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Displays
Published in Stephan Konz, Steven Johnson, Work Design, 2018
Certain types of graphs are better. The most common graph is a time series (see Figure 1.3); sometimes a “picture in a picture” is used in which “recent” data is displayed in the upper corner of the long-term graph. The relational graph (e.g., Figure 2.26) also is excellent. Cleveland and McGill (1985) recommend dot charts (see Figure 16.6) over divided bar graphs. Figure 16.7 shows some techniques of indicating data variability. Line charts have an advantage over vertical bar graphs (where the data point is the end of a column from the axis) in that the line chart emphasizes a key feature, the slope, without the distractions (clutter) of the vertical lines of the bars. However, line charts require a continuous axis. Scales on the axes either are continuous (time, temperature, weight) or discrete (cities, people, experimental conditions). You can connect the data points (i.e., have a line) for continuous axes (see Figure 16.4) but not for discrete axes (see Figure 16.6). Lines also are better for comparison of multiple data sets. If a bar chart must be used, make the bars horizontal as they are much easier to label than vertical bars.
General Design Guidance
Published in James R. Williams, Developing Performance Support for Computer Systems, 2004
Line charts – are appropriate for data where the function is continuous or smoothed, or has many points. Line charts are also good for showing trends (especially if the x-axis is ordered, e.g., time) and when comparing a series with the same x-axis. Consider the following guidelines when designing line charts: The shape of the line graph should tend towards the horizontal with greater length than height because the eye is practiced in detecting deviations from the horizon (Tufte, 1983).Avoid elaborate encoded shadings, cross-hatching and color (Tufte, 1983). However, consider using high contrast shading to highlight the shape of the function.Do not use abbreviations in labels and present horizontally (never vertically).Keep labels to single lines (Tufte, 1983).Consider starting the vertical axis at the lowest data point rather than at the horizontal axis (Tufte, 1983). This allows the user to immediately see the value of the lowest point on the distribution.
Statistical Inference I
Published in Simon Washington, Matthew Karlaftis, Fred Mannering, Panagiotis Anastasopoulos, Statistical and Econometric Methods for Transportation Data Analysis, 2020
Simon Washington, Matthew Karlaftis, Fred Mannering, Panagiotis Anastasopoulos
The final graphical technique considered in this section is the line chart. A line chart is obtained by plotting the frequency of a category above the point on the horizontal axis representing that category and then joining the points with straight lines. A line chart is most often used when the categories are points in time (time series data). Line charts are excellent for uncovering trends of a variable over time. For example, consider Figure 1.15, which represents the evolution of the U.S. air-travel market. A line chart is useful for showing the growth in the market over time. Two points of particular interest to the air-travel market, the deregulation of the market and the Gulf War, are marked on the graph.
Interactive Visual Exploration of Big Relational Datasets
Published in International Journal of Human–Computer Interaction, 2023
Katerina Vitsaxaki, Stavroula Ntoa, George Margetis, Nicolas Spyratos
Currently, work regarding automated chart suggestion is at a preliminary stage, supporting column and bar charts, line charts, pie charts, scatter charts as well as data tables. In particular:Column charts are employed for combinations of nominal or ordinal data with quantitative data when the number of categories (nominal or ordinal data) is smaller than seven.Bar charts are used for combinations of nominal or ordinal data with quantitative data when the number of categories (nominal or ordinal data) is greater than seven.Line charts are suggested for combinations of time data with quantitative data.Pie charts are suggested for combinations of nominal or ordinal data with quantitative data when the values to be represented are decimal and their sum amounts to 1.Scatter charts are employed when the data for both axes are quantitative.In all other cases, results are presented as data tables.
Transportation data visualization with a focus on freight: a literature review
Published in Transportation Planning and Technology, 2022
Yunfei Ma, Amir Amiri, Elkafi Hassini, Saiedeh Razavi
The line graph (or line chart) is one of the simplest visualization techniques, representing the change of a variate over time (Ferreira et al. 2013). However, the line graph is not a very popular method for displaying temporal patterns because trajectory data based on time often exhibit periodicity which makes a radial map a better choice for showing recursive events (Jin et al. 2019). For the XY-axis-based analysis of temporal events, the y-axis is used to show attributes and the x-axis to show the time. The line graph represents the progress of attribute-value according to time. Figure 21(a) shows the different traffic patterns by a line graph, where each line graph could be used also as a fingerprint for traffic pattern visualizations (Wang, Lu, and Li 2020). Line graph serves as the analysis for temporal attributes and is usually combined within a larger hybrid visualization. The advantage of the line graph for temporal analysis is that it can represent multiple attributes and stack them on the same x-axis. It is visually obvious for the user to identify potential patterns based on time, compared to the radial diagram layout which could mostly have 1–2 attributes, and the inner and outer rings are different in scales for comparison (Bak et al. 2015). However, the disadvantage of the line graph is that it is hard to represent the periodicity of the attribute. A radial map is better for the visualization of the periodicity of attributes. Figure 21(b) shows a combination of weather data and traffic patterns in the line graph. The combination of different layers of attributes on a line graph could facilitate the analysis of co-occurrence patterns for events like traffic bottlenecks and extreme weather conditions (AL-Dohuki et al. 2021).