Transcript
You are listening to an MPavilion podcast. Conversations about design and the world we live in. For more visit our archive at mpavilion.org and subscribe wherever you find your podcasts.
Bonnie Shaw:
Good evening, everybody. Thank you all for joining us out here in this absolutely gorgeous evening in this absolutely gorgeous pavilion. It’s so special to actually be out here face to face as humans. This is certainly my first time in a almost room with a large group of people like this. And it’s yeah, it feels pretty special and a bit scary. So I’m Bonnie Shaw. I’m the co-founder at Place Intelligence. We’re an agency that works with big data and a deep respect for planning and city design to produce evidence, to support decision making and city design work. And we’ve partnered tonight with our good friends at Arab and Hassell to bring you a series of conversations about data and about knowledge and about the role that city design plays in revitalising our cultural institutions and our cities.
Bree Treverna:
Just as we begin, I would like to acknowledge as traditional custodians of the lands that we are on today, where people have been making, discussing and building for many thousands of years, we celebrate the diversity of aboriginal people and their ongoing culture and connections to the lands and waters of this country. We pay our respects to elders, past, present, and emerging, and acknowledge any aboriginal and Torres Strait island people with us today. And as I was coming to the event tonight, I thought I need a notebook and I haven’t needed a notebook for a long period of time. Because I’ve just written on scraps that are sitting around my desk, which I eventually throw away. And I found this one and I grabbed it in a hurry, but it’s actually a guidebook. And as I was walking here tonight, I was thinking about just the different ways that we engage with information and how this conversation started.
And certainly the conversation with Bonnie began through a discussion about this return to the city that we are having at the moment and the different kinds of information and data that was suddenly coming forward, telling us different ways about how we were living, how we wanted to live, whether we should continue to live in that way. And then the conversation very much expanded with the discussion with Annie, from Hassell around or whose voices do we do we hear in that? How do we understand how our city is feeling? What is the temperature of our city and what can we do with data to help understand that? So in that spirit, we’d like to help build on that conversation tonight. So we would encourage you all to be vocal, to ask questions, we’ll be asking our panelists to be building on things that others have been saying. And tonight we’ll bring you three different conversations.
Bonnie Shaw:
So the first conversation tonight will focus around data as illumination and the role that data can play in revealing new insights and new knowledge about our cities. As we kind of grapple with some pretty interesting challenges.
Bree:
Then we are going to talk about some of our knowledge and cultural institutions and the way that they have helped us to understand different kinds of information flows. The voices that we are hearing through that. And then finally a conversation around how design can help us to engage different communities and have different types of conversations. We’ll also be having some Q and A in between our sessions. So we’d encourage you all just to get comfy. As we bring our first panelists up here, we’d also encourage you orders to catch an eye of someone that you don’t know. It’s all a bit awkward. Use that social awkwardness to introduce yourself to someone that you don’t know. Say hi, and look forward to a great conversation.
Bonnie Shaw:
Thanks Bree.
Bree:
Thanks.
Bonnie Shaw:
So I’ll introduce our first panel if you guys want to come up.
So our first panel is focused around data as illumination and we’re joined by four incredible data experts and city makers. On my left here is Norion Ubechel, he’s the CEO and co-founder of Place Intelligence. Norion’s got 15, 20 years of experience working across place making and campus activation and asset management, data science, and brings that to the invention and delivery of some incredible data driven tools.
Next up is Marion Terrell Marion is a senior expert in all things, government and city data. And she’s been leading the cities and transport program at the Graton Institute for the last 15 years. Next up is Sarah MacArthur, the newly appointed acting director of City Lab, the city of Melbourne’s innovation team, where she leads a whole range of incredible programs, looking at service design for better city impact. And then finally on my left is Niels Volta, who is a senior interaction designer at Paper Giant. And Niels brings an incredible degree of experience as a researcher, academic and practitioner, working with data and technology across cities. So please welcome them.
And so I wanted to start the discussion tonight and throw it open to the panel with a question. And Sarah, I’m going to start with you around what these new kinds of data sources that we can access are teaching us about our cities that we didn’t already know.
Sarah:
I think so many things like it’s we want to dig into this a little bit more, but with Sharon Madden’s comment from her new computer, you know what, from a new book, what we’re using the metaphor of the city as not a computer for and what kind of, or the way that our language and the way that we view cities and what that actually tells us about what we can draw out of it. So how do we look with new eyes? I think at what the data is actually telling us, how do we find new metaphors to our actually describe what’s actually happening within cities and also think about not just past and what we’ve gathered previously, but how do we actually start to think about the future and the ways that we can map place and what those desirable or preferred futures actually look like and bring that into the mix.
Bonnie Shaw:
Amazing. And you do a lot of work around speculative futures. Do you want to maybe explain a little bit about what that’s about and what kind of data you might use in that work?
Sarah:
Yeah, I think one of the things I’m working on right now and working with RMIT to bring forward a master’s course, which truly focuses on the idea of imagination. And if one of the core tools that we are looking at is a lack of ability around finding collective imagination, collective sense, making and collective decision making on our futures. How do we find ways to encourage people and give them the tools to feel like they agency within futures? I think futures is something that is often seen as the domain of a few or, you know, powerful or, you know, whether it’s, you know, technocratic societies, government, whoever, how do you find ways to find diverse and pluralistic futures to encourage everyone to have an ability to shape what that is. And do you make sure that they’re not relying on used futures or futures that have been presented to them in many different ways, like media culture, film, or influenced in other ways to really find what their desire is, and then pull that forward to help make decisions.
Bonnie Shaw:
Awesome. And so this idea of kind of opening up access for participation from everyone. Norion, I’ll throw to you and maybe, a similar question. What access to these new data sources, what do you think it’s changing about how we’re designing cities?
Norion:
Thanks, Bonnie. Great to be here. Thanks everyone for coming out and thank you for the fascinating insights. We’ve been working really hard to try and understand how we can aggregate really large data sets at national and international scales and machine through that. So we can find patterns in places and precincts and local communities and all the way up to the macro scale. And then of course, keeping in mind the locality of place, how we understand our local context, our local communities, and bring that together in unique ways that we can generate insights to power design-based decision making. And I guess the interesting thing there is with so much data that’s out there. The question is around building the tools that we need first to unlock that data, but of course building the knowledge and literacy and the design and planning community of how to approach that data in the first place.
And so we’re seeing this first wave of that happening globally at the moment. There’s a lot of democratisation of mobility data and qualitative data from social media, all kinds of information that’s now accessible, but the question has always been, how do we find insight and meaning in that information, as opposed to just, you know, death by data. Often we present these large, you know, complex reports with every metric under the sun. And it really comes down to those very salient sound bites that’s going to allow a decision maker to have more confidence in what it is that is being prescribed. And so I think in that respect, you know, as we move towards from, you know, hindsight diagnostic analytics into insight and predictive analytics and then finally into the artificial intelligence piece and machine learning around looking at big patterns, which is, you know, like Netflix is recommending a movie, computers might very well recommend potential patterns or solutions that you might like to look at. But it is of course, the role of the designer as the prescriptive analyst, which you’ve always been prescribing the future conditions for societies to use that information in more meaningful ways.
So of course the goal here I think is to unlock data and then learn how to work with that, to create meaningful and tangible benefit.
Bonnie Shaw:
Amazing. And Marion in your work, when you are producing these very complex reports on the state of how the transport networks are functioning and the roles that plays in our cities and economies, how are you seeing those, the data that you’re using and the insights that you’re producing being used by the audience that you’re targeting.
Marion:
So thanks for the question, I guess what I’ve, there’s been a lot of downside to COVID, but I think what the real upside for me has been is the absolute explosion in real time data. And so we’re getting, and it’s not, not just for me, but I think decision makers are relying on, they’re looking. There’s no point in knowing that there’s a recession a year after it’s happened. People really want to know right now. And so governments have become more innovative as well. Looking at things like payroll data, restaurant bookings, mobility data, visa transactions, and shipping movements, and many, many sources. And each of them is quite imperfect. And I think that’s partly, there’s been a bit of prejudice against using them for that reason, but there’s an enormous strength in being able to triangulate and use multiple sources so that even though they’re imperfect collectively, you can get a much more responsive picture of what is going on.
And at a time like we’ve just been going through and are continuing to that’s. It just gives us an enormous amount of policy grunt, I suppose, that we didn’t have before. So, so I’m trying to use that a bit. I did do some work probably four years ago using Google maps data and scraping journey times over a long period of time. But these days it’s much easier. There’s lots more mobility data. And again, each, each data set’s got its own peculiarities, but the more you get, the more you can overcome or find out that, but by using multiple, you start to converge on some insight that then you can rely upon.
Bonnie Shaw:
So I’m picking up on your use of the word perfect and Niels you probably know where I’m going. So you were heavily in the science gallery, obviously you were heavily involved in the science gallery’s perfection exhibition and built the mirror. How are you seeing the use of kind of perfect and imperfect data sets being used in city making?
Niels:
I really get the easiest question to start with don’t I. Look, I think a lot of my work is centred around ethics and how we involve the public in discussions about ethics. And obviously once you start talking about ethics these days, the word data comes up straight away. And there’s something really interesting about how we engage with data. Do we just look at data sets and take them for granted? Do we immediately inform a decision, say a new transport network or a massive government investment in transport or yeah. New opportunities to improve the mobility and to improve access to the city, or are we actually going to engage with the public? And it’s my belief that data in itself doesn’t really tell a story. It gives you lots of data points and it gives you lots of opportunity to develop a story around, but it’s really the public that you want to engage in that conversation and help them understand, first of all, what data they have produced over their life or while they’re commuting, or as they’re using Google maps.
And they are really the, sort of the sort of source to help you build that narrative and subsequently inform policy. And as you point out, Bonnie, a couple of years ago, we developed this sort of thought provoking, I should say, prototype. So an AI that can read your face and that sort of spits out a number of assumptions about who you are as a person. And even though that’s, that’s very far away from what we are discussing today. So urban data and how data informs urban design, the narrative is exactly the same. And that is that the public simply does not understand what data means or what sort of consequences, fast stretching consequences data can have. I might be perceived to be aggressive by my algorithm. And that might automatically rule me out from say certain types, certain types of employment, or I might be automatically surveilled by police. I’m not saying it happens, at least it doesn’t happen necessarily in Australia. It happens elsewhere. But I think when it comes to urban design and data being used to inform urban design, I think we have to follow the same sort of yeah precautions and be conscious that there is a person behind every single data point. And it might just be a single data point at a bit of an outlier, but that’s really interesting for an open design sort of yeah proposal or idea.
Bonnie Shaw:
Thank you. Alright. I feel like everyone’s kind of loosened up a little bit now, if anyone has questions and would like to jump in, please feel free. Hey, Sarah, I’m going to come back to you because I know you did a whole lot of work recently around the Melbourne vision and used a lot of qualitative work and story with, with the community in Melbourne. Can you talk about a little about how you might bring qualitative data and quantitative data together to tell a compelling story?
Sarah:
Yeah, I think building on what you were saying, you know, the idea that data doesn’t tell all the story and that the stories are, you know, embedded within people and, you know, bringing quo and quant together and more than just sentiment, but actually deeply really understanding in particular, you know, at the neighbourhood level, how communities can own their own story and their vision is something that we’re actually headed towards now. So as you were saying, Bonnie, we worked with community to build the 10 year community vision.
Something that each council does to help understand how councils can deliver on the aspirations of community. But we’re now really taking that down to the neighbourhood level and understanding how, how those communities can actually build their own visions and what it actually means. And also how you give people ownership of those as well. So that it’s not just something that’s, that’s owned by government because we want to, we want to be able to share, you know, what, what that data means and empower people again, how do you help enable them to realise their own individual aspirations by building capability and capacity within communities to help enable them in the future as well? So I think, you know, there’s, there’s the role not only for, for government to pull those that quant together, to be able to see the power of that, but that’s also about empowering communities to be able to understand use and enable their own futures with that data as well.
Bonnie Shaw:
And Norion, you guys should feel free to ask each other questions as well. Don’t wait for, for me to jump in, but you talk about kind of understanding data and cities at different scales. And Norion that’s a lot of what you talk about opening up access and democratising access to data. What can you learn from the data that you access and use at different scales? So like a regional macro scale, right down to a local community.
Norion:
Thanks Bonnie. So the question was how do we understand context through different scales or resolutions of data and how we might be able to apply that in different scenarios? Okay, well I think, you know, there’s, there is this globalisation factor at play, right? Cities are very like each other. And so there’s a bit of almost too much similarity that we might find. And, and one of these concepts of, you know, COVID and lockdowns, and that there’s a hidden benefit there of understanding that there’s this context in place and people have to pause and be in their local environment. And of course that, if you think about culture, where culture comes from, it comes from people being in one location for a long period of time. So maybe it was a bit of a pause to reflect back on our local culture.
Which then of course starts taking shape into our local communities. And we start thinking about the context of our place, our walkable catchments, our nearby parks, the level of exposure that we can have with other people. Then we’re stepping up in scales thinking about, you know, bigger precincts, broader communities in and around that, and then how, how these places are actually distinct and different from one another.
Of course, cities are very much like brands on one, in one respect, they’re competing against each other to attract businesses and offer the highest quality of life so people want to live there. And so all of these different resolutions or scales are important, I think to consider when we’re building data narratives that people can use to inform design and decision making processes in all different aspects of city building. And so I think we have to be aware of the local context, but we also have to see the macro patterns as we step up and then learn the lessons from best practise from around the world and bring that back to enrich our, our processes. And I think I’ll pass to one of the other panellists to either build on that or move away.
Bonnie Shaw:
Marion, what do you think?
Marion:
So, so I think what you’re saying is very interesting, but in my world, it’s a little bit different to that. So, because what I’m trying to do is influence policy makers, scale matters a lot. So the jurisdictional levels in Australia they are very, very different, they’re very different powers. They have very different capabilities, different leaders. So when I think about how to use data, it’s very particular to scale. So not necessarily different if you think of Melbourne versus Sydney, but very different if you think of Melbourne versus Geelong or city of Melbourne versus state of Victoria, for example. So, so I’m very interested to hear you see more about similarity in scale when it’s so different, I suppose, to what I experience.
Sarah:
But also I guess, to build on that there’s sometimes artificial boundaries that we have. So the city of Melbourne is obviously just a series of suburbs within the inner city, but doesn’t represent greater Melbourne. But obviously working with other local government areas to actually get that full picture is important because understanding that an invisible boundary doesn’t necessarily kind of shape or give you the complete picture, I think is important.
Bonnie Shaw:
Most people walking around don’t realise they’ve stepped from the city of Melbourne into the City of Vierra.
Niels:
There’s, there’s also something really interesting. And Mariah, it’s perhaps a question to you, cause I think you are the sort of the data tech person among us. How we deal. Look, I think when we talk about data, the five of us, we’re very often talking about data from the digital sort of perspective, the ones and the zeros on, on some sort of hard drive or computer system. But how do we make sure that the data for which we don’t necessarily have digital data let alone written data? How do we still make sure that that data reflects or is reflected in how we design cities and open experiences? And I’m particularly talking about here, us, Australia, culture of 65,000 years old with, with very limited sort of yeah written data available about those histories and the narratives and the culture. How do we make sure that is reflected moving forward?
Norion:
That’s a very fascinating question. And an interesting question about scale as well. So I think that there’s, there’s digital information that is created through our modern societies that we can of course look at to get an approximation of how things work, how connected our communities are, the level of activity in one place versus another, the level of economy in one place versus another, but also the narrative of place and culture is really important.
And I think that whilst we have so much data available and we can use that and unlock that to broaden our understanding, we still have to do deep engagement with our community. We still have to get involved in the landscapes that we’re designing and the places that we’re curating in order to create great outcomes. So I think on, on the one hand, big data can reveal the big patterns, but it isn’t all of the answers. And so I think there is this, you know, intimate balance between deep quantitative analytics and of course, deep qualitative process. And, and I would say the qualitative data is the higher level, you know, information. You really want to get to that level of synthesis. That’s about embodying all of the context, emotional, historical, et cetera. And so I would advocate that we have to be, you know, comprehensive in that approach.
Sarah:
I think another layer to that is just acknowledging that everything we build, every, every system, every map, every kind of snapshot of whatever we’re looking at is also deeply imperfect. The idea that [inaudible 00:24:46] from the Centre for Public Impact talks about, and that we create certainty artefacts, these kind of snapshots or moments in time, or here’s a plan or a strategy or, or a, you know, a thing that we’re planning to do, but it’s still deeply imperfect and such as the data, just being able to move in shorter cycles and feedback loops, and also to kind of move at different speeds in order to kind of keep up with that. And how do we move acknowledging that it just needs to be constantly living and updated and we’ll never have that picture of perfection.
Bonnie Shaw:
Excellent point. We keep coming back to perfect and imperfect. It’s very interesting. I would like to throw it open to the audience. If there are any questions, I’ll give you a minute. I know it’s scary. It took us, it took us quite a while to warm up here. You saw it. So take a deep breath and put a hand up and join in. Otherwise I’m going to pick someone, Tim. Any questions? Hi, Sue.
Sue:
Hi.
Bonnie Shaw:
So the question is when you’re talking about perfect and imperfect data, how can you understand address and start to mitigate issues around bias? Neils, can I throw to you first?
Niels:
I was going to say, I feel like it’s a question for me, or at least I’ll start with. Look, it’s really something we wanted to test with that provocative thing that we built a couple of years ago with biometric mirror. And I think one of the solu, well solution is probably the wrong word, but at least one of the mechanisms to try and mitigate as much buyers as possible is by developing a sort of inclusive process around how you deal with data. And look in reality and in practise, it’s impossible that every single data point is assessed against the person that created it to sort of build up that narrative. So I’m, I’m very conscious that that’s impossible, but it’s really an argument to involve people in, you know, sort of data analysis process, as much as possible and organically, and naturally you’ll see where your individual biases arise.
And these might, so for instance, in our project, those relate to gender identity, those relate to cultural identity, ethnicity, and we all have them, whether we want it or not. The challenge is data tends to amplify these biases and it also tends to create a risk where you as a human have very little control after a while, you have very little control over how, over how these biases sort of eventuate. And obviously, yeah, I don’t have to tell you what the sort of devastating consequences of that can be. So again, it’s that argument for an inclusive process.
Sarah:
Can I build on that? Am I able to jump in?
Bonnie Shaw:
Please do.
Sarah:
Yeah. I just think that’s a really great point, you know, building on that idea of collective sense, making it was talking about, you know, that participatory sense making synthesis of the work is the way to start to attack that, to start to break down, you know, those individual biases and again, get people involved, not just in, you know, creating the data or inputting into the data, but all the way through the process throughout.
Bonnie Shaw:
Marion, you look like you’re about to jump in.
Marion:
I was just actually thinking about, so, so part of this is the process of collecting the data that you want an inclusive process as a way of minimising bias, but it’s also what you do with the data at the other end, that is very important. And the thing that came to mind with what Sarah was saying is that you, I was thinking about weather forecasting and how it used to be that people just went out and they had their rules of thumb, and it was somewhat effective at certainly at the local scale. But now it’s highly complex, highly, you know, super computers do this, but the forecasts are always better when it’s a combination of the supercomputer and human judgement . The, the computer itself doesn’t really get you all the way. There’s, there’s, there’s still this knowledge that people bring and this judgement that they bring to bear in the interpretation of data that is fundamental as well as the process of collection.
Bonnie Shaw:
So I’m going to wrap us up and I’m going to ask the panellists to think about the greatest opportunities you see ahead for the use of city data in planning and design and community engagement. What’s, what’s the big, bright shining, positive future, Neils? And while you have a brief moment to think we’re going to swap over the panellists, and while that happens, our microphones need to be sanitised so that we’re all being COVID safe. And what I would ask you each to do is lock eyes with someone that you don’t actually know that you didn’t come here with and start to think about a question that you might ask them so that we can actually start to enjoy the fact that we are in this space with a group of people we don’t know. So Norion, bright, positive data driven future. What do you think?
Norion:
Well, I think we’re really fortunate to be able to access many, many decades of data to unlock our creative potential, to design better cities and places, and be able to test those continuously over time. So using data to create insights, to better design cities in places, and then use that same framework to continuously test and optimise them into the future. So I think it’s really liberating our ability to, as designers and planners, to be part of the process of creating cities in places in a more active way, and be able to continuously learn from what we’re creating.
Bonnie Shaw:
Awesome. Marion, what do you think?
Marion:
I think the thing I am most excited about looking forward is that not only does new data allow us to have new information and new insights, but it allows for new opportunities of people matching together that we didn’t have before just by the pure volume. So I think there’s limitless possibilities for, for better matching and cities that are in large part, the place for that to happen.
Bonnie Shaw:
Sarah.
Sarah:
Probably two things. So one is deep history and deep future. So beyond just the things that we’ve been collecting, how do we really tap into indigenous knowledge systems and other ways of viewing, and then also, how do we, how do we think about imagination as data and link that to place in terms of what people want? And then the second is really that participatory aspect. How do we get people involved in data stewardship and the collective? So that there’s a real true ownership and buy-in of the work that we’re collecting.
Bonnie Shaw:
Nice work, and Niels bring us home with something beautiful.
Niels:
I’m largely building on what, what Sarah said, and I’m going to put my inclusivity hat on as again, I think we are very fortunate to live in a society where we as individuals are very much in control over the data we produce. I think that’s definitely something to sort of yeah continue into the future, but also look at alternative models of individual data governance. And I don’t know, interfaces that allow us as in individual citizens to control what sort of data we share for what purpose, what, what the sort of outcomes of my data are, how they benefit me and my fellow citizens. I think there’s a lot of opportunity in that area.
Bonnie Shaw:
Awesome. Thank you very much. If you could all give a warm round of appreciation for our panellists and we will. I think you can leave your mics here and.
Speaker 1:
You are listening to an MPavilion podcast. Conversations about design and the world we live in. For more visit our archive at mpavilion.org and subscribe wherever you find your podcasts.