Create a heat map using Google fusion tables

HeatMap Ireland Population 2011

Our first assignment of the term was to create an image of a (Google) Fusion table outlining an Irish population heatmap based on 2011 census data from the Central Statistics Office (CSO).

I was tasked with the creation of a random distribution of counties based on population density, describing as follows:
– how to achieve a heat map.

– what information could be gleaned from a heat map

-what other ideas/concepts could be represented in the heat map.

After downloading the population data from the CSO, the first step was to clean it up into 26 distinct counties, and keeping the province data separate. After this the excel worksheet was loaded to Google fusion tables, and also the KML data was loaded separately. The spreadsheet and KML tables were then merged to create a new file. (Note you need to have a Google account to load anything up to Google fusion tables.)

From the ‘Location’ Geometry I configured the heatmap to divide the 26 counties of Ireland  into 5 custom buckets of population. I adjusted the default colours to shades of blue and adjusted the suggested defaulted values of population to make more sense.  I’ve included a screenshot below of the steps to do this.

Heat map_first_image

To add the county boundaries in black, under border color I selected ‘Use one color’ as in the following screenshot:Heat map_second_picture

I also appended the legend and updated the title of the legend to be more descriptive as shown in the below screenshot:Heat map_picture3

Also under ‘Change info window layout’ I selected county and total persons only so when you click on a county you can see the name and its population.

What information could be gleaned from the heat map

In general, its instantly clear that the the counties immediately surrounding the main cities have the highest populations in the state. (Namely Dublin, Kildare, Galway and Cork). Heat maps are useful because they allow us put into context big data through a visualisation. So rather than look up and down through a spreadsheet to pick out the counties with the largest populations, the heat map visualisation makes it clear in a matter of seconds.

You can also use the filter option to (for example ) show just those counties that have populations in the region of 100000 – 150000 people. In my map as currently configured, these counties could fall under two colour schemes.

One thing to note is that the source KML file has no geometry data for County Carlow. Also the geometry for county Cavan is incorrect in the KML file (the KML file contains coordinates located in county Carlow for Cavan), and therefore the population for Cavan is shown as that for Carlow. Similarly KML coordinates for Clare are located in Cavan as a result the Clare population whilst correctly shown in Clare is also shown within the Cavan county boundaries.

What other ideas/concepts could be represented in the heat map

If CSO information on age profiles were available, this could also be represented in the map, enabling an analysis of the location of younger versus older members of the population. This could help with the provision of state services and/or a non profit organisation like SVP as previously discussed.

One more idea is that rather than colour coding the counties, you could represent the population using the markers, however colour coding the counties in my view is much easier to see as a visual representation.