I have a good news for you. It is not a rocket science! All you have to do is to create a Google account, (probably you already have one). Than go to your Google drive, search for Google fusion table and connect it to your drive. Great! Than you will need some data what you are going to use before you can begin.
A good source of information is cso. So i went to its site and I exported some data to exel and saved it on my local drive. (You can input data to exel manually or copy and paste.) Than you have to open your Google drive and click “new” and click fusion table. Than choose file from your computer and click next. Than you can set in which row are the column name’s are and click next. Than click file and geocode. Than I created a few buckets clicking on change style and I got this.
The point of colors on the map refers to number of the population of the county by males and females.
Than i downloaded a kml file from idependent’s website and I followed the same steps. KML is a Keyhole Markup Language, an XML notation for expressing geographic annotation and visualization within Internet-based, two-dimensional maps and three-dimensional Earth browsers.
Than I merged the two tables and I got this.
We can see on the map that the most populated county is Dublin. I am not looking for the reasons now why there is a huge difference in the population of the Capital and the counties because this blog is not about the economy of Ireland. But feel free to create your own fusion table about unemployment, number of new jobs created or even on the number of universities in each county and merge it with this table. I am sure you will get interesting figures.
I was interested about how many accidents are on the irish roads so I retrieve some data from rsa’s website and I created a fusion table.
Google fusion tables allow you to visualise and understand your raw data on a map or chart. It helps you to understand the corellation between two or more datasets. But it does not give you answers so I reccommend it to use with caution if you are looking for correlation. However is a great tool which can be used widely to visualise data geographically.