Data Visualisation - Canberra income by postcode

This is an October 2010 data visualisation project to develop prototype interactive charts undertaken as part of the Master of Digital Design.

Interactive Analytic Charts

This visualisation is rather a set of linked visuaulisations, developed to provide analytic context and allow (encourage) the data to be  approached from multiple points. The data set is 2003-04 average incomes by postcode compiled by the Australian Taxation Office, mashed up with a list of suburbs by postcode from wikipedia and a set of suburb boundaries which I traced myself.

Concentration of higher average incomes is clearly shown to be in older suburbs close to the centre

Subsequent rings of suburbs have progressively lower average incomes further from the centre

The main chart is a bar graph of average incomes by postcode - it is arranged by default by postcode, which relates approximately to the age of suburbs in that postcode, but can be arranged by average income rank. The population of each postcode was in the original data set and is indicated here by the width of the bars. This can be turned off, but is very useful for visually comprehending the scope of the data set. The chart also usefully has marked the Australia and Canberra wide averages.

Mousing over a suburb in the map or a postcode in the main chart brings up a detailed information box which in addition to the figures from the data set lists the suburbs in that postcode.

I have additionally added two small analytic charts - a histogram showing the spread of postcodes by average income (there are only a couple with high averages) and a summary bar graph of average incomes by region. Both of these are also interactive and can be used to assist navigation - mousing over highlights all relevant postcodes in the main chart and  in the map.

A consistent colour scheme has been used across all charts to allow intuitive reading of income concentration without needing to mouse over.

Together these charts encourage further exploration and reveal a richer narrative than any would individually - and are more informative for the mashed up additional data.

2615 in West Belconnen is the only postcode below the Australian average

Hall as a small village with it's own postcode is easily identified as an outlier

All postcodes in South Canberra region highlighted showing range of average incomes between postcodes

Income bar graph rearranged by rank without population weighting for width - no surprises the highest average incomes are in 2603 which covers Forrest and Red Hill

The visualisations show as expected that Red Hill and Forrest has the highest incomes. They also show clearly subsequent rings of decreasing average income - this is a text book diagram of most contemporary cities. I was pleased to discover outlying items such as how well off Hall was and that West Belconnen was the only postcode below the national average.

However these visualisations are also a clear demonstration that no matter how neat the visualisation is, they are always constrained by the quality of the data. In this case, postcodes are not very fine grain. It would probably be much better to do the same visualisation with suburb or even street level data. For example Griffith is in the same postcode (2603) as Forrest and Red Hill but is not nearly as rich as Yarralumla. In West Belconnen (2615) there are some suburbs such as Flynn which would be much richer than suburbs such as Page and Scullin, which are in a postcode (2614) with rich suburbs such as Aranda and Weetangera. At a more zoomed in level it should be apparent that in suburbs such as Melba and Hawker there is a substantially richer end - on top of the hill. Canberra demographics are further mixed up anyway, with planning and social policies mixing public housing and units suitable for first home buyers throughout most suburbs.

Any data that summarises, makes averages etc should be read with caution - yet it is necessary to find patterns. Therefore a strategy of showing everything available, with as many different views and levels of zooming in, out and between as possible, must be pursued to ensure that data is read in appropriate context.

This is another project I have revisited in thinking about the project for the NMA collections. It is my most refined prototype of the analytic map as interface. Here I have visualised the data in multiple analytic ways simultaneously so that a user can have many hooks for exploration and easily locate individual data within the context of the whole data set. The suburb map and the summary bar graph of average incomes by region are examples of where appropriate mashed up additions can provide richer context than was immediately in the data set.

The analytic map as interface

Proposal for this semester's Master of Digital Design project, which can be followed by the unit tag 8199.

I propose to build a simple analytic map to contextualise and make navigable in a browsable way the National Museum of Australia’s digital catalogue. Beginning with an overview and allowing zooming in to detailed tiles, maps assist the location and navigation of data by succinctly visualising complex relationships and structures. Additional context can be provided by simple analytic charts that further reveal relationships within data sets.

With the current online interface to the vast catalogue it is difficult to know where to begin browsing, it is impossible to comprehend the whole collection (scale, structure etc) and there is little context to an individual object.

My principles will be to start with viewing everything in a way that reveals structures and relationships to suggest themes to narrow viewing focus and filter the data set, and once viewing subsets or individual objects, provide context to locate them within the data set and suggest other related items to browse.

I don’t propose to build an interface such as this because I think it is particularly original – but because I am genuinely interested in personally exploring the NMA collection myself, and because I am curious to study how visualisation techniques scale.

A vast collection

The NMA collection is vast – both in total items (more than 200,000 objects) and in variety of content. On their website the NMA describes the themes of their collection as Aboriginal and Torres Strait Islander cultures and histories, Australian history and society since 1788 and people's interaction with the Australian environment, which are sufficiently broad to cover just about anything.

NMA's current online catalogue home page

NMA's object record view - often there is little information about the object or the collection it is a part of 

I previously observed that the online catalogue is not curated, and that most objects and collections are not given a contextual description that explains their significance. However the NMA does have a separate section of the website where recent acquisitions and the highlights of the collection listed under the three broad themes above are given significant contextual narrative documentation. Identifying and visualising this subset would be great as mashed up addition to an interface because it is in the Museum’s opinion the most interesting content, and more critically it is the most completely catalogued. It therefore might also be a useful home/landing page, particularly if the fully zoomed out view of the entire set is not legible.

Mitchell Whitelaw has been developing visualisations of similarly large and diverse data sets – the National Archives and Flickr Commons. Here ranking assists us to find top and bottom items, but unless already zoomed into a small subset, it can be difficult to locate middle items. Word clouds that visualise the most frequently used words in object titles, are useful in narrowing focus on content themes – Mitchell says that coverage can be between 75% and 95%, but there are outliers that are invisible. How do you locate these hidden objects?

Questions of organisation

I intend to organise browsing and zooming in around questions that I am personally interested in such as:

• Which are the biggest/smallest objects? 
• Which are the oldest objects? 
• Which objects are there the most of? 
• Which are the largest collections? 

Some questions that I would like to ask, but I doubt the public data set will have answers for, include:

• Which objects are on exhibition? 
• Which objects have never been on exhibition? 
• Which objects are the most fragile? 
• Which objects are currently the subjects of restoration work? 
• Which records are newly added to the catalogue or have been recently updated? 

Finer grain filtering can be facilitated at the intersection of these questions – for example ‘show me old small objects’. I hope that using multiple filters in conjunction will help to find hidden objects.

Two data types that I suspect can provide interesting browsing links between collections are object material/s and associated location/s – both are linked from the current online catalogue records, but would be much more useful if they were visual and had an indication of quantity - for example ‘other objects associated with this location: 5’.

Ultimately I would love to end up with a unique visualisation. However I dont have anything particular in mind at the moment and am not going to try to think of something arbitrarily. I would like to let visualisations emerge from exploring the data. My plan is to start very simply, with what I have outlined above, and then let the data prompt subsequent questions.

A native of the web

After encouragement from Mitchell, I have decided that rather than work for most of the semester in Processing, where I am confident I could achieve a well resolved visual interface, it would be better to migrate early to native web formats that I have not worked previously with and risk less resolution but benefit from the significant challenge of learning and plugging together back end technical systems.

So I will need to translate from Processing to HTML5, CSS and JavaScript. Then I will need to ensure the large data set does not crash the browser, which can only work with limited memory. I suspect that I will have to set it up to load dynamically, which will require a MySQL database queried with PHP or Django. I am leaning toward using Django because it is built on Python, which I think I am likely to learn anyway in the future for Rhino 5 or other applications.

Ben Ennis Butler has suggested some clever potential work arounds for interactive web implementations of static visualisations (ie visualisations that dont require access to a database and are not redrawn dynamically), which I can fall back to if I get stuck. He did this for the histogram he designed to show the Australian prints collection at the National Gallery of Australia.

Ben Ennis Butler, histogram of Australian prints collection at NGA

This visualisation is exceptionally browsable and well suited to the scale of the collection. I am tempted to do a similar visualisation first as a test of how well it can work for a dataset the scale of the NMA collection.

Show everything

The 'show everything' approach has been advocated by Stamen, as well as Mitchell. The approach is to start with a view of everything and then zoom in and filter to subsets and individual items, facilitating a better comprehension of the scale of the entire data set and the position of an individual item within it and encouraging browsing by showing related items.

Stamen's SFMOMA Artscape does this very well, but only for a collection of 3,500 items.

SFMOMA Artscape by Stamen - zoomed out

SFMOMA Artscape by Stamen - zoomed in

Constructing the visualisation like a map with pre-generated tiles, the interface is slick. However this set up appears to limit dynamic rearrangement of tiles, leaving the user stuck with the preset ordering by acquisition date and not able to filter to a subset - searching or following keywords, artists etc allows you to zoom to items one at a time, but not able to see all subset items next to each other or skip ahead to particular items.

An interface for users

Finally, at the end of this project, if I have a working interface, I would like to do some user testing. Documenting how users explore the data would be a significant outcome that would assist developing design approaches to future visualisations, both in general terms and specific to the NMA collections.