Team Data OutroSystems & Tools 10 February 2015 | 9.21am
A while back we provided you with an introduction to our team from Monash University, Team Data, and the background on some of our progress. Arup and the Monash University Industry Team Initiative set us, a group of four multi-disciplined students, to perform reconnaissance work on digital.Arup’s data projects over a three month period. We’ve been upgrading, developing, analysing and conceiving part of what Arup’s digital future should look like, and bolstering the importance of a company like Arup to be engaging with the right data to make its processes more efficient, and to gain insights that answer unimaginable questions.
The last time we showed you BIM Dashboard it was an adolescent, it’s now in early adulthood. Visually the application is more direct in its functionality, with distinct analysis and filtering planned, a step up for users, from its more powerful ‘pick-anything’ filter/sort structure. Our current demo is blitzing through our data and displaying things the way we want it to. It’s important to note this kind of work requires getting interaction visually and technically right—so you can see clearly and see on time.
Proposed analysis palette for BIM Dashboard.
Proposed filter window for BIM Dashboard.
Project level view on the BIM Dashboard web application (work in progress).
Model level analysis on the BIM Dashboard web application (work in progress).
The time slider on the BIM Dashboard web application (work in progress). A recurring filtering tool in our applications.
We’ve also been tasked with visualising trial sensor data the City of Melbourne is collecting in urban areas in collaboration with Arup and The University of Melbourne as part of Creating a Smart City through Internet of Things. The purpose of the data collection is to help researchers understand and communicate the effect of canopy cover on the urban environment and its effect on cooling. At this point, our group’s methodological approach has greatly improved, and we believe our GIS approach clearly demonstrates the information the sensors are out there to collect. A challenge we face when creating visualisations is to appropriately ask and answer: what’s the right data to pick, and the right way to show it? There is a fine line between creating an easy and a hard to interpret digital visualisation, and this is why we’re a design and programming team.
A screenshot of our progress on the data visualisation for Creating a Smart City through Internet of
Things. Before the visualisation helps researchers, it’s helping us understand particular issues with the external data collection.
During our time, we’ve touched on four main projects, and additionally assessed the same amount of projects on top of this, each with their own purpose and set of requirements. As we get to grips with the data that’s available to us and figure out how best to show it, it curious to note the consistencies we’ve discovered across the projects we’ve worked on. This in itself presents a future for data visualisation, and not just for this company. Data visualisation can be interpreted as a simulation of what’s happening in a particular context and it’s happening in time, because we’re simulating growth, decline and events of a particular subject in time—it’s the most absolute unit for progress or movement. It’s also happening in physical space or intangible space.* In short, we’re dealing with a subject in time that can either be mapped spatially or not. We’re doing this to identify trends and patterns, or the lack thereof, to gain insights or realise anomalies within a context, which amounts to very useful information. Given the similarities, in the future we imagine combining spatially mapped hazard data with spatially mapped engineering observations, or correlating a database of model files against another with financial data in an application like BIM Dashboard to gain even more useful and more accurate information. When there are well established data processing and visual design frameworks and inter-connectivity between applications, we could get even more definitive answers and just ask: what visualisations can we cross-examine?
To conclude. Thank you Arup for letting us play with your data, and we hope this progress grows well into the future!