“Classified (or discrete or categorical) data represent named groups of values, for example, high, medium and low hazard. Continuous data represent a continuously varying phenomenon such as depth in me”
In this module, we will explore both of these and their visualisation and application.
Goal: Explore and map continuous and classified data
Load the Countries and Populated Places layers from Natural Earth
Check your results
Do you get anything like the maps in the screenshots above?
What do the values in the hillshade layer represent?
Name | Expectation |
---|---|
Countries layer |
ne_10m_admin_0_countries in the classified-data/ne.sqlite database |
Populated places layer |
ne_10m_populated_places in classified-data/ne.sqlite |
tandale_hillshade.tif |
In classified-data/exercise-data |
While many phenomena that we want to map or analyse exist as continuous data, grouping or classifying values works well when you wish to reduce data preparation complexity or deal with local variances in the interpretation of data. For example, a flood depth of 50cm may represent a high hazard zone in an area where people commonly have basements in their houses, and a low hazard zone in areas where people commonly build their houses on raised platforms.
If you take continuous data and classify it in a style, does that make the data classified?:
Is a binary (0/1 or yes/no) field classified or continuous:
Can a text field be continuous:
Download the sample data for the lesson.