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Liveable Sydney @ Carriageworks


Arup recently hosted a panel discussion on whether or not Sydney is one of the world’s most liveable cities – and what we can do about it. The guests and panel were diverse in background – creating lively debate from state, local, youth advocacy, social and private sector interests. A summary of the liveable Sydney panel can be found here.

Alongside the event were these visuals, which were largely fuelled by data related to this previous post on accessibility modelling. For me, it was exciting to see how having statistics so up-front, vibrant and personal enabled positive discussion and conversation both before and after the talks (regardless of how grim some of the figures are!).

Liveable Sydney? from Arup Australasia on Vimeo.

Setting the course for machine learning

Last week, a bunch of Arupians participated in a three-day crash course in Machine Learning, developed in partnership with the University of Technology Sydney (UTS) Advanced Analytics Institute. The course was run by Dr. Richard Xu who directs the university’s Machine Learning and Data Analytics Lab (and who is not shy to include a few complementary ‘big data’ jokes).

Our team’s individual backgrounds were diverse — ranging from spatial sciences, software development, geology, transportation, structural engineering and building physics. Although from different fields, we all had a common interest and purpose in learning the skills to apply and scope out Machine Learning and Artificial Intelligence projects within the built environment.

A brief summary of reflections of the experience and applications to the built environment can be found here.

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Measuring accessibility – on the 30 minute city


One of the recent projects I’ve been involved in at Arup has been developing spatial, analytical tools to understand transport accessibility. In particular, this is to do with destination-based accessibility – so rather than assessing how well-performing a city is delivering transport at particular points (which could go anywhere), we looked at how this performs delivering to all other places in the city. In particular we were looking at places that are important to creating liveable environments – such as to education, parks, healthcare and our jobs.

For me, this topic was building well on research I had done in 2015 (See ‘Where to From Here? A Modelling Methodology for Measuring Land-Use and Public Transport Accessibility in Melbourne), which assessed destination-based accessibility within transport modelling software, restricted to travel zones. This time there were some major improvements to the method ; mostly from removing from a software shell to raw code, and much more disaggregate units of analysis.

We assessed Greater Sydney Sydney at a 300m x 300m grid level, producing over a million travel time isochrones for driving (including traffic), public transport and walking to assign accessibility values to liveability variables in approximately 120,000 small cells in the city. In a nutshell, our toolkit involved a bit of OpenTripPlanner, Python, Amazon Web Server and FME – all using Open Data sources. This means means the method is highly reproducible for both other cities, and applicable to the same city with a different network (which, could be used to evaluate transport network changes, or alternate land use scenarios). A web map has been produced to showcase some of the work done in this space is so far , exploring what the ’30 minute city’ means for Sydney:



It is certainly exciting to see the potential of this thinking and method being applied to both Sydney and other cities. Accessibility and the impact on individual opportunities is often overlooked and undervalued in many forms of transport analyses. With the increasing richness of the data that is becoming available from the Government and other forms of Open Data; combined with open analytical and visual methods like these it is encouraging and clear that these analyses can potentially produce insight towards tackling some of our growing issues in Australian cities, such housing affordability, transport disadvantage, sustainability.