## How do people move around Barcelona?

**Would you tell me, please, which way I ought to go from here?**

Alice:

Alice:

**The Cheshire Cat:**That depends a good deal on where you want to get to.

**Alice:**I don't much care where.

**The Cheshire Cat:**Then it doesn't much matter which way you go.

**Alice:**...So long as I get somewhere.

**The Cheshire Cat:**Oh, you're sure to do that, if only you walk long enough.

Lewis Carroll,

*Alice in Wonderland*

## How do people move?

Just like the Cheshire Cat said in

GPS tracks from mobile phones is a good way to explore how people move around the city. This is how Barcelona looks like if we add GPS traces (Image 1). I used a rendering process to visualize and highlight popular paths: lines with brighter colors correspond to higher densities of people passing-through this specific street segment.

Maybe you're thinking about how the hell I got this data. Or more importantly: is my phone company tracking my movements? My answer: yes, it surely does. Confidentiality and privacy are important issues so

*Alice in Wonderland*, if you walk long enough you will get somewhere (I talked about*where*here). However, in the real world we just have a finite number of destinations and a finite number of places to pass-trough. So, how do we know how people move from one place to the other?GPS tracks from mobile phones is a good way to explore how people move around the city. This is how Barcelona looks like if we add GPS traces (Image 1). I used a rendering process to visualize and highlight popular paths: lines with brighter colors correspond to higher densities of people passing-through this specific street segment.

Maybe you're thinking about how the hell I got this data. Or more importantly: is my phone company tracking my movements? My answer: yes, it surely does. Confidentiality and privacy are important issues so

*science*has come to the rescue (again!) and proposed other ways to estimate how people move around a city.*Image 1: GPS tracks in Barcelona*

One way of estimating people (or vehicle) flows is analyzing how the cognitive process works when somebody is moving around. In other words: why do people move following certain paths instead of moving randomly across streets? Looks like an obvious question, right? But, sometimes, asking the right questions is even more important than finding an answer. If you know where to go, it will be easier to find a way.

One way of trying to answer the question about why do people choose certain paths is using Complex Network Theory applied to the street network.

**Betweenness centrality**is an indicator of a node's centrality in a network. It is equal to the number of shortest paths from all nodes to all others that pass through that vertice. A segment with high betweenness centrality has a large influence on the transfer of items through the network, under the assumption that item transfer follows the shortest paths.

For example, this is betweenness centrality (radius = n) in Barcelona (Image 2).

However, using betweenness centrality with a

This is how betweenness centrality with

*radius = n*doesn't make many sense if we are measuring movement of walking people. At the end, Barcelona is too big to move from one edge of the city to the other one by walking. So a more accurate result would be setting a radius more human-friendly, for example,*radius = 800m*.This is how betweenness centrality with

*radius = 800m*looks like in Barcelona (Image 3):*Image 3: Betweenness centrality in Barcelona (radius = 800m)*

Pretty different results, right? That's because I used a metrical restriction when running the algorithm. Furthermore, we can apply other criteria to calibrate the algorithm. For example, instead of treating each node or segment as a binary interaction, I could weight each node in proportion to their capacity (vehicle lanes), influence (retail or monuments presence), etc. Apart from adding another dimension of heterogeneity within the network beyond the topological effects, it will generate more accurate results.