In this article, the options for creating and customizing a scatter plot widget are explained. For an overview of creating and visualizing and analyzing data in any type of widget in FieldScope, see the Customizing Widgets article.
A scatter plot widget display allows you to select 2 numeric variables from a project’s data set and compare them on X- and Y-axes. Scatter plots can help you explore the relationship between the two selected variables. The configuration options for scatter plots in FieldScope are discussed below.
Once you have selected Scatter plot as the type of widget you’d like to create, you will see options to (1) use a filter set and (2) configure data. You may use the preconfigured “Recent observations” filter set or you may select a filter set you create.
After selecting a Filter set, you have the option to choose a numeric variable for both the X-axis and Y-axis. Only observations that have values for the selected X- and Y-axes variables will be included as points on the graph.
A third option allows you to select a third parameter to further categorize the data. To view the categories (e.g., month of observation), click the dropdown menu under Categories. Any categorical variables included in a project are available, as well as Month of observation and Year of observation, which are derived from the observation data and included as an option in all projects.
To view a legend for the categorized colors, click the three dots and lines in the top right of the graph display. The example below shows Latitude for the X-axis, Day of observation for the Y-axis, and Month of observation for the category.
There is also an option to display a best-fit line and give graph viewers access to a correlation equation. When this option is enabled, anyone viewing the scatter plot can place their cursor over the best-fit line to see the best-fit line equation and correlation value.
The best-fit line provides an indication of the relationship(s) between the variables represented by the data points on the scatter plot. The line is drawn as close to the center of all of the points as possible. The number of data points above the line will be about equal to those below the line.
The best fit line can be described by the equation y = bx + a, where b is the slope of the line and a is the intercept, or the value along the Y-axis through which the best fit line will pass.
The r value is called the correlation coefficient. If r = ±1, the model is a "perfect fit" with all data points lying on the line. If r = 0, there is no linear relationship between the two variables. A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally described as weak.
Before adding the scatter plot to your visualization, you can edit the name of your scatter plot by clicking on the edit pencil icon in the upper left corner of the screen. Once you have configured your scatter plot widget, click on Save.
You will be returned to the main screen of the visualization, where all of the created widgets will appear together.