From time immemorial, man has had the urge to explore into the unknown and chart new territory. From the first ancient Viking sailors to Christopher Columbus, there is enough testimony that supports this fact. And as man has discovered newer and newer lands, they have endeavored to record their discoveries, to keep an exact record of what they have discovered. Cartography, or the art of creating maps as a field developed as a result.
The dawn of the 21st century has meant that the world has become a global village. Repercussions of events in one part of the earth are now felt worldwide. Multinational companies operating on a global level have to deal with several countries at once. So in todays world maps are no longer used just as guides or route finders. Their application is now much more varied
Globalisation has meant that today data is being generated from all the corners of the globe. Data that needs to be compared, contrasted and then inferred from. This data can be represented in the form of tables or even graphs and charts. But what then? Is there no way to include the locations from where these data are generated visually? Is there no way to include maps in data visualization? Of course there is.
Using maps for data visualization has become a widespread phenomenon, one that is used by most data miners for extracting information accurately and fast from data generated from different locations. Be it sales of a particular brand of cola in different states, or aeroplane routes that are most popular, maps are an invaluable tool for data visualization.
Maps have the advantage of being region specific. A lot of data mining is concerned with region specific data. Consider an example. Lets say a manufacturing company has factories and go downs in 15 states and sales outlets in 25 states. When the company wants to gather data for its annual report it will need the data for total goods manufactured and stored at each of it factories and go downs, total goods transported to each of its sales outlets and total goods sold from them. This kind of data would be best interpreted if plotted on a map which shows the states in concern, and the data that goes along with them. With the data available in such a handy format, the analyzer can now draw inferences from the data with much greater ease than he would have otherwise.
Again let us consider a health organization trying to fight the outbreak of an epidemic. Their efforts have to be such that the most affected regions receive the most attention. To prioritize their actions, they could plot the number of outbreaks per region on a map and then decide on the regions which are most affected. This would simplify matters much more than trying to look through the data manually.
The usability of maps as data visualizing tools is immense. The moment the data is combined with its location, facts and underlying connections about the data emerge that would have been hidden otherwise. Mapping is a field of data visualization whose complete potential is still to be tapped.
For more information on visual maps, and mapping please visit FusionCharts.
