A Cmap chart is a type of data visualization that portrays relationships. They are often used in complex networks or for the analysis of events with many different variables. Cmap charts can take on five distinct forms: line, bar, matrix, tree map, and choropleth map. Each different shape communicates something different about the relationship it’s portraying. For example, line charts use the length to represent strength of the connection between two items while bar charts use height for amplitude.
Line and bar charts do not represent or portray actual data in the real world. However, they are useful for the visualization of a large number of distinct values (especially with respect to quantitative variables). Bar charts are useful for comparing a quantity between two different groups or over time. Line and bar charts are both showtime-based, meaning that there is a time dimension present.
Choropleth maps can be used to represent continuous variables such as rainfall, population or income. You can adjust any of these parameters (for example, by making changes to line thickness), so you can display how certain values impact the map over time. All of the charts use color to represent data values.
CMap charts are not limited to just the 5 shapes described above; there are many other possibilities including scatterplots, histograms, and bubble maps.
Choropleth maps are most often used for displaying data that has a discrete, step-wise relationship between its variables.
As a guide, the chart below represents students’ GPAs on an axis from 0 to 4: each dot represents one student’s GPA and the size of the dot is correlated with college GPA. The colors and brightness are representative of which major each student is enrolled in. This allows us to easily compare students within the same major and compare across majors.
Introduction to Cmap charts [to use as knowledge, not to be copied verbatim]:You can use maps to display data for a spatial area that has a continuous (quantitative) variable that varies over space, such as population density or electricity costs. The map shows how values of the variable change across the area, and how those changes relate to each other spatially. Maps can be created with Cmap chart software, such as TIBCO Spotfire. If you use a map to display more than one continuous variable, you create a choropleth map.
Choropleths show how the values of one or more continuous variables vary across a geographic area. In creating a choropleth map, the first step is to decide on the geographic area to be represented and the second is deciding which variable(s) will represent that geographic area. Once both decisions are made, you can start creating your chart by selecting one of the five basic shapes (line, bar, matrix, tree map or choropleth) and adjusting parameters for your chosen pattern type.
Each chart type has the following parameters that you can adjust to create a map:
Line thickness: determines how thicker or thinner lines are scaled in the map’s display. Increasing the line thickness may help to simplify your chart’s display.
Line color: determines how color is used to represent values of the variable. You can use color alone, define individual colors for each data value, or use color gradients (a range of colors).
Linetype: defines whether the lines are solid or open (dotted). Dotted lines only provide cross-sectional information, not directionality. You can also choose to have dashed or dotted coordinates instead of solid ones.
Directional color: determines whether the lines are solid for your data values or if the color will only be used to define directionality. You can choose a gradient color as in line color.
Transparency: allows you to see underlying information (solid lines) or only showing the lines based on the values of your variable. You can create a dark map (a completely black background for your map) or a light map (a bright white background for your map). Light and dark maps are useful for revealing underlying patterns in your data when you want to emphasize certain values. However, many commercial software packages provide an option to randomly adjust transparency so that it matches a large population of data points.