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Interactive graphics: exemplified with real data applications.

Malik WA, Unlü A - Front Psychol (2011)

Bottom Line: Graphics are widely used in modern applied statistics because they are easy to create, convenient to use, and they can present information effectively.Static plots do not allow interacting with graphics.The benefits and strengths of interactive graphics for data exploration and data quality analyses are illustrated systematically with three complex real datasets.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of Augsburg Augsburg, Germany.

ABSTRACT
Graphics are widely used in modern applied statistics because they are easy to create, convenient to use, and they can present information effectively. Static plots do not allow interacting with graphics. User interaction, on the other hand, is crucial in exploring data. It gives flexibility and control. One can experiment with the data and the displays. One can investigate the data from different perspectives to produce views that are easily interpretable and informative. In this paper, we try to explain interactive graphics and advocate their use as a practical tool. The benefits and strengths of interactive graphics for data exploration and data quality analyses are illustrated systematically with three complex real datasets.

No MeSH data available.


Scatterplot of airtime by distance. It shows some very fast and some very slow flights.
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Figure 3: Scatterplot of airtime by distance. It shows some very fast and some very slow flights.

Mentions: We are aware that there are extreme values for both the distance and airtime. It may therefore be instructive to ask whether the distance and airtime data are internally consistent – that is, to ask if the airtimes are plausible in the context of the distances traversed and vice versa. One way to pursue this question is to construct a scatterplot of airtimes by distances. One such scatterplot of airtime by distance covered by aircraft is shown in Figure 3. The internally inconsistent values are clearly visible in this plot. Some unbelievably fast aircraft are visible in the lower corner. One arrow points to two flights from Pittsburgh to Kansas, which are more than 700 miles distant from one other. These flights took only 2 min airtime from Pittsburgh to Kansas, which is unbelievable. Also very slow flights are visible. A second arrow points to two flights from Atlanta to Louisville and these airports are at a distance of around 321 miles. The airtime of these flights is 304 min, which is quite high for a multi-engine aircraft.


Interactive graphics: exemplified with real data applications.

Malik WA, Unlü A - Front Psychol (2011)

Scatterplot of airtime by distance. It shows some very fast and some very slow flights.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3111423&req=5

Figure 3: Scatterplot of airtime by distance. It shows some very fast and some very slow flights.
Mentions: We are aware that there are extreme values for both the distance and airtime. It may therefore be instructive to ask whether the distance and airtime data are internally consistent – that is, to ask if the airtimes are plausible in the context of the distances traversed and vice versa. One way to pursue this question is to construct a scatterplot of airtimes by distances. One such scatterplot of airtime by distance covered by aircraft is shown in Figure 3. The internally inconsistent values are clearly visible in this plot. Some unbelievably fast aircraft are visible in the lower corner. One arrow points to two flights from Pittsburgh to Kansas, which are more than 700 miles distant from one other. These flights took only 2 min airtime from Pittsburgh to Kansas, which is unbelievable. Also very slow flights are visible. A second arrow points to two flights from Atlanta to Louisville and these airports are at a distance of around 321 miles. The airtime of these flights is 304 min, which is quite high for a multi-engine aircraft.

Bottom Line: Graphics are widely used in modern applied statistics because they are easy to create, convenient to use, and they can present information effectively.Static plots do not allow interacting with graphics.The benefits and strengths of interactive graphics for data exploration and data quality analyses are illustrated systematically with three complex real datasets.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of Augsburg Augsburg, Germany.

ABSTRACT
Graphics are widely used in modern applied statistics because they are easy to create, convenient to use, and they can present information effectively. Static plots do not allow interacting with graphics. User interaction, on the other hand, is crucial in exploring data. It gives flexibility and control. One can experiment with the data and the displays. One can investigate the data from different perspectives to produce views that are easily interpretable and informative. In this paper, we try to explain interactive graphics and advocate their use as a practical tool. The benefits and strengths of interactive graphics for data exploration and data quality analyses are illustrated systematically with three complex real datasets.

No MeSH data available.