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Warhol | Weiwei 2016

Andy Warhol | Ai Weiwei, developed by the NGV and The Andy Warhol Museum, with the participation of Ai Weiwei, explores the significant influence of these two exemplary artists on modern art and contemporary life, focusing on the parallels, intersections and points of difference between the two artists’ practices. Presenting the work of both artists, the exhibition explores modern and contemporary art, life and cultural politics through the activities of two exemplary figures – one of whom represents twentieth century modernity and the ‘American century’; and the other contemporary life in the twenty-first century and what has been heralded as the ‘Chinese century’ to come.



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GOVHACK 2015: Melbourne, where are we going?

The following is the entry I was a part of for GovHack 2015, an Australian national ‘hackathon’ to do cool, fun things with data. Our team developed an interactive data viz called ‘Melbourne, where are we going?’ – the project description and link are below!



‘Melbourne, where are we going?’ is an interactive web visualisation platform that allows the user to explore the relationship of both transport and housing in Victoria – an area where Melbourne and many other Australian cities face many challenges. This is achieved through the numerous open datasets related to transport, the built environment and socioeconomic indicators.

This is an initial prototype targeted as policy / planning tool, a tool to communicate upcoming issues to the public, as well as just an interesting way to communicate data that affects us all. For this reason we have applied for a number of categories related to planning, governance, policy, data journalism, innovation and connected communities.

The interactive visualisation utilises the recently released PTV General Transit Feed Specification (GTFS) data, extrapolating it into a temporal visualisation of the public transport network movements over time. In this demo, we simulate the approximately 500,000 train stop arrivals that occur throughout the day in Victoria, as a proof-of-concept for the whole transit network.

At the first instance, this enables the data to be seen in a much more engaging way, and at a network level rather than simply a timetable format. This allows users / policy-makers to see how well-serviced an area is during different parts of the day. We all live different lives and adhere to different schedules, so this can firstly help match our lifestyle choices and personal circumstances (such as employment industry or location) with where we live, where we’d like to live, or (if we are a policy-maker) where we should be planning people to live.

One of the factors that distinguishes this between other GTFS-related visualisations is the next dataset. This tool integrates Building Permit Approval data from 2011 to 2014, which is used in order to measure growth in urban development. This allows us to compare where the city is growing in comparison to the level of service the transport network is currently achieving. The challenge with this data is in its geolocation to integrate with the mapping format. This data is very detailed, yet the lowest level of geography provided is street. Here, we mashed the data with the approximately 4,000,000 points in VicMap Address data in order to create an address-weighted estimate of the geolocation of these building approvals over time at a point level. The data was cleaned and filtered to be those used is only for those able to be geocoded for these sets, and only for domestic / residential dwellings.

The third dataset used is the VAMPIRE Index, developed by Griffith University and provided by AURIN. This index can be used to help describe the issue we are explaining and provides a robust description to face, and explain the situation in 2011 and whether or not this is getting worse over time. This index combines our narrative between transport and housing – providing a robust base on which to make sense of the data post-2011.

Future Development: The GTFS data feed is now available in most Australian cities, and would further be applied to create similar tools that are tailored to their transit agencies. Other Australian cities also have similar building permit data, and the VAMPIRE index has been developed by Griffith for multiple as well. As both the GTFS data and the building permit data are regularly updated feeds, if they are collected and archived over time we will be able to further develop this tool in order to track these relationships over time.


Dissertation research


My research interests earlier this year began in understanding what drives certain behaviour in the urban environment – specifically in what changes to transport systems do in affecting people’s choice to use public transit. It’s been a long haul, but today marks the completion of this project, titled ‘The use of automatically-collected transport data in the spatiotemporal analysis and visualisation of policy change: a case study of the San Francisco Municipal Railway’. Here I will provide a bit of an overview for those who might be interested.

There are a wide array of technologies within our urban environment that are constantly collecting data. Many of these systems are still in relative infancy, however for transit systems have been among the earliest to be implemented and mature. While many of these sensors, particularly in transit, are used for real-time decision making, there is an increasing interest in the use of this data for retrospective analyses. Studies over these long periods provide improved means in understanding changes in behaviour, as they can potentially be provided with enough data to achieve statistically sound results.

The dissertation focuses on utilizing data that has been collected throughout the public transport network of the City of San Francisco. San Francisco are innovative in their approach towards collecting data to improve their public transit network. In particular, they have been collecting detailed data of their bus network over the past five years. This data includes automatic-vehicle-location (AVL) and automatic-passenger-counting (APC) data. This vehicle location data, which many people are exposed to every day through ‘next bus’ information in signs or on their mobile devices can provide very detailed information on the transport system.

When archived over large periods of time, it can draw a picture of how the systems has been performing and how it has changed, from a stop-level, to route-level and system-wide perspective. There are many performance variables that can be deduced from these samples of vehicle location – including the how often the bus arrives on time, the speed of the bus and the waiting time for passengers. While this gives us an indication of the vehicular performance of the fleet, we can also ascertain an understanding of passenger experience, and from their ridership changes, how this experience affected their behaviour.


This is achieved through APC data, which people may be less aware of. This data is collected through laser beams above the door of the bus which perform count calculations of how many people entered and exited the vehicle based on how the beams were broken. Counting data could also be used from information sources such as fare-collection boxes, where you touch on your Oyster Card or Myki.

The dissertation uses these large datasets to assess how both people and vehicles changed over time throughout the city. During the research, it was decided to focus on one particular policy where a large change was hypothesized to have occurred in the system. A number of changes were identified, while a number of variables also appeared inelastic to the policy change.

As part of this dissertation, two specific visualisation and analysis techniques were employed. Firstly, this involved the development of an interactive visualisation tool which enabled rapid exploratory analysis of the data. Secondly, two networks were created to represent the data – one where all consecutive bus stations were linked, and the other where all bus stops with a shared route were linked. With these networks, a number of graph-theoretic attributes were tested, weighted with the data at different time periods – including degree, betweenness centrality and efficiency.

Throughout the process it was found that the policy changes and effects were able to be portrayed in the automatically collected data, however not without its own challenges and potential inaccuracies based on its size and sampling methods. Further research in this field could involve utilising similar tools and methods in understanding at a smaller time scale, such as the system response after a tube strike or natural disaster. If you are interested, pieces of this work feature in the publication listed below and the full dissertation will possibly be available in several months after marking at CASA, UCL library.


Lock, O. 2014. The use of automatically-collected transport data in the spatiotemporal analysis and visualisation of policy change: a case study of the San Francisco Municipal Railway. Diss. University College London (UCL). London, United Kingdom.

Erhardt, G. D., Lock, O., Arcaute, E. & Batty, M. 2014. A Big Data Mashing Tool for Measuring Transit System Performance. The Big Data and Urban Informatics Workshop. Chicago, Illinois, USA.