Santtu Pyykkönen - Geospatial Expert & Technical Consultant

Data for urban mobility planning

Clean growth, climate action, you name it - cities are forced to plan urban mobility with new perspectives.

Modeling urban flows - for what?

Clean growth, climate action, you name it - cities are forced to plan urban mobility with new perspectives. As I saw an interesting analysis from Amit Levinson on urban mobility during the #30DayMapChallenge, I wanted to give the analysis a try and see how the cities where I’ve lived would look like from this point of view.

As a result, you can observe what are the probable cycling paths within the Metropolitan area of Helsinki. 1000 routes were calculated on the basis of population density and the distribution of urban destinations (e.g. shops).

From personal living experiences to data-driven analysis

For me personally, it was interesting to analyze these cities as urban environments I’ve lived in. For example, the latter map resembles probable cycling routes throughout the city of Helsinki, whereas the first displays routes by car. The results definitely are interesting and they somewhat reflect my personal experiences on moving around in Helsinki.

The original analysis was built on randomness, on an idea where we locate randomly 1000 origin and destination locations within the city and we calculate the most probable route a car driver or a cyclist would take on the basis of speed limits and cycling infrastructure, among other factors. This definitely shows how cities are structured. Nevertheless, I wanted to reduce the randomness by adding probability via data on population and urban amenities such as shops and other places.
Helsinki - population count versus destinations count.

Above, you can see how the population is distributed into different places within the city as well as what are the “hot spots” of shops and amenities. So, as I wanted to include the urban departure and destination sites in the analysis and reduce the randomness, I added to the analysis the clusters of population and possible urban destinations, such as amenities (e.g. schools) and shops (e.g. supermarkets).

I think that this analytical framework at least gives a quick overview of the demand for example for cycling infrastructure in different parts of the city and gives us some practical tools for predicting the routes that the citizens will most probably take.

León and Querétaro under inspection - some preliminar insights

The majority of the time I lived in Mexico, it was in León and Querétaro, which are mid-sized cities (1-1.5 million residents) in Center Mexico. If I compare the three cities of interest as a citizen as well as an analyst, it seems to be that in Helsinki there is an established cycling infrastructure which in theory could cover the city quite well, whereas in Mexico there are large neighborhoods where you need to use cycling routes that are somewhat improvised within the city.

Querétaro, Mexico, by bike.

León, Mexico, by bike.

It is interesting to compare the population distribution of these cities to the distribution of amenities and shops in Querétaro and León. This is where I think we lack data, but more on that later.

Querétaro - population count versus destinations count.

León - population count versus destinations count.

When analyzing the urban flows by car it seems to be that this method captures how car-oriented planning has structured our cities. The highways and motorways stand out quite emphatically from the maps below.

Querétaro, Mexico, by car.

León, Mexico, by car.

Data quality as an issue in Mexico

It seems to be that the level of detail in OpenStreetMap in León and Querétaro is not that high that in Helsinki. If this analytical path was to take further, it would be worth a while to analyze how good is the data quality with DENUE, a dataset on “economic units” from the National Institute of Statistics and Geography (INEGI) in Mexico. It seems to have a nice API, in addition to an R-package called inegiR, which could be very useful for taking this analysis further.

Modeling urban flows: fresh perspectives to an ancient challenge

I’d conclude this quick detour to urban mobility by marking the following point: nowadays we have up-to-date open data as well as powerful open source software for urban planning related analysis.

OpenStreetMap and Humanitarian data exchange are helping cities to really change the way they plan the cities all around the world. There’s a lot of data, no doubt about it.

As cities evolve, also the data evolves. An important milestone would be to move towards automated continuous monitoring instead of irregular analysis.

Open data and open source tools are here to help cities to make transport and urban mobility safer, easier, and more efficient.

Sources:

Tools:

I used R for the whole process. I have published a gist that demonstrates the code that was used for the city of León (Mexico), but can be applied one’s city of interest.