 Hi everyone, I'm Gala and I am from Neighborlydix. We are a company in Melbourne, Australia and what we do is we collect, analyze, and visualize social data. I'd like to start my presentation by acknowledging the traditional owners of the land and pay my respects to the elders past and present. Right, so I'm gonna be talking about social data today. Okay, let's get started. So to start off, I wanted to show a video to get you in the mindset of a city maker. In this video, you will see four new cities and four old cities. And I would like to accompany the video with a quote by a famous city maker called Jan Gell. In the old urban settlements, they always did it the other way around. First you'd have a path in human activity, then some sheds along the path. And over the centuries, these sheds turn into buildings and streets. So it starts with life, with people moving towards the river, towards wherever they're going. And then next, it's the spaces that human life requires. And then the buildings were built in relation to these spaces. This way we have streets formed by the feet and the town squares based on what the eye can see. So one of the things that really strikes me when you're looking at these cities is the difference in the structure. And if you've traveled to many of these cities, you will find that there's also a difference in the feeling. And some questions that I've asked myself is why? Why do they feel so different? Why is the experience so different? And can we use data to describe these differences and to understand them? So what even is city making? So I'll introduce it for those of you here who are not in the urban planning space or city making space. Most times the way that it works is they go and they collect just tons and tons of data, the census data, disadvantaged data, traffic and pedestrian flows, land use, property use or property sales, infrastructure like the roads and the buildings that are there. Maybe they'll have a consultant walking around like this area has a cafe field, whereas this area has an industrial field. If they have money, they'll have a survey maybe on the spot at a park or something or maybe send them by mail. And if there's some decisions to be made, they'll have some community engagement like a town hall. And all of this data just gets basically chunked into some analysis and a master plan is created. And there's a lot of issues with this process. And one main one that I think is quite important is there's not really a way to diagnose. So you made this plan and 10 years happened and you built a bunch of stuff and did that work? Did it do, you said you were gonna build an art precinct. Is it an art precinct? How do we define an art precinct? It's not very flexible and things change in these very large time spans. A lot of times the data when you're starting is already quite outdated like the census maybe was from a few years back. And so by the time you end it's not really what it used to be. A lot of the community engagement can be overcrowded by lobbying groups if they have a particular outcome in mind. And well surveys I mean they're expensive and also they're only as good as the questions that you ask. And as we all know sometimes when we do surveys people will say something but actually do another. So we've identified a lot of these problems and one of the things that I found and that in city making we find important is well people are gonna live in these cities and if people are gonna live there they should be at the center of the city making process. And one of the things that is very clear to me is that of all the data sets that I mentioned there wasn't a lot of data about what do people like in these spaces? What do they value? What brought them there? What keeps them there? What makes you wanna be in this neighborhood and become a part of it? And so I think city makers have really struggled to find this data is not quite clear where you go and get it from. And I think they have tried. And this data about people is just so important. And well we now have a lot of data about people because every day people wake up and they talk about the spaces that they use and how they use them. They talk about the cafe they went to and they talk about the park where they go with their family or maybe where they go running. So social data. We define it and by no means do we own this definition and this is what I'm gonna mean when I talk about social data. So we wanted to have a geospatial component. We wanted it linked to a physical place that is there. We wanted to be made unsolicited so kind of organically as people live their lives. And so therefore we wanted to be place focused because what we're interested in is places because we're city makers. And what we want to understand is data that will tell us about the behaviors of people's in connection with these places. So I list here a bunch of different sources but by no means is this exhaustive. This is just a subset. And these sources will change and they will evolve through time. So let me show you an example of what I mean. Oh sorry. Let me talk about my title topic. I can take this data and I can use it for lots of different things. I can use it to sell you things and I can use it where to go for dinner to tell you where to go for dinner. And one of the things that I think is very important is well, maybe the biggest problem with this data is not necessarily the fact that it's collected. Let's put that to the side. I think the most problematic thing is how it is used. It is being used to work against us. And so if we change that conversation, can we use this data to make better cities? And that's kind of the theme that we're trying to talk about at Neighborlydix and using social data for better cities. So I'll show you an example. So this is MacArthur Square. This is a park near my house. And so from a mapping kind of platform, we can gather that there are some businesses around that there's a main road that it's long. If I then go into its Facebook page, I can now start seeing that there's a picture and that there's this community group called Vic Rocks that seems to place rocks there. From public Facebook feed, I can see that there's this pop-up museum. And in the Instagram feed, I can not only see the pop-up museum, which is the images on the left, but I can see that there's dogs and that people like the buildings. This is not the entire feed, but one of the things I can see is that people really like photographing the trees that are there from the events. So these are Facebook events, but nothing to stop us from using Meetup or maybe Eventbrite. I can see that there's some historical stuff to this place. Maybe a historical meaning. From Twitter, I can see that people got angry about it being an out-leash dog park. And I can see pictures of trees again. And from Yelp and TripAdvisor, we can get, it actually, it's hard to read, but it basically says that there's some historical buildings in the area. And so we've gone and we queried all these different social media platforms and we now have learned so much about that place. And this is information that would be really, really hard to get in any other way. And we've done that and usually we can do that quite quickly. So what are some of the strengths of social data? It's scalable. If once you've done it for one place, it's quite easy to do it for other places. In fact, if you went to the previous talk, he was talking downstairs, he was talking about once you create scripts to absorb some of this data, you can just reuse them. It's organically generated. So people post about things and there's certainly bias about how they post about it, but they're posting about the spaces and this is information we wanna grab as city makers. And the other part is the reactive. So by creating these digital footprints of our neighborhood, we can then compare neighborhoods. We can also track the progress if a project is happening. And this is actually really useful information. We can also see a different dimension that we might not have seen before. And I wanna show you that by example. So here is two areas, right? And here's a classical way I would compare these cities of Singapore and Nairobi. And I can see that Singapore is more people and it's more dense and it's got a better GDP, whatever that means. However, these things are calculated. And as we absorb it, we're developing ideas about these two places. In this map, you can see in gray, it's all the Facebook pages that have a linked latlong or an address and in pink is all the Google places that you find in Google Maps. And so we can start seeing that Nairobi is actually really dense. And so there's something going on here because if I was looking at it the classical way, maybe I would have assumed that there was gonna be more things in Singapore. And so if I look at a picture, I can see that maybe it has to do with when these pictures were taken. But at least I'm starting to see some evidence that people in Nairobi are walking about and in Singapore, things seem a bit more organized. So I'm already starting to develop further and deeper ideas about these cities as a place. Right, so here's a bit more detail. Nairobi got 7,000 Facebook pages, a bit more. That's like more than seven times what Singapore got. And I'm not sure what this tells us, but it definitely is something we should take a look at and we should consider. And one of the things we have found is that Facebook places are usually linked to a more informal economy. And if you've been to Nairobi, that is something that you will know. You know, these markets, they have thousands and thousands of people operating in a very small space. And we should know that when we're city making. We should know about these informal economies. Greenfields have a lot of home businesses and we're not gonna pick that up by walking around and seeing who's registered their business. So as we dig deeper, we can also start finding evidence of things that are missing, maybe community services. So what are some of the weaknesses? I said pseudo open, but it's not totally open, right? Like these people, of all the different platforms, they have this data and they can decide to close it at any time. There's areas where there's not a lot of it, especially in rural areas or obviously areas where people don't use the internet as much. It's created differently in different places. So because this data is created organically, well, I don't know what are the things that drive people in Australia to post things versus America. That's something maybe Facebook would know, but I don't know it and so therefore I kind of have to act accordingly. It's got unknown biases. Everyone knows these types of biases, but I think one thing I wanna highlight is so do all the other data sets that are used for city making. Like not many people go to these town halls. In fact, a lot of people are really, really underrepresented in the city making process and this data going back to one of the strengths that I said is like it's generally available. And I think that's really powerful because it allows us to include more people in the city making process. And different, it's really difficult to collate it. So how do I know that Joe's Pizza and Joe's Pizzeria and Joe's is all the same place? That takes a lot of thinking, especially if you wanna automate it. And it just takes a lot of time and you have to kind of do it source by source. Okay, so some concluding remarks. So what if I told you that I was gonna, these pictures here are from Instagram? If I told you that I was gonna grab all your public Instagram feed, you would be like, hey Gala, don't do that, you're invading my privacy. But what if instead I told you that I was gonna use them to make your neighborhood more livable, to create, enhance and maintain a supportive environment for those who live there and spend time there, maybe to improve it? Social data gives us this ability to look at neighborhoods across the world, finding differences and similarities. These pictures that you see here, half of them are from a neighborhood in Mexico City called Roma. And half of them are from a neighborhood in Melbourne, Australia called Fitzroy. But when you look at it, they seem similar. And maybe we can learn from one place and the other. They have similarities no matter the fact that they're in totally different places in the world. This is not something that is easy to see when you are making cities. They have all these points of similarity and we can gain a lot from knowing that. Social data can give us these unique digital footprints which we can turn into insights to understand these heartbeats of these neighborhoods, what makes them tick? Why do people love some and then hate some? How do people live and how do people experience these places? This social data can give us this very unprecedented ability to learn and support city makers when they're building new cities. Sometimes when they're growing a city or maybe when they're trying to reinvigorate an area. One of the important parts about it is that this data is being posted by people and therefore it's people in the center. But most importantly is when we turn the focus from it being aggregated by person and we change it to be aggregated by place, then we lose a lot of the harms of the privacy and we get a lot of the gains of understanding the behaviors place-based. That's it.