Data is the new oil in the digital economy – it’s a claim you’ve probably heard dozens of times. There is a good reason for that: the volumes of data available, about almost everything, are growing exponentially. In fact, 90% of the data in the world today was created in just the last two years – and it has enormous implications for cities and neighbourhoods.
Last week Deloitte launched its Commercial Real Estate Outlook which outlines the new “rules of the road” for the property sector. It shows that data-centres are a preferred investment opportunity over housing and that 80% of commercial real estate companies believe they should prioritise the use of predictive analytics for business decisions. The landscape is changing fast.
As data becomes increasingly important, so do other questions surrounding it, like where does it come from, how representative is it?.
If you’re asking those questions, you’re not alone. In this post, we’ll explore:
What is social data?
What it isn’t
How representative is social data?
Why social data matters for cities.
What is social data?
Social data is not a new term. Using mathematical concepts to describe human behaviour emerged as early 1800s, spearheaded by French social thinker Henri de Saint-Simon. In the last decade, it more commonly refers to using large datasets and mathematical methods to understand the behaviour of human crowds.
MIT’s Media Lab has become famous for this concept. Here, early studies began with cameras and time-lapse photography, while recent studies involve smartphones to understand social networks like friendship groups, teams and community influencers.
Early placemaking pioneer William Whyte’s used time-lapse photography to understand the “social life of public spaces” and to use this data to drive decisions about public space improvements. He verified a number of common sense social patterns such as how people are attracted to busy and active spaces and why people sit where there are seats. One of his early conclusions which now seems so simple it’s hard to imagine it wasn’t considered was: “what attracts people most, it appears, is other people”
Today, we are creating millions of data points about the places we go and what we value, through our phones and social media accounts. This creates a real-time, digital ‘twin’ of our neighbourhoods that reflects where people spend time and what they value.
Social data is data derived from these unconventional digital sources that is reflective of peoples’ behaviour and lifestyle choices (some sources include social media, rating and review sites, map applications and event pages). Neighbourlytics harnesses this powerful information to provide real-time insights into the social life of neighbourhoods.
So, what is social data?
Social data is data about groups of people, at scale.
There is enough volume of data to understand how the ‘herd’ or ‘crowd’ (rather than individuals) behave.
Social data is geospatial.
To draw insights about people’s behaviour in our neighbourhoods, location is an important factor. Geospatial data can include social media, sensor data or mobile phone metadata for example.
Social data is renewable.
It can be queried again, at any point in time, and the output might be different. Time-series data supports the use of mathematical models to understand human behaviours and social interactions. For example, interactions based on mobile phone locations, rather than the census which is static.
What social data isn’t
In making sense of social data, it’s important to highlight out some points about what it isn’t.
Social data isn’t just social media.
Social media may form part of a social dataset, but social data refers to any dataset that can be used to understand human behaviour and interaction.
This can mean big data (like for particular social media platforms), but it does not have to be “big” necessarily. A dataset that contained thousands of travel blogs for a particular city, is an example of social data, yet not “big” data.
Social data isn’t static.
Like the census or surveys (for example). Traditionally we have understood how populations work through the lens of demographics. This is becoming increasingly inaccurate for urban planning as people are globally mobile and live in a real-time experience economy. 10 years ago we didn’t have smartphones, that’s how old the census is in some countries.
Social data aims to describe behaviour, not opinion.
It’s not survey data or user-generated data. William Whyte (see above) was one of the first urbanists to understand that what people say about a place and what people do in a place are often quite different. For instance, you can ask peoples’ opinion about a park and they will give you one answer, watch where they walk, where they sit, who they talk to and how long they stay and you get an entirely different picture.
How representative is social data?
So this is all good and well my aunt Edna isn’t on social media, what about her? This is another important question. More people are active online than you might think. Think about when you last opened google maps? Registered for an online event? Checked or posted on a social media account? Looked up a small business page on Facebook? All of this contributes to social data.
In Australia, there are 19 Million people on social media every day, that’s more people than are enrolled to vote.
What might surprise you is that Seniors over 65 are one of the greatest growth areas in use of social media.
Social media use is on the rise globally. Countries like Kenya and India have some of the highest level of mobile phone use in the world. In a recent Neighbourlytics study of Nairobi, we capture 3 x more data than for Singapore for example - exposing a wealth of small informal businesses.
Why social data matters for cities
We’re entering a revolution, moving from place-making to “sense-making” – deriving value from new data, and it's having a profound impact on cities.
Principally, if we want to create cities that are human-centered, we need to have data about the humans. Traditionally urban planning has focused on traffic and buildings in part because these attributes are slow changing, easier to quantify, and the feel safe - since we know how the data about them was made.
These datasets prevail because sometimes it’s easier to stick with what you know, rather than asking the question as to whether its the best way to understand a problem (it could be, but its always worth asking the question). We believe social data disrupts this.
The best city planning starts with people first. But people aren’t all the same (of course), we all behave differently and different neighbourhoods, cities, cultures and groups have different behaviours.
Most experts see the city through the lens of their own experience rather than what’s actually there. Yet, most of us are leaving digital evidence of the places we engage with, shining light into the ways in which places influence us and drive us towards certain behaviours.
Imagine how much better we can design, plan and create places for people if armed with this insight into the nuances and diversity of human behaviour, not just what we see.