Mapping Coffee Shops+ Walkability

I’d much rather hang out in a cafe. That’s where things are really happening.
— Joe Sacco

why coffee shops?

In every city, you will find what we call “third places”, a type of environment allowing citizens with no particular affiliations to comfortably meet. If the home (first place) and the workplace (second place) are the usual social environments, third places provide another needed option. In his book “The Great Good Place”, urban sociologist Ray Oldenburg argues the necessity and importance of these three realms in our living environment in order to achieve a healthy and balanced life. He points out that these places are essential for civic life to flourish and to foster strong ties between community members. Bars, coffee shops, parks, bookstores, barbershops, and community centres are all considered third places because they are inclusionary social places. In this list of places, I am particularly interested in coffee shops for three reasons:

1- They welcome a highly diverse population
2- They are an interesting socioeconomic indicator
3- They respond to the private market.

Typically, when a café opens, it’s a sign that the neighbourhood is doing pretty well – as opposed to a dollar store for example. I also noticed that coffee shops tend to cluster in high pedestrian areas and in neighbourhoods’ centres, which makes sense. So, out of curiosity, I decided to locate coffee shops and their walkability.


How I did it

It’s incredible how a simple idea like this one can translate into a technically complex procedure. However, once you’ve dipped your toes in the water, it becomes less arduous for subsequent projects. Here’s in a nutshell how I did it.

1- Collect and prepare available data: Data from Open Street Map provides categorized and geolocated places around the world. A simple SQL query is executed to specifically select cafés in Toronto.

2- Build the network: Open data from the City of Toronto provides the street layer, which will be useful to add cafés onto it. With ArcGIS, a network dataset was built in order to do a network analysis.

3- Do a network analysis: The tricky part is to represent the walkability of coffee shops. A simple euclidean distance buffer will not represent the true walking distance since there are barriers where pedestrians simply cannot walk into (e.g. buildings, private properties, railways, etc.). In other words, walking distance has to be calculated based on where pedestrians can walk. To do this, I use the tool “Make Service Service Area” and created 5 buffers to represent walking distance.


Euclidean distance (bird's eye view)

True walking distance based on the street network


4- Export data to Carto: Carto is a great service for WebGIS. Once I saved all the data, I exported it via the Cartodb plugin for QGIS. I used QGIS since the procedure with ArcGIS Desktop was too complicated.

5- Formatting the map: This is the last and fun part and it probably only took 10% of the total time I put into this project. Once it’s exported on Carto, all is left is to format your map to make it visually intelligible and attractive.

This map is to be taken as a fun tool and has some limitations. The data from Open Street Map is not the most up-to-date and some newly arrived coffee shops might not appear on the map. It still gives a good general idea which areas seems to be most caffeinated.