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Tackling social challenges

Watch Sean Audain discuss a case study of how the smart city program works in Wellington, the capital of New Zealand.
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Over time the manager of that area, Jenny Raines, has been very keen to understand what’s actually happening in the streets and how do social issues, things like begging, affect the city and how can we do better to support those citizens. [Chris Vas]: What options and what avenues were explored in the past before going down the route of trying to deploy technology to, as an enabler to solve the challenge? [Sean Audain]: The thing with technology is if you look at the word itself, it’s Greek, it comes from ‘technia’, it means craftsmanship. So, all of the time in our Smart City programs we’re looking to achieve an outcome and the Smart City is just the craftsmanship required to do it.
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So, if you take something like begging, we were under a great deal of pressure to pass a bylaw a couple of years ago to deal with the begging problem that was arising in the streets. One of the problems is, when you’re a city is sometimes you don’t have a lot of data to make a decision with. So, what we did is we said well, why don’t we try and find something out about the city. So, what we did is we took our local hosts and the people who work in the streets and we shifted them to digital reporting.
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So, that involved creating an app that fed into our spatial systems which moved their reporting over time so that it would appear on a map and we could classify it and look at it. To actually do that, that meant sitting down with those local hosts and designing those forms, it meant teaching them about privacy, making sure they didn’t contaminate the data. It also meant understanding what problems look like on the ground and how they get classified. And what that did is it gave us a picture of where begging was occurring according to what our people see.
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To index that we also used a bit of vision learning in Cuba Street and taught a camera in a distributed computer to simply understand begging behaviour and record whenever a beggar was present. So, it’s behavioural learning. It’s a little bit different from facial recognition and it’s not recognising a person, it’s recognising what a person’s doing. [Chris Vas]: Interesting. [Sean Audain]: Because the problem with using human data sets like the local host is they can’t be everywhere all at once. And then on top of that we layered all of the data from Council’s phone system; so whenever people had rung up to complain.
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And what we did is we then fed that into a democratic process and we could use it to basically verify anecdotes. So, when people came in and said, ‘Well, begging’s a recreational activity’. We could look at the data and say, ‘Well, based on this, there were beggars out at ten o’clock at night on a Thursday and it was only 9°. It’s a very strange form of recreation.’ It also meant that we could understand the pattern of life so that when we had earthquakes, we could quickly check on our homeless community, make sure they were alright.
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It also allowed us to target more intensive research and when we sat down and talked to people, we discovered that begging and homelessness were quite different problems. There were only a small section of beggars who were actually homeless and the biggest driver of begging in Wellington was loneliness. People had homes; they just didn’t want to sit by themselves all day. And what this did over time was move from a law and order discussion about how do we enforce and deal with this problem to a compassionate societal driven response which is how do we sit down and make sure these people aren’t lonely so they don’t have to sit on the street.
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That also restricted the way we shared the data. So, in the end we shared information, we didn’t really share data. That project then allowed us to begin more of a data sharing project with a wider group of agencies. After we’d had that discussion, we found that actually awareness on the streets is something that a lot of agencies are quite interested in. So, what we have is a group called ‘The Trauma Intelligence Group’. It’s got about half a dozen different agencies in it, people like the District Health Board, the ambulance service, Police, ACC, basically anybody involved with harm in the city.
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And what we’ve done is created a series of MOUs which bind how data is shared, how it’s released, when, when that group will meet, how it meets, what it’s purpose is, and over time we’ve moved from being good bureaucrats who read printed paper reports to each other at these quite long meetings, to having a spatial dashboard available to each agency with anonymised data put into it. So, one of the key things was working out how the privacy worked, so that meant making sure that data was anonymous before it left an organisation, it also meant governing the release of data; so no organisation can release another organisation’s data without permission. And it also meant understanding different roles.
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So, for example, it’s not the Council’s place to be Policemen, that’s the Police’s job. If they require evidence then there’s an evidential process for them to use to request that, and it was making sure that those sorts of things were followed and things didn’t begin to slip. But what it meant was we could start to understand what is, how does the city’s environment affect the city’s health. So, what that meant in practice was, for example, we noticed that there were an unusual number of falls on certain days in the city.
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And then when it was traced through the, through the ambulance pickup system, so we saw the falls entering the hospital looked for where they were coming from in the ambulance system, and they were largely coming from the stadium. And so, when we went out and had a look, we found some broken steps so we fixed them and the number came down a bit. And it was starting to understand things like that. If you look at it, what it looks like from a public service of view, you know, Council as an infrastructure provider has money for steps. It’s a lot cheaper for us to fix a step than the Health Board to fix a person.
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And that then means that the Health Board is free to start looking at how can we perhaps solve a problem that’s more rooted in our jurisdiction. Probably the best example that’s come out of it is the latest alcohol bylaws. So, there were two alcohol ban areas proposed, one in Kelburn, one in Kilbirnie, and because we’d worked with the Police in such a constructive relationship for so long, the Police released all of the alcohol-related crime data located directly into the street, which meant that we could put maps in front of the community and the Councillors which showed this is the footprint of alcohol-related crime that the Police see.
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And what that led to was a much more informed debate around, yes, we do have a problem with alcohol-related crime in this area, but we don’t in this area. So, if we’re doing this for this reason it makes sense to do it in this area but not that area. And what it meant was, because we were working from common evidence basis, it made the debate much more informed. So, you were having discussions about how to interpret evidence, not whether people knew what they were talking about or not. Which is much more civilised. [Chris Vas]: What role did community or society in general play to, to take on the challenge?
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So, not just moving the pendulum and the problem to the public authorities but to the society taking responsibility and addressing them. [Sean Audain]: So, through the submission process and through the Councillor debate, what it did was start to verify what people were saying but also gather their interpretations. Which meant that some more creative solutions were come up with. It also meant that people began to talk about the limitations of data and where data wasn’t really what was required to make a decision. And that became very interesting to us. [Chris Vas]: That’s an interesting point because we talk about Smart Cities, we talk about Smart Societies, we talk about the need to co-create some of the solutions.
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So, you’re actually starting to see the public become engaged in reframing some of those problems and, and coming up with new, creative ideas to, to tackle some of these challenges. What I’m, I’m also interested in understanding, you know, where has the community actually engaged in becoming part of the solution? So, while the responsibility and accountability, I think, at the end lies with public authorities, there is also the shared responsibility with society. So, do you see that happening? Do you see that as an evolving journey for the community to become engaged? [Sean Audain]: Yes. So, one of the best examples, it’s from a related area, so community funding.
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So, we released all of our community funding data and then put out a bounty to a hackathon to have somebody do something with it. And what that resulted in was community made apps that basically became grant finders. You told it what you wanted to do and then it would analyse the data to tell you how much you should ask for, which fund you should ask for it from, and then successful pieces of previous applications that you could write your thing more easily. That was a solution that the community created themselves and it was a good example of – part of the Smart City journey is understanding you don’t have to own the touch point with the customer or the citizen.
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Probably the other big lesson is in understanding what our duty of care is as an organisation. When you’re dealing with more vulnerable communities you have to take a more proactive stance than necessarily just data governance. And it’s about understanding data as an aspect of what you do, not necessarily as a thing of its own. [Chris Vas]: Fantastic. Thanks for sharing with us these case studies, Sean. As we started out this course we started to talk about the route to Smart Societies as a pathway through Smart Cities, an urban infrastructure which is connected, an integrated innovation ecosystem, but also smart governance which is user centric and citizen driven.
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As we’ve seen in the case study with Wellington, we’ve seen these factors evolve since the Smart Wellington program commenced. We’re fortunate and we thank Sean and the Wellington City Council for sharing with us their experiences and, and the time to, to document these case studies. Thank you very much.

Sean Audain from the Wellington city council discusses how the use of technology, data and digital reporting helped to inform the ‘craftsmanship’ to tackle an issue such as homelessness and begging.

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