20 research outputs found

    An algorithm for the selection of route dependent orientation information

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    Landmarks are important features of spatial cognition and are naturally included in human route descriptions. In the past algorithms were developed to select the most salient landmarks at decision points and automatically incorporate them in route instructions. Moreover, it was shown that human route descriptions contain a significant amount of orientation information, which support the users to orient themselves regarding known environmental information, and it was shown that orientation information support the acquisition of survey knowledge. Thus, there is a need to extend the landmarks selection to automatically select orientation information. In this work, we present an algorithm for the computational selection of route dependent orientation information, which extends previous algorithms and includes a salience calculation of orientation information for any location along the route. We implemented the algorithm and demonstrate the functionality based on OpenStreetMap data

    Analyzing Cognitive Conceptualizations Using Interactive Visual Environments

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    A survey of qualitative spatial representations

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    Representation and reasoning with qualitative spatial relations is an important problem in artificial intelligence and has wide applications in the fields of geographic information system, computer vision, autonomous robot navigation, natural language understanding, spatial databases and so on. The reasons for this interest in using qualitative spatial relations include cognitive comprehensibility, efficiency and computational facility. This paper summarizes progress in qualitative spatial representation by describing key calculi representing different types of spatial relationships. The paper concludes with a discussion of current research and glimpse of future work

    A parent-centered radial layout algorithm for interactive graph visualization and animation

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    We have developed (1) a graph visualization system that allows users to explore graphs by viewing them as a succession of spanning trees selected interactively, (2) a radial graph layout algorithm, and (3) an animation algorithm that generates meaningful visualizations and smooth transitions between graphs while minimizing edge crossings during transitions and in static layouts. Our system is similar to the radial layout system of Yee et al. (2001), but differs primarily in that each node is positioned on a coordinate system centered on its own parent rather than on a single coordinate system for all nodes. Our system is thus easy to define recursively and lends itself to parallelization. It also guarantees that layouts have many nice properties, such as: it guarantees certain edges never cross during an animation. We compared the layouts and transitions produced by our algorithms to those produced by Yee et al. Results from several experiments indicate that our system produces fewer edge crossings during transitions between graph drawings, and that the transitions more often involve changes in local scaling rather than structure. These findings suggest the system has promise as an interactive graph exploration tool in a variety of settings

    Spatial and temporal resolution of sensor observations

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    Beobachtung ist ein Kernkonzept der Geoinformatik. Beobachtungen dienen bei Phรคnomenen wie Klimawandel, Massenbewegungen (z. B. Hangbewegungen) und demographischer Wandel zur รœberwachung, Entwicklung von Modellen und Simulation dieser Erscheinungen. Auflรถsung ist eine zentrale Eigenschaft von Beobachtungen. Der Gebrauch von Beobachtungen unterschiedlicher Auflรถsung fรผhrt zu (potenziell) unterschiedlichen Entscheidungen, da die Auflรถsung der Beobachtungen das Erkennen von Strukturen wรคhrend der Phase der Datenanalyse beeinflusst. Der Hauptbeitrag dieser Arbeit ist eine entwickelte Theorie der raum- und zeitlichen Auflรถsung von Beobachtungen, die sowohl auf technische Sensoren (z. B. Fotoapparat) als auch auf menschliche Sensoren anwendbar ist. Die Konsistenz der Theorie wurde anhand der Sprache Haskell evaluiert, und ihre praktische Anwendbarkeit wurde unter Einsatz von Beobachtungen des Webportals Flickr illustriert

    Investigating behavioural and computational approaches for defining imprecise regions

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    People often communicate with reference to informally agreedplaces, such as โ€œthe city centreโ€. However, views of the spatial extent of such areas may vary, resulting in imprecise regions. We compare perceptions of Sheffieldโ€™s City Centre from a street survey to extents derived from various web-based sources. Such automated approaches have advantages of speed, cost and repeatability. We show that footprints from web sources are often in concordance with models derived from more labour-intensive methods. Notable exceptions however were found with sources advertising or selling residential property. Agreement between sources was measured by aggregating them to identify locations of consensus

    ๊ฑด๋ฌผ ์†์„ฑ ์ •๋ณด๋ฅผ ์ด์šฉํ•œ ๋ณดํ–‰์ž ๋‚ด๋น„๊ฒŒ์ด์…˜์šฉ ๋žœ๋“œ๋งˆํฌ ์ถ”์ถœ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2014. 2. ์œ ๊ธฐ์œค.์ตœ๊ทผ ์ธํ„ฐ๋„ท๊ณผ ์Šค๋งˆํŠธํฐ์˜ ๋ฐœ๋‹ฌ๋กœ ๋‹ค์–‘ํ•œ ๊ณต๊ฐ„์ •๋ณด์„œ๋น„์Šค๊ฐ€ ์ œ๊ณต๋จ์— ๋”ฐ๋ผ ๋ณดํ–‰์ž์šฉ ๋‚ด๋น„๊ฒŒ์ด์…˜์— ๋Œ€ํ•œ ๊ด€์‹ฌ ๋˜ํ•œ ๋†’์•„์ง€๊ณ  ์žˆ๋‹ค. ๋ณดํ–‰์ž์—๊ฒŒ ๊ธธ์•ˆ๋‚ด๋ฅผ ํ•˜๋Š” ๋ฐ ๋žœ๋“œ๋งˆํฌ๋ฅผ ์ด์šฉํ•˜๋Š” ๊ฒƒ์ด ๋ณดํ–‰์ž์˜ ์ด๋™ ํŠน์„ฑ, ๊ธธ์ฐพ๊ธฐ ์„ฑ๊ณต๋ฅ  ์ธก๋ฉด์—์„œ ํšจ์œจ์ ์ด๋‹ค. ์ด์— ๋”ฐ๋ผ ๋ณดํ–‰์ž๋ฅผ ์œ„ํ•œ ๋žœ๋“œ๋งˆํฌ๋ฅผ ์ถ”์ถœํ•˜๋ ค๋Š” ์—ฌ๋Ÿฌ ์—ฐ๊ตฌ๋“ค์ด ์ง„ํ–‰๋˜์–ด ์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์„ ํ–‰์—ฐ๊ตฌ๋Š” ๋žœ๋“œ๋งˆํฌ๋ฅผ ์ถ”์ถœํ•  ๋•Œ ๊ฑด๋ฌผ๋“ค ๊ฐ„์˜ ์ฐจ์ด๋งŒ์„ ๊ณ ๋ คํ•˜๊ณ , ๋ณดํ–‰์ž ๋‚ด๋น„๊ฒŒ์ด์…˜์ด ๊ตฌ๋™๋˜๋Š” ํ™”๋ฉด ์† ์ง€๋„์— ๋Œ€ํ•œ ์‚ฌ์šฉ์ž์˜ ์‹œ๊ฐ์  ์ฃผ์˜๋ฅผ ๊ณ ๋ คํ•˜์ง€ ์•Š์•˜๋‹ค๋Š” ๋‹จ์ ์ด ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฑด๋ฌผ์˜ ์†์„ฑ์„ ์ง€์—ญ์  ๋ณ€์ˆ˜์™€ ์ „์—ญ์  ๋ณ€์ˆ˜๋กœ ์ •์˜ํ•จ์œผ๋กœ์จ ์ด์™€ ๊ฐ™์€ ๋ฌธ์ œ๋ฅผ ๊ฐœ์„ ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ง€์—ญ์  ๋ณ€์ˆ˜๋Š” ๊ฑด๋ฌผ๋“ค ๊ฐ„์˜ ์ฐจ์ด๋ฅผ ๋‚˜ํƒ€๋‚ด๊ณ  ์ „์—ญ์  ๋ณ€์ˆ˜๋Š” ๊ฑด๋ฌผ์ด ๊ฐ€์ง€๋Š” ๊ณ ์œ ํ•œ ํŠน์„ฑ์„ ๋‚˜ํƒ€๋ƒ„์œผ๋กœ์จ ๊ฑด๋ฌผ์˜ ํ˜„์ถœ์„ฑ๊ณผ ์‹œ๊ฐ์  ์ฃผ์˜ ์ •๋„๋ฅผ ๋ฐ˜์˜ํ•œ๋‹ค. ๋˜ํ•œ, ํ›„๋ณด๊ตฐ ์ถ”์ถœ ๋ฐฉ๋ฒ•์— ๋„คํŠธ์›Œํฌ ๋ณด๋กœ๋…ธ์ด ๋‹ค์ด์–ด๊ทธ๋žจ์„ ์ด์šฉํ•˜์—ฌ ๋„คํŠธ์›Œํฌ์˜ ์—ฐ๊ฒฐ์„ฑ์„ ๊ณ ๋ คํ•˜๊ณ  ํ›„๋ณด๊ตฐ ์ค‘์ฒฉ ํ˜„์ƒ์„ ํ•ด๊ฒฐํ•œ๋‹ค. ๋ณดํ–‰์ž์šฉ ๋žœ๋“œ๋งˆํฌ๋ฅผ ์ถ”์ถœํ•˜๊ธฐ ์œ„ํ•ด ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ์ œ์•ˆํ•œ ํ”„๋กœ์„ธ์Šค๋Š” ๋ณดํ–‰์ž๋ฅผ ์œ„ํ•œ ์„ ํƒ์  ์„ ์ •, ๋žœ๋“œ๋งˆํฌ ํ›„๋ณด๊ตฐ ์ถ”์ถœ, ๊ฑด๋ฌผ ์†์„ฑ ๋ณ€์ˆ˜ ์ •์˜, ์†์„ฑ ๋ณ€์ˆ˜์˜ ์ฐจ์› ์ถ•์†Œ ๊ทธ๋ฆฌ๊ณ  ์ตœ์ข… ๋žœ๋“œ๋งˆํฌ ์ถ”์ถœ์˜ ๋‹จ๊ณ„๋กœ ์ด๋ฃจ์–ด์ง„๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์„œ์šธํŠน๋ณ„์‹œ ๊ด€์•…๊ตฌ ์ผ๋ถ€์ง€์—ญ์„ ๋Œ€์ƒ์œผ๋กœ ์ œ์•ˆํ•œ ์•Œ๊ณ ๋ฆฌ๋“ฌ์„ ์ ์šฉํ•˜์—ฌ ๋žœ๋“œ๋งˆํฌ๋ฅผ ์ถ”์ถœํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ถ”์ถœํ•œ ๋žœ๋“œ๋งˆํฌ๋ฅผ ๊ตญ๋‚ด ํฌํ„ธ ์ง€๋„์„œ๋น„์Šค์˜ ๋ ˆ์ด๋ธ”, ๊ฒฝ๋กœ ์•ˆ๋‚ด์—์„œ ์ด์šฉ๋˜๋Š” ๋žœ๋“œ๋งˆํฌ์™€ ๋น„๊ตํ•˜์—ฌ ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ถ”์ถœ๋œ ๋žœ๋“œ๋งˆํฌ๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ์ถ”์ถœํ•œ ๋žœ๋“œ๋งˆํฌ๊ฐ€ ๋ณดํ–‰์ž ๋‚ด๋น„๊ฒŒ์ด์…˜์˜ ๊ธธ์•ˆ๋‚ด์— ์ด์šฉ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์‚ฌ์šฉ์ž๊ฐ€ ์ „์ฒด ๊ฒฝ๋กœ ๋ฐ ๊ณต๊ฐ„์— ๋Œ€ํ•ด ์ดํ•ดํ•˜๋Š” ๋ฐ ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋ผ ํŒ๋‹จํ•˜์˜€๋‹ค. ์ดˆ ๋ก โ…ฒ ๋ชฉ ์ฐจ โ…ณ ๊ทธ๋ฆผ ์ฐจ๋ก€ โ…ต ํ‘œ ์ฐจ๋ก€ โ…ธ 1. ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 1.2 ๊ด€๋ จ ์—ฐ๊ตฌ 5 1.3 ์—ฐ๊ตฌ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ•๋ก  8 2. ๋žœ๋“œ๋งˆํฌ ์ถ”์ถœ ๋ฐฉ๋ฒ• 11 2.1 ๋ณดํ–‰์ž๋ฅผ ์œ„ํ•œ ์„ ํƒ์  ์„ ์ • 11 2.1.1 ๋ณดํ–‰์ž์šฉ ๋„๋กœ๋ง ์ „์ฒ˜๋ฆฌ 11 2.1.2 ์„ ํƒ์  ์„ ์ • ๋ฐฉ๋ฒ• 13 2.2 ๋žœ๋“œ๋งˆํฌ ํ›„๋ณด๊ตฐ ์ถ”์ถœ 15 2.3 ๊ฑด๋ฌผ ์†์„ฑ ๋ณ€์ˆ˜ ์ •์˜ 18 2.3.1 ๊ธฐํ•˜ํ•™์  ํŠน์„ฑ 18 2.3.2 ์˜๋ฏธ๋ก ์  ํŠน์„ฑ 19 2.3.3 ์ง€์—ญ์  ๋ณ€์ˆ˜์™€ ์ „์—ญ์  ๋ณ€์ˆ˜์˜ ๊ณ„์‚ฐ 20 2.4 ์†์„ฑ ๋ณ€์ˆ˜์˜ ์ฐจ์› ์ถ•์†Œ 22 2.5 ์ตœ์ข… ๋žœ๋“œ๋งˆํฌ ์ถ”์ถœ 25 3. ์‹คํ—˜ ๋ฐ ๊ฒฐ๊ณผ 26 3.1 ์‹คํ—˜ ๋Œ€์ƒ์ง€์—ญ 26 3.2 ๋ณดํ–‰์ž๋ฅผ ์œ„ํ•œ ์„ ํƒ์  ์„ ์ • ์‹คํ—˜ ๋ฐ ๊ฒฐ๊ณผ 27 3.2.1 ์„ ํƒ์  ์„ ์ •์„ ์œ„ํ•œ ๋ณดํ–‰์ž์šฉ ๋„๋กœ๋ง ์ „์ฒ˜๋ฆฌ 27 3.2.2 ์„ ํƒ์  ์„ ์ • ๊ฒฐ๊ณผ 28 3.3 ๋žœ๋“œ๋งˆํฌ ํ›„๋ณด๊ตฐ ์ถ”์ถœ ์‹คํ—˜ ๋ฐ ๊ฒฐ๊ณผ 30 3.3.1 Isovist ํด๋ฆฌ๊ณค 30 3.3.2 ๋„คํŠธ์›Œํฌ ๋ณด๋กœ๋…ธ์ด ๋‹ค์–ด๊ทธ๋žจ(NVD) 32 3.3.3 ์ตœ์ข… ํ›„๋ณด๊ตฐ ์ถ”์ถœ 34 3.4 ๊ฑด๋ฌผ ์†์„ฑ ๋ณ€์ˆ˜ ์ •์˜ ๋ฐ ๊ณ„์‚ฐ 37 3.5 ์†์„ฑ ๋ณ€์ˆ˜์˜ ์ฐจ์› ์ถ•์†Œ ์‹คํ—˜ ๋ฐ ๊ฒฐ๊ณผ 39 3.5.1 PCA ๊ฒฐ๊ณผ 39 3.5.2 PCA ๊ฒฐ๊ณผ ๋ถ„์„ 41 3.6 ์ตœ์ข… ๋žœ๋“œ๋งˆํฌ ์ถ”์ถœ ๊ฒฐ๊ณผ 44 4. ํ‰๊ฐ€ 48 5. ๊ฒฐ๋ก  61 ์ฐธ๊ณ ๋ฌธํ—Œ 63 ๋ถ€๋ก 67 Abstract 88Maste
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