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Random measurements from a population or replicates on a specific sample are often also referred to as univariate data. Learn more here

One-dimensional spatial data and bivariate data. Temperature vs depth (in ocean, boreholes, or the atmosphere), elevation cross-sections, solar insolation vs latitude, etc. are all examples of one-dimensional spatial data. An example of bivariate data is temperature vs salinity for ocean water. Both types of data are easily visualized using x-y scatter plots. Learn more here

One-dimensional time series. Examples include tides (ocean and solid earth), seismic signals, temperature, pressure, elevation, solar irradiance, etc. as functions of time. This type of data is easily visualized using x-y scatter plots.Learn more here

Spatial Contour Plots are extremely common throughout the geosciences. Contour plots are very useful for visualizing and interpreting large data collected on a two-dimensional grid network. Examples of such data sets include elevation, surface air temperature, ocean salinity, atmospheric water content, or vegetation type.
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Two dimensional times series are best visualized by HovMoller plots in which the absicca (x-axis) is the time and the ordinate (y-axis) some spatial variable like latitude, height, or depth, and the measured quantity identified by isopleths or color contours.
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