Google Fusions Tables

In this blog I will construct an Intensity map for the Republic of Ireland with data received from the 2011 census. Also providing the readers with an informational  guide on achieving your own map in Google fusion tables. Along with information such as merging,layering,gleaming data and concepts on data that can be abstracted from similar intensity maps.

Irish Population Intensity Map

Below is an image of a Intensity map representing the Republic of Ireland’s population and boundary lines. Each county is divided into male,female and total population. Highest populated areas are represented by a vibrant red colour gradually fading as the population reduces whilst light blue represents the least populated counties.

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Achieving Intensity Map

To achieve the intensity map for the Republic of Ireland I first had to source the correct data sets. I needed population files(Excel) and county boundary lines(KML).I downloaded the appropriate files needed to complete this task.

I was now ready to open Fusion tables and begin to upload my population data. This data was opened in spreadsheet format. I read trough the data sheet checking all data was input and correct for male,female and total population I also noticed that Laois was spelled incorrect and would not merge with Laois boundary lines after correcting this error I repeated this process for my KML file.

I now had two separate files uploaded on Fusion tables and needed to merge my data creating one single file. I matched both files with county names and began to merge.

When merging was complete I opened map geometry and checked the merge was successful. I still needed to make my intensity map more visually effective.

I began to bucket fill counties depending on total population. Red was visually prominent against the blue  and became the most heavily populated areas . All other counties decreased from dark blue to a lighter blue in colour.

I also wanted to see the stops and coverage of transport offered by the Luas trams in Dublin City Center. I downloaded Luas stops in KML file and uploaded to Fusion tables. This file needed to be layered over the original merge I created earlier. This was achievable by using the Fusion table wizard and layering both files one over the other.

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Gleaming Data from Intensity Map

Now that my intensity map has relevant data and structure we can begin to gleam information that can be used in relevant situations. The map highlights in red the highest populated counties in this case been Ireland’s three main cities(Galway,Cork,Dublin). Its also clear to see the coastal counties contain the highest populations and this gradually reduces the further inland you move. The Luas data within Dublin City allows the end user to view stop points and facilities. Aiding in future expansion and development where population is high and transport is low. Other data that may be relevant are employment rates and vehicles registered. If the goal is to reduce vehicle congestion and vacilitate employment transport.

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what other ideas/concepts could be represented in the intensity map

Intensity maps have the ability to show large clusters of data in a simplistic but effective way. Data formated this way has many uses such as merging data for business or just visual effect.

Intensity maps can be used to define the progression in govermental elections,religion,education,wealth. and much more if a large grocery chain merges data for all  major grocery chains within that area. This data could be very useful if they’re future plan is to find a location for your new retail store. Population of the area and proximity to your nearest rivals could be critical in your decision making. Also with the ability to create a map with the locations of your segmentation preferences.

I sourced a map highlighting Hispanic origin within the various counties in Texas. This information is key if your agenda is to open numerous Mexican restaurants within the state of Texas.
texas

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