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Tables are generally used where users will look up a specific measurement, while charts of various types are used to show patterns or relationships in the data for one or more variables. Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines, or bars) contained ...
A scatter plot can be used either when one continuous variable is under the control of the experimenter and the other depends on it or when both continuous variables are independent. If a parameter exists that is systematically incremented and/or decremented by the other, it is called the control parameter or independent variable and is ...
According to Bertin, each of the visual variables suggests its own mode of perception and interpretation, which MacEachren ties to the cognitive theory of Image schema (e.g., size: Large-small ~ more-less). [9] These modes make each variable better for representing certain kinds of information, and serving certain purposes, than others.
Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess (PR) capabilities but their primary function is to distinguish and create emergent patterns.
Visual variables filling each region are used to represent each aggregate summary value: hue is commonly used for qualitative variables, such as predominant land use, while lightness is most common for quantitative differences, such as population density. Choropleth maps are the most popular form of thematic map due to their intuitive nature ...
A mosaic plot, Marimekko chart, Mekko chart, or sometimes percent stacked bar plot, is a graphical visualization of data from two or more qualitative variables. [1] It is the multidimensional extension of spineplots, which graphically display the same information for only one variable. [ 2 ]
To determine if a causal loop is reinforcing or balancing, one can start with an assumption, e.g. "Variable 1 increases" and follow the loop around. The loop is: reinforcing if, after going around the loop, one ends up with the same result as the initial assumption. balancing if the result contradicts the initial assumption.
An example would be showing one variable as a choropleth map, with another variable shown as proportional symbols on top of the choropleth. A correlated symbol map represents two or more variables in the same thematic map layer, using the same visual variable, designed in such a way as to show the relative combination of the two variables.