Universal patterns in cities and new data sources

Welcome back to talk about cities. So cities are quite fascinating. So if you think back 200 years ago, maybe you will not be able to think back 200 years, but we know that only a few percent of the world population lived in cities. It is projected that by the year 2050, 75% of all humans will live in cities. So this emphasises that it’s really dramatic and crucially important to understand how cities work. Over the past decades, the former president of the interdisciplinary Santa Fe Institute, Geoffrey West, together with a number of colleagues, investigated the growth of cities. They looked at some low granulary data on crime, on wealth, on other aspects which describe cities.

They were able to find that simple mathematical laws govern the properties of cities.

Basically, given these laws, you can describe properties like crime, like average walking speed, health and many, many other quantities by one individual property. And this is the population of a city. By investigating how this behaves across a number of cities of different size, they were able to find out to which extent these quantities scale with city growth. For example, you wouldn’t expect that you have the same number of houses as people living in a city because there are huge houses, which are able to host a number of humans and smaller houses, which can only be the home of a few people.

So this was a quite universal finding that you can write down very simple mathematical laws describing the growth of cities.

If you now think ahead and take into account all the detailed and highly granular data which are out there - which we have to some extent covered in previous weeks - then you can understand that this can really help us to further investigate and deepen our understanding how cities work, how their inhabitants behave over time. We use cards in terms of using public transport systems. We use payment cards to buy food in the supermarket. Or we actually go shopping online to get goods delivered. So all these individual patterns describe how dynamic a city is developing over time.

And we can take into account location-based information, so for example, information we share via our mobile phones, as we have seen before in the talk by Federico Botta. So he used information on mobile phone data to describe how many people are in a given restricted area. But this idea is extendable and can be used by various different data streams which are coming from a city, and in particular, from the inhabitants of a city. So if we look at this example here, we have a map showing all the locations of photos shared via Instagram by users which were located in Lagos, Nigeria in August 2013.

So this emphasises not only the global coverage of these new big data sources but also if we go ahead and look at what happened one year later how these information sources grow over time. We have a 175% increase in the number of photos shared in the same restricted area in Lagos.