The visualisation of geospatial data is achieved via cartography a.k.a. mapmaking. In this process, variables correlated to geographical locations are layered over maps, in what is called georeferencing. When the visuals accurately depict the data, maps are an efficient and intuitive way to communicate spatial processes⁴. Nowadays, most geospatial data has been acquired with remote sensing devices, satellite, or aerial imagery. Recently the addition of drone footage and higher resolution (satellite) cameras have provided Earth scientists with daily and detailed images of the planet^{5, 6}. This has transformed our field considerably over the last decades, increasing the amount of geospatial visualisation programs and allowing for more detailed geospatial analysis.

Several figure types can be used to represent spatial data:

• Maps
• Graphs
• Images/pictures
• Animation/videos

The visualisation of temporal (time series) data is a powerful tool in the geosciences, as we often study changes trough time. Temporal data is mostly represented by graphs but can also be visualised using process visualisation techniques.

Observing changes in (spatio-)temporal factors lends insights into the forecasting of future events as well as the reconstruction of the past. Despite the complexity of spatiotemporal data, recent decades have seen shift towards real-time visual analysis and data integration⁷. This has been accomplished through the development of platforms such as ArcGIS and R statistical package⁸.

After the collection of spatial and temporal data, statistical analysis can be applied. Statistics is the science of analysing, interpreting, validating, and characterizing systems that are incompletely known. Visualising statistical data is an important part of data analysis. To do so, we must ensure that our databases have a clear data-structure. This includes adequate documentation, effective naming conventions, and an understanding of the project’s needs⁹.