Online Credit Card Fraud and Spending in the UK

I was recently perusing the very informative Guardian News Data Store.  As a  British news source, Guardian has a significant amount of data focused on the United Kingdom. It’s worth mentioning that they also have an ample amount of data that pertains to the global community, and a section geared just for the United States.  A data set that I was particularly intrigued by was about online credit card fraud by postcode in the UK. Identity theft and credit card fraud is a crime that happens all to commonly in today’s tech savvy world. When you look at the numbers published by Guardian, it’s rather alarming to see how much money was stolen and used by online frauds–from August 2008 through August 2009, £500 million of an estimated £46 billion of shoppers money was recorded as fraudulent transactions.

To visualize these alarming statistics, I thought it would be interesting to geotag the postcodes with latitudes and longitudes to then formulate a map in Maker!.  Below is a map I made that shows the number of recorded frauds in each postcode with a focus on London.  I chose to zoom in on London because the proportion symbols were the largest representing the most amount of frauds. Click here to view the interactive map in Maker and pan/zoom out to see which other areas of the UK had an abundance of frauds, namely Manchester, Nottingham, Romford, and Birmingham.

While I was checking out a map I made of the estimated £46 billion that UK shoppers spent using credit cards online,  I realized that there is a helpful tool when zooming into different areas of the UK.  If you hold down the ‘shift’ key, right click, and then draw a diagonal line across an area on a map, a transparent blue box will appear. The screen will then zoom to the selected area.  Below is a tutorial that illustrates this process:

Click here to view the online credit card fraud data set in Finder!.  The data set also includes total value in £, number of goods ordered, number of frauds, fraud value in £, and percent(%) of good and bad transactions.