…clear and understandable?#
Labels
Any visualisation should be clear and legible i.e. produced at a high resolution with any text large enough for reading, however further elements of legibility can be underestimated, particularly when visuals are used in different contexts.
Beyond text, other labelling can make images more clear and understandable. Features of interest can be directly labelled, clearly indicating what a reader’s attention is being drawn to.
Features in an image may be obvious to an Earth observation scientist or even someone familiar with the story in question, but they may not be obvious to someone without this background and/or someone seeing an image for the first time.
Clear labelling of data sets used (and links to them), and use of plain text explanation of variables (rather than sector specific jargon) can also make understanding visualisations easier.
Scale
Given how infrequently people view the Earth from space with their own eyes, it can be easy to lose the sense of scale. This is particularly true for images where there are no recognisable features, so clear labelling is important to help provide a visual reference and, in addition, a scale bar can be a helpful addition for clarity.
Scale can also be important when creating visualisations with numerical data. In these instances, it can be helpful to provide context as part of the visualisation (See an example of this in the relative size of ice loss vs cities later in this guide).
Formats
Modern media consumption takes place increasingly through digital media on phones, tablets, and laptops. For visuals to be clear and understandable, they increasingly need work in multiple formats and sizes, in particular on screened devices such as mobile phones.
To make visualisations accessible to more people it is also important to include alternative text in web based visualisations, to support text readers; and to avoid flashing GIF animations. Link text
Colour
Colour schemes can be used to great effect to highlight features in Earth observation data, however they need to be suitable for common visual impairment i.e. colour blindness. Colour schemes can also be more or less intuitive, depending on the particular feature of interest e.g. using red for hot and blue for cold is understandable for most people. However, colour schemes can also affect the interpretation of visualisations so care needs to be taken to choose an appropriate colour scheme.
