Data visualisation takes many forms. Progressive organisations are asking better questions of their data by using visualisations as a discovery tool that can help them make more informed decisions and improve efficiency of business processes. Governments provide social data to inform the population of social characteristics of a given area. Or, magazines use graphic representations of Downton Abbey information purely for entertainment.
The great thing about data visualisation is that the nature of the data is secondary. Seemingly menial data can, if presented well, be engaging, entertaining and interesting. Ten years’ worth of social big data can be useless without a framework for understanding it. Data visualisation fills that role in making large amounts of data understandable and actionable, provided that the visualisation suits the data.
For example, geographical data (such as crime rates according to population density per district) can be accessed through government open data portals. This type of data would have less impact as a bar chart or list than it would being represented on a map using a tool like InstantAtlas. The actual figures overlaid onto the map would provide too much information to be useful, so you could use the features in this tool to show quantities as colours to represent intensity.
The underlying concept is that the data is the raw material, and you apply creativity to choosing how to present it. And there are a range of tools available for visualising data. This tutorial on Google’s Developers Blog discusses how to map earthquakes using Google Maps API. Readers are guided through importing the dataset from the US Geological Survey in JSONP format and placing a basic marker at each earthquake’s location.
Since simple markers give little detail of the earthquake, users are then shown how to represent each marker with a circle size or heatmap that is determined by the magnitude of the earthquake. All the necessary code is provided to build your map from start to finish, and this method can be applied to many other visual representations of data.