I want to generate insights and results from our data and knowledge
by visualising & interpreting the data

Visualisations come in many forms, such as hand-drawn scribbles, complex digital imagery, or multidimensional, interactive applications. It takes both visual and scientific skills to create useful visualisations. Remember, the purpose of visualisations is to tell a story that will affect your audience and encourage them to make the changes that your data is suggesting. The visualisations need to tell the story accurately. At the same time, they need to be interesting enough to hold your audience’s attention. As noted, the visualisation is only part of the story; you will need to interpret the data to place the correct emphasis on the parts of the visualisation that you want to have stand out to the audience. If this expertise is not available among the stakeholders and actors within your Citizen Observatory, you may need to engage experts that can support you with data interpretation and visualisation (more on how to engage key stakeholders here).

However, with some training, every citizen scientist can learn to use data visualisations as a means to interpret and check their own data sets. Here is the reflection from a participant of the WeObserve online course, after finishing a training module on data visualisation:

“After last week’s module, I have been working on the way I visualise my data. In my online blog this week, I have included the new charts I produced because of your course … Because of the visualisation, and how to make the data more meaningful, I discovered some errors in the data records, but also it has identified a weather anomaly, where the last week of June over many successive years has been colder than the weeks either side. I have no explanation for this. Going back into the actual station records, it isn’t incorrect tabulation, there is clearly a trend for this 7-day period to be cooler, over successive years. Curious!“ (Norman Woollons, Learner)

Two very common types of data visualisations are:

• Time-series graphs: These usually show how a variable changes over time. The data on these graphs can be historical from observed and measured data, or future projections based on simulations. Historical time-series graphs can also be based on simulations.
• Maps: These show information and data in relation to a specified system of reference. Two or more variables are plotted against each other. Standard maps combine geological and infrastructure information (such as topography and street maps) with a geographic location; but you can map any information that has a location based on the geographic coordinate system (latitudes and longitudes). There are other ‘maps’ based on different reference systems (like the weekly mood map, or mapping a person’s mental state while performing an activity related to a perceived challenge and the person’s skill level).

Sometimes these two graphs are combined to produce an interactive experience; clicking on the map might display a time-series graph at that location.

Example from the LandSense project

The Natura Alert app was developed by the LandSense Citizen Observatory to report threats to bird habitats in Important Bird and Biodiversity Areas (IBAs). Using Natura Alert, volunteers in the BirdLife network in Spain have collected threat information, which can be visualised on a map displayed in the Natura Alert web application. The data collected for Spain can also be summarised in a dashboard of charts available in the web app as shown in the figure below. This allows users to see the main habitat threats in Spain as well as by IBA. As more data are collected, these graphs will change dynamically to reflect the current situation.