Archive for the ‘visualisation’ Category

Awesome geo-visualisations from New York Times

Thursday, January 14th, 2010

Geo-visualisation and visualisation is becoming more main-stream with media publications creating more and more great visualisations. The New York times have been releasing a constant stream of great geo visualisations, they even have their own visualisation lab on the NYT site. (I’ve also notice that the Guardian has their own also.) Last week the NY Times released an interactive mashup featuring the popularity of Netflix rental across several cities in US.

Using the mashup, you change look change the titles and see the rental popularity of the movie across different zipcodes. Its a great mashup to bring interactivity to data.

I discovered that buy levitra online one of the authors Matthew Bloch has a blog page with all list of the visualisation he’s created for NY Times. Wow, what a great selection of visualisations!

Here’s a sample of my favourite visualisations.

2008 Election results – Obama vs McCain

US Movie Revenue popularity

US Inflation breakdown

Hopefully, as data is becoming more open, we’ll see more Geo-visualisations in New Zealand.

Google crawler visualisation of ZoomIn

Monday, July 27th, 2009

I’ve been playing around with Google Earth animations to test its visualisation capabilities. I wanted to see how the google crawler was indexing [business:ZoomIn]. Google indexes over 150K pages a day. I’ve been curious to see how it does it. So I created a Google Earth Animation to visualise the process. We’re in the process of uploading the videos online. (The KML is too big to embed in the browser !)

In the mean time here are some images from my animation.

Here are 3 pictures showing all the points that the crawler indexed for NZ, Wellington and Auckland. (click on the images to see full size)




Enjoy! Videos to follow.

Crime 10K – our submission to the Geo Spatial Mashup challenge

Wednesday, July 2nd, 2008


Here is our entry to the GeoSpatial Mash-up competition.The purpose of the competition was to demostratewhat different visualisations could be achieved using maps and data from the Stats dept. like census data.

The theme for our mashup is that we chosen to show  the number of recorded offences for every 10,000 residents across New Zealand.The mashup show the number of crimes highlight by colour over the different Police regions across New Zealand.

The Mashup is built on top of Google Maps API and KML. Unfortunately it highlights some of the problems in visualising data using KML. Anytime we wanted to change the shading of the polygon we had to load another entire KMZ file. This is not the best approach in building a lean mapping application.

This highlights one of the problems with KML and provide an opportunity for us to introduce Tardis – our mapping visualiation tool. We’ve built Crime 10k Tardis version using flash, to highlight how much faster and interactive visualisation can be.

Your website as a graph

Thursday, August 2nd, 2007

(This really should be your webpage as a graph. See below)

Just read an amazing post of a bunch of different data visualisation techniques and I spotted this one to visualise your website as a graph.

So I did a couple, here is ZoomIn


And here is Trade Me.


As you can see the trade me tree is a lot more dense!

You can build your own tree, visit the website as graph site and make your own !

What do the colors mean?
blue: for links (the A tag)
red: for tables (TABLE, TR and TD tags)
green: for the DIV tag
violet: for images (the IMG tag)
yellow: for forms (FORM, INPUT, TEXTAREA, SELECT and OPTION tags)
orange: for linebreaks and blockquotes (BR, P, and BLOCKQUOTE tags)
black: the HTML tag, the root node
gray: all other tags

Three in one

Tuesday, July 3rd, 2007

One of the things that we’re always trying to do with data visualisation is show more variables, while trying at the same time not to clutter the map and overwhelm the viewer. When trying to show more than one variable across a map, it rapidly becomes impossible to grasp the information in one go. Edward Tufte, on page 153 of his classic book “The Visual Display of Quantitative Information”, describes one such attempt to map two variables through interaction of two colour schemes as “a puzzle graphic”, “experienced verbally, not visually”.

But that doesn’t mean that it’s not worth the effort. The distribution and interaction of two variables across a region is inherently complex, and to expect every nuance to immediately leap out at a viewer without reflection and complex analysis is unrealistic. My experience of reading professional weather maps that show multiple overlapping contours (e.g. of pressure and temperature) leads me to believe that any multivariate geographical visualisation will require some practice to get the most out of: to learn that where the isobars and isotherms are perpendicular, thermal advection is occurring.

If two variables are too much for Tufte, then the map below would appal him: I’m experimenting with showing three census variables at once on a map of central Wellington. I’ve mapped residential population density to the red channel, density of office workers to the blue channel, and density of other workers (e.g. manufacturing, retail) to green.

Trivariate mosaic visualisation - Central Wellington

It’s certainly complicated! But nevertheless, certain patterns do immediately leap out, and the more I look, the more I see. Purely residential neighbourhoods are quite distinctive in shades of pure red, and variations of population density can be seen quite clearly. The CBD stands out very plainly, but on second glance there’s also a difference between the Lambton Quarter and Thorndon: the cyans of the former show that office workers are balanced by others (probably retail), whereas the latter is much more of an office ghetto. Te Aro is quite a patchwork of greens and earthy tones, showing that a growing residential population is starting to complement the retail, entertainment and tradition light industrial uses of that part of town.

So, while bearing in mind Tufte’s warnings, I think that this sort of trivariate thematic mapping might have a lot to offer. In particular:

  • High data density: three separate variables across densely measured meshblocks.
  • Emergent patterns: without having to explicitly code “mixed use districts” or “CBDs”, they emerge from the interaction of two or more variables.
  • A meaningful grid: census meshblocks are mostly based upon city blocks, so variations in urban form, such as the grid pattern of Te Aro versus the curvy hill suburbs, are easily visible. The familiar forms (at least to locals!) of the wharves guide the eye without explicit use of contextual layers.
  • Exploiting human visual processes. While I’d concede Tufte’s point that one has to consciously remind oneself of what certain colours mean, the fact that the RGB system has a neural reality makes it easier than a more arbitrary system would have done.
  • Metaphorical power: I deliberately chose to make light colours represent high density and black represent an absence of people, which is the inverse of the usual approach. That gives a map full of neon colours, evocative of “city lights”, and fits with my own preference for dense and vibrant cities. It also brings to mind a mosaic, with its connotations of colourful diversity. As always, symbology reveals ideology!
  • Visual appeal. Well, at least I like to think it looks good! While it wasn’t deliberate or expected, I think it’s ended up quite reminiscent of early Paul Klee.
  • Depth of exploration. While the two previous points may seem relatively trivial, a map that is attractive and evocative is more likely to bring people back to explore further than an ugly or dull map would. Every time I look, I keep finding more patterns and intriguing anomalies, and it keeps raising new questions to ask of the data.