Could social data democratise city planning? Neighbourlytics thinks so
Neighbourlytics captures data of community engagement to give insights into the unique social life of particular neighbourhoods, giving urban planners more information about the areas they plan, create and manage. The data is gathered from a range of social media platforms and other online information sources – such as reviews and ratings sites, travel wikis, mapping sites, and event promotion pages – to uncover what local people are doing, saying and loving in their neighbourhoods.
Christiansen-Franks says, “What makes our data is great at finding is identifying what is unique and interesting about local places and shining a light on it, rather than just saying the supermarket has a lot of people going to it, we can see that the community gardens are really well loved, or the murals and artworks have a lot of people talking about them.”
One project, alongside the Brookings Institute, analysed innovation districts across the world to understand what makes those precincts work. One of these areas pinpointed was Britomart and the wider viaduct area in Auckland city, which Christianses-Franks says, “was off the charts in terms of data and public vibrancy”.
Christiansen-Franks says, “It could simply be because it is the old quarter and that there is a lot of unique things happening in that neighbourhood. Also, pedestrian neighbourhoods have more data in them because people are talking about the place rather than moving through it.”
One of the biggest holes of urban planning is the lack of information gathered on the public, particularly in ethnically diverse areas, where people feel despondent about participating in the current process. While Christiansen-Franks is clear that social data does not give the full picture – and is not a replacement for community engagement or public opinions – it does give a view of shared values and behavioural patterns in distinct public spaces.
“At the moment we focus on the social side because we see that as a massive blind spot in the industry and often taken in a very superficial way. We focus on data that helps you see the neighbourhood through the eyes of the locals and the people who use the space.
“However, it would be a mistake to look at our data and think you have the whole picture, but it helps augment your face-to-face engagement. We partner with other engagement consultancies and online platforms so that we form part of a suite of understanding a project process.
“It’s certainly a hell of a lot more than you get from driving down the street and looking, which is often what a lot of urban designers are doing. But it does have biases and have to be thought about in that way.”
So, what are some of the biases and how does the data represent all cultures and socio-economic groups?
Christiansen-Franks says, “Firstly, it’s important to note that all data has bias, government data has bias. Our bias is the people who use social media, Google, or one of the other platforms we use, we only have data about them. Very early in the piece, we built an analytics capability to bring in lots of data sources, because the more data sources we have the more we can outweigh that bias. So, we are very conscious about being as representative as we can across the community.
“There is a bit of a misnomer that low socio-economic status is a big determinant about participation online but we don’t find that at all. The biggest blind spot in our data is Chinese visitors who use WeChat, because that data can’t be used.”
An area where the data is particularly useful is in retail, as online trade platforms continue to rise in popularity, some have feared for the future of local retail stores.
Christiansen-Franks says, “We are moving into an experience economy, we don’t get social status from the Calvin Klein jeans and the nice car. We get social status from what we ate for breakfast and where we went on holiday. So retailers are trying to figure out what to do there and we can help them figure out what is happening in that space.”
Another interesting gleaning is the data captured on cycle lanes and the subsequent effect on surrounding parks and businesses.
“In terms of cycle lanes, we often work with bicycle user networks and local government. What we have found is that when people are in a car and they are going from A to B they don’t stop, get out and interact with public spaces. Whereas cyclists or pedestrians are more inclined to do that. So we can see the impact of passive transport networks on the surrounding place and that people are posting about things. Public transport is a big one as well, when bus routes change or new tram lines open you start to see activity in a particular place go up.”
So, although Neighbourlytics may not be the panacea to a disproportionate urban planning process, it could be a part of a more mobile solution to help involve the community.