchesblázquez et al. 2014). Many geographical phenomena occur on a global scale, such as global climate change (Kadiyala et al. 2015), and global land cover (Chen et al. 2017). Although the Digital Earth platform has the ability to store, retrieve and visualize global data (Goodchild and Woodgate 2012; Hernandez 2017), advanced analysis (e.g. spatial analysis on the sphere (Nicholas 2013), spatial simulation (Chen and Lin 2018; Mekni 2018)) should be further performed. The analysis of spatial relations is one foundation for the advanced analysis. The spatial relation is commonly grouped into topological relations, direction relations, and metric relations (Worboys 1992; Sharma 1996). Among these spatial relations, topology is considered to be first-class information (Egenhofer and Mark 1995). The topological relations are effective for spatial querying and human decision-making (Dube 2017). With the attention of the global-scale geographical phenomena, topological relations between the global-scale spatial entities should also attract more attention.
Ontology-Based Modelling and Information Extracting of Physical Entities in Semantic Sensor Networks
Published in IETE Journal of Research, 2019
Mohammad Ahmadinia, Ali Movaghar, Amir Masoud Rahmani
There can be various spatial relations between the physical entities. For example, an entity is placed near another entity or an entity is placed inside another entity, etc. In the SWOL model [26], a set of spatial relations between entities has been presented. They are divided into two general groups: dimensional and topological relations. Dimensional relations specify an entity's geographical direction toward another entity. Dimensional relations include eight geographical directions (that is, four main directions and four secondary directions). To put it more accurately, here, we use relative angle to model dimensional relations. The range of this angle is between [−180,180]. For instance, entity x is located at angle 30 east-west of entity y. The second group of spatial relations includes topological relations based on the SWOL model. These relations include eight various relations such as DC (disconnection), EC (no external connection), EQ (equality), NTTP (X is a part of Y yet not tangential on it), NTTPi (X includes Y yet not tangential on it), TPP (X is a part of Y and internally tangential on it), TPPi (X includes Y and tangential on it), and PO (having partial intersection). They are adopted from [26]. Figure 4 illustrates the modelling of various spatial relations between entities.