Topic outline

  • Data Application: Visualisation
    • Introduction
    • Learning Outcomes

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  • Introduction

    Before getting into the main topics, we give an introduction to visualisation in general, and discuss how visualisations can be useful throughout the research process.

  • Topic 1: Colour Scales

    Colour is often an important part of a visualisation, but is one that is often not considered carefully. 

    This topic discusses how the choice of colours can affect the message you are trying to convey, or over-emphasise (or completely hide!) certain properties of your data. 

  • Topic 2: Map Projections

    Many areas of environmental science make use of spatial data, and a good way of displaying and exploring spatial data is by showing the data on a map. 

    This topic discusses some of the main challenges of projecting spatial data onto a 2-dimensional image, and some of the different trade-offs you can make when choosing how to present your data.

  • Topic 3: Timeseries

    After considering how to visualise space, we move to discuss how to display data spread out over time. Timeseries data is very common in all areas of research, and in this topic we discuss a few different options for visualising such datasets. 

  • Topic 4: Using Python and Jupyter notebook

    This topic focuses on some of the tools used for creating visualisations and for organising your code. We introduce one specific tool - Jupyter notebooks - and how you can use it to organise your code and analysis notes more efficiently. 

  • Topic 5: Interactivity

    Visualisations don't have to be single images. For complex data, it is often useful to allow the user some means of interacting with the data in a visual way. This could be simply adding some way of filtering the data behind a graph, or some more complex interactions, including panning, zooming, or highlighting different aspects of a complex dataset. 

    In this topic, we discuss some of the concepts of interactivity and present a couple of examples to illustrate the ideas.

  • Conclusions

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