Tag

Code Point Open

14
Feb
2014
0

Visualising customer data to provide valuable insights

In an increasingly competitive environment, customer experience is a fundamental business driver. One of the best ways to bring perspective of the customer to business decisions is by using data analysis to find correlations, isolate patterns and track trends to serve up the type of information to allow a company to tailor the customer experience for improved engagement and better profits.

DeeOpenData

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10
Oct
2013
0

OS OpenData interactive postcode viewer

We recently wrote about the work of our on our summer interns in our Labs team, Joseph Braybrook, creating a Minecraft map of Great Britain. During his time with us Joseph also created an interactive postcode viewer for exploring all 1.7 million postcodes in Great Britain.

The idea came from a demo program included with Processing – a programming language and development environment with a focus on creating audio/visual applications. The demo visualises 41,557 US zipcodes as individual points as shown in the screenshot below.

We decided it would be interesting to try the same approach using British postcodes, which are readily available as open data in our Code-Point Open product. This is a much larger dataset with almost 1.7 million individual records.

To further showcase what can be achieved with OS OpenData we also incorporated some of our mapping in the form of OS VectorMap District.

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22
Jan
2013
2

Football fan maps

We recently came across this great use of Ordnance Survey maps to display an answer to a very old question – where do the supporters of different football teams actually live?

This question has been debated for some time, but the Oxford Internet Institute came up with a great idea to solve it using digital media.

The team consisted primarily of Joshua Melville and Scott Hale and they created a map that displayed Twitter mentions (tweets) of Premiership football teams, using geo-tagging to show the fans locations. This was based on tweets/data collected between August 18 and December 19, 2012.

Initially the team used pinpoints or dots to show each tweet/mention, but the data quickly overwhelmed the map background, so the decision was made to aggregate the locations to post code areas. This proved a more effective way to display their findings although more processing/geographic data was needed to achieve this.

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