engleski

Streamlining visualisation of geographical data through statistical programming tools

In a world where the daily amount of generated data is overwhelming, it is getting increasingly difficult to extract useful information in a concise, easily interpretable format. Statistical data gets lost in big tables and linear graphs and people fail to connect them to a real-world phenomena. A good example for this is statistical data related to geographical regions - without visualising the geographical data that lies under the statistics, it is hard to conceive and understand the data. By combining these two aspects of the data, visualising them gets a lot simpler, the information presented is denser and more visually interesting to the observer. Naturally, there is more to it than just using two raw datasets and visualising them - it is extremely important to make sure the data collected is correct, standardised, and reusable as well as making the visualisations as close to the subject at hand as possible. It is important to consider various aspects such as the audience, to identify which combination of colour and size works best to highlight the data and whether to use relative or absolute statistical data (or showing both side by side). The paper gives an overview of common issues regarding to the collection of statistical and geographical data, and presents our solution for streamlining the visualization of geographical statistical data.

visualisation, GIS, geostatistical data, R programming language

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