The world of geography: Visualizing a knowledge domain with cartographic means

Skupin, André

doi:10.1073/pnas.0307654100

Cited by 107 publications

(74 citation statements)

References 10 publications

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“…Representing complex data in the form of a map enables people to relate to such representations more easily at the first glance on the visualization. Skupin presented a method that produces a map-like visualization for a document corpus [12]. Similarly, Pang and Biuk-Aghai used the same concept to produce a map-like visualization for Wikipedia [13].…”

Section: Map-like Wikipedia Visualizationmentioning

confidence: 99%

A New Way to Use Wikipedia in Education: A Pilot Study of Map-like Wikipedia Visualization on iPad

Li¹,

Si²,

Chio³

2014

IJFCC

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Abstract-iPad has started to appear in classrooms recently. Studies show that the tablet computer engages students in learning and improves student performance. On the other hand, Wikipedia attracts many attentions in education field as well despite of worries about credibility and reliability. We have developed a prototype of interactive Map-like Wikipedia Visualization in an earlier research project. In order to test the feasibility of such a new technology in classrooms, we conducted an explorative study which combines the usage of iPad, Wikipedia and the visualization in a high school in Macau S.A.R. This paper presents the design of this study and the result we observed.

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Section: Map-like Wikipedia Visualizationmentioning

confidence: 99%

A New Way to Use Wikipedia in Education: A Pilot Study of Map-like Wikipedia Visualization on iPad

Li¹,

Si²,

Chio³

2014

IJFCC

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“…Various techniques have been devised for producing such maps. For example, from the GIS community, there is the notable work of Skupin, Fabrikant and others [10,35,[37][38][39]. Adding to intuition, there is some evidence that map-based displays of abstract data provide an aid to comprehension [9].…”

Section: Fig 1 Lilliput and Brobdingnag (Thanks To Projectmentioning

confidence: 99%

Viewing Abstract Data as Maps

Gansner

Kobourov

2013

Handbook of Human Centric Visualization

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From telecommunications and abstractions of the Internet to interconnections of medical papers to on-line social networks, technology has spawned an explosion of data in the form of large attributed graphs and networks. Visualization often serves as an essential first step in understanding such data, when little is known. Unfortunately, visualizing large graphs presents its own set of problems, both technically in terms of clutter and cognitively in terms of unfamiliarity with the graph idiom. In this chapter, we consider viewing such data in the form of geographic maps. This provides a view of the data that naturally allows for reduction of clutter and for presentation in a familiar idiom. We describe some techniques for creating such maps, and consider some of the related technical problems. We also present and discuss various applications of this method to real data.

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“…Mapping is an important method to observe multivariate spatial patterns in the geographic context [21].…”

Section: Finding the Reduced Projected Datamentioning

confidence: 99%

Mining Spatio-Temporal Data of Fatal Accident

Musdholifah¹,

Hadhim²

2013

IJCA

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Traffic accidents are an important concern of today's governments and societies, due to the high cost of human and economical resources involved. Data mining has been proven able to significantly help in improving traffic safety. Among several data mining tasks, clustering technique is mostly applied on spatio-temporal data, especially for the traffic data. A number of traffic related works proposed different clustering techniques for mining the spatio-temporal of traffic accident. However, some difficulties appeared when analyzing these datasets, such as the size of data, the lack of statistical evaluation methods, and interpreting the valuable patterns. With regard to solving this problem, this paper proposes a clustering approach for mining spatio-temporal data of fatal accident using local triangular kernel clustering (LTKC) algorithm. LTKC is kernel-density-based clustering algorithm that has the ability to determine the number of clusters automatically. We also propose three visualization techniques for use to interpret and present the optimal clustering result in an easy-understanding form. From the experimental results, LTKC approach was found to be able to discover responsible clusters within fatal accident data, which had proven by silhouette and Dunn index values close to 1. In addition, using visual techniques, we can state that the clustering results were well-separated and compact clusters.

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The world of geography: Visualizing a knowledge domain with cartographic means

Cited by 107 publications

References 10 publications

A New Way to Use Wikipedia in Education: A Pilot Study of Map-like Wikipedia Visualization on iPad

A New Way to Use Wikipedia in Education: A Pilot Study of Map-like Wikipedia Visualization on iPad

Viewing Abstract Data as Maps

Mining Spatio-Temporal Data of Fatal Accident

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