Data visualisation – some of the best from NZ

This year at the Science Media Centre we’ve gotten really interested in visualisation of science data and began dabbling in these sorts of infographics which we create for the media at large to use.

But the ultimate extension of this type of thing is full-on web-based interactive data visualisations that let you cut the data whatever way you want, rely on robust, legitimate sources of data and which are presented in a way that makes for compelling communication. Our resident data visualisation guru, Landcare Research’s Chris McDowall has turned out some excellent visualisations this year – notably the time lapse animation of New Zealand earthquakes leading up to and following the 7.1 magnitude quake in Canterbury. A similar animation mapped a day in the life of social networking service Twitter based on the geo-location tags of Twitter users.

Now some of the country’s best interactive data visualisations have been highlighted with the winners of the Great New Zealand Remix and Mash-up competition announced today. The winning visualisations are worth a look – one of my favourites is MashBlock, which sought to graphically represent data from the 2006 Census in a very innovative way. This is from the award summary (MashBlock’s creater Cameron Prebble picked up $5,000 and the Best Mash-up award – as well as an award for best use of a Google cloud computing service with an extra $1,000 thrown in):

MashBlock is a tool to visualise demographic data from the 2006 Census for 66 Territorial Authorities, 2000 Area Units, and over 48000 Meshblocks.

This site is built to provide fast location-based queries utilising the Google Maps Geocoder, HTML 5 Geolocation and the AddressFinder autocomplete library to allow the user to find the Meshblock, Area Unit and Territorial Authority their search falls in.

The backend site is built with Ruby on Rails and a PostgreSQL database with the PostGIS plugin. The frontend utilises the javascript libraries jQuery, Raphael and the Google Maps API to create the visualisations and user interaction.

All the data is sourced from Statistics New Zealand.

Being able to drill down into parts of New Zealand to look at demographic make-up is really interesting – the data has been available publicly before now, but this makes it a pleasure to browse – the data isn’t complete for the country, but its a great base to start from.


Another stand-out was Grid Watch by Jeremy Arnold, who won for best visualisation. Grid Watch shows power usage by region and grid exit points (substations) for 2010 in parts of the North Island. Again, this is fascinating – seeing what the power usage is like in different parts of the country, and what the breakdown of power usage is like between sectors of the economy. Here’s what Gird Watch is based on:

An interactive info-graphic with map that shows power usage by Region and Grid Exit Points (Substations) for 2010. Visualizing key information about the national grid, that was previously only available in spreadsheets and zip files, and combining it from CSV extracts from the Electricity Authorities Centralized Dataset (CDS) web interface.

Grid Watch
Grid Watch

Anyway, just a couple of my favourites that have really inspired me to think about the types of science-related data that may be publicly available that could tell a compelling story about, well anything from key environmental indicators such as air and water quality, to climate change to health demographics to known deposits of mineral wealth in New Zealand. The possibilities are endless as the above examples illustrate.


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