Google Fusion Table

Irish Population per County (Source: CSO Census Data)

Google Fusion Tables – Visualise your data

Okay so you have gathered some awesome data and you want to impress your boss with some useful information. Now while bar charts have their place, here is a way to make data visually alive. Thankfully there is a useful application which will do the hard work for you, and impress your boss at the same time.

“Google Fusion Tables is an experimental data visualization web application to gather, visualize, and share data tables.”

Google Fusion Tables is a web application tool used to create a visual interpretation of data sets. Data tables can be gathered from public data or imported from your own data. The data is then visualised and can be published and shared on the web. There is a real collaborative feel to the application and the information can be communicated to your target audience with ease.
Google Fusion Tables must firstly be installed by creating a Google account and signing into My Drive. Simply connect Fusion Tables as a new application, for free, and you are ready to begin.

Designing an Irish Population Heat Map
To create a Heat Map of the Irish population by county we needed two specific data tables, namely:
• Population figures by county (csv. file)
• Counties of Ireland data map (kml. file)
Now there are various ways these can be created but for this Heat Map the Population figures were taken from the most recent CSO database, which was taken in 2011.
The Map data was derived from a KML data file and contained geometry data on all the counties in the Republic of Ireland. This data was used to essentially plot the county boundaries in Google maps.

The next step was to cleanse the data which is important for any data exercises. The data from the CSO population table was converted into an Excel document and it was noticed that some of the counties included subsets which needed to be amended. The ‘State’ and ‘Provinces’ were removed and the data for Tipperary North and South was combined into one county. This left the data with 26 counties and corresponding population figures for each county.

The KML file was downloaded into Fusion Tables and there were 99 rows in total. This was the geometry data for the counties. This step is very important as the data from the two tables must be compatible or the files will not merge correctly.
These tables were uploaded into Google Fusion Tables ready to be ‘Merged’. This is where the power of Fusion Tables comes into its own. The Map file was opened and from File

A new Tab was created, with the merged data given a visual representation of the Population of Ireland by County for 2011. At this point, the map needs to be edited to give the Heat Map some visual meaning. It was decided to distribute the counties into six buckets based on population density. The figures were distributed as; 0 – 75,000 (6 counties), 75,000 – 100,000 (4), 100,000 – 125,000 (4), 125,000 – 180,000 (6), 180,000 – 250,000 (3) & 250,000 – 1,273,070 (3). Though this was not evenly distributed, the counties were easier to distinguish and the map had a clearer visual impact. The counties could have been evenly distributed by breaking the data from the Population table into even sets and represented in this fashion. Each bucket was given a colour which was incrementally darker as the population density increased. A legend was created and gives the Heat Map more context when distinguishing the county’s population numbers.

I have made my data public and this is an important feature of Google Fusion Tables. Anyone can now take my data use this to carry out further research on population in Ireland.

Irish Population Data in action
The Heat Map of Irish population could be used in a number of interesting ways depending on data gathered. The CSO website has a number of detailed databases with well-presented data sets on a number of topics including; housing, health, education, labour market, tourism and transport. These would be used on a macro level to for the government to decide on future spending requirements in certain areas. The country is experiencing a housing shortage and the government are expected to deliver social housing projects. To identify the biggest number of social housing areas needed the government would use a combination of social housing applicants by area. Plotting these two data sets would give a nationwide Heat Map and identifying the most needed areas on a more local scale. The KML data would need to be more granular to target specific areas within counties. A well-presented Heat Map would give an excellent representation specific area shortages and therefore where funding is most needed.
Taking data from the 2011 CSO Census, another heat map was created showing the Vacancy rates of Housing per County. The Heat Map below shows properties that re left vacant per county. This is another example of using CSO data to present a visual Heat Map in reporting social issues.

Further Practical uses for Fusion Tables
Google Fusion Tables have a variety of functions for making a visual interpretation of your data. Scene perception studies have proven people show an increased understanding of pictures based on colour. The most recognisable is for representing weather. News and weather reports are presented with predicted weather patterns and forecasts. This visual information is consumed and used for; sea crossings, floods, farming, heatwaves, icy roads, planning journeys.
An excellent use of Heat Maps has been on the research of Global Warming patterns. Predictive maps are powerful when publishing outcomes. The psychological impact of seeing the global warming patterns is proven to help with understanding and give meaning to, often complicated, data sets.

Google Fusion Tables is an excellent application to present data in a clear visual format. The application is extremely useful for taking geometry data in a KML file and creating a Heat Map using Google Maps. This visual representation, when shared, is an interactive way to present your data to a wider audience. The collaboration element gives the opportunity to enhance data and findings based on original data sources. The application has the potential to give a greater understanding of data sets, in a user friendly visual format.



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