Transforming Data

Agrimetrics combines data from a variety of sources to bring additional insights to the surface. As part of that process we may produce some statistical measures as well as running more sophisticated models. Common transformations in the statistical data are listed below so that values returned by our products can be interpreted correctly.


TransformationDescription
AverageUnless specifically stated otherwise, "average" is the arithmetic mean of all the values available at finer resolution.
E.g. a daily average temperature is the arithmetic mean of all hourly values for that day.
Monthly minimum/maximumTo calculate representative monthly values such as minimum temperature, we take the set of daily minimum temperature values (the lowest temperature recorded for each day in that month) and take the average of those. This represents the typical values for that parameter experienced in that month rather than representing the peak value (e.g. coldest minimum) experienced in the month.
Long-term averagesTo calculate long-term averages at monthly resolution, we take all the daily values for that month in a 10-year period, and calculate the average of all of them.
For example, the long-term average minimum temperature for January is the average of the daily minimum temperatures for all January days in a 10-year period. It does not indicate the peak values, and is not the average of the lowest temperature reached in each month in that 10-year period.