Philippe Caron

Designer and web developer

Accra Trotro network map. Data viz and network mapping to explore uses of GTFS data

Map design for Trip Lab at concordia University.
2016 as a designer


Collaborators
Trip Lab team, Zachary Patterson, Natalie Wiseman


Case Study Written July 27th, 2018

A network of taxis running on predefined routes.

In the winter of 2016, I was approached by the Transportation Research for Integrated Planning Lab (TRIP Lab) to design a public transit map. One of their recent project had mapped data coming from bus routes in Accra, called trotros. From the data they could collect, and based on the knowledge of the locals, we could understand that trotros where a form of organically scaling public transportation network. The bus routes were planned by a city wide authority, while individual bus routes where ran by individuals. From how I understand it, we could compare this to a fleet of privately owned taxis, running on predefined routes. In theory, this looks like a formidable idea, although it had some drawbacks from a mapping and design perspectives. I should mention that I wasn't able to experience any of it physically, therefore my lens only involves looking at this design challenge from a data viz perspective.

The brief involved creating a map that would serve to understand the extent of the trotro network. Rather than focusing on the accuracy of the map, the goal was to rather show the potential for designing a map that was readable and easy to understand. Whether I was able to achieve this goal is still debatable, but the outcome certainly was interesting to look at!

Schematized map of the Accra trotro network. Network lines are organized on a hexagonal grid. Six colours represent six areas of the city. Some colours overlap towards the centre of the city.

Final map of the Accra trotro network Illustrator 2016

Index of several hundred trotro lines.

Bus route table Illustrator 2016

Existing infrastructure

The main challenge with a scalable and organic bus network is that it becomes challenging to keep up with its expansion, and subtle modifications that would happen over time. Bus drivers would sometimes take small detours to accommodate riders, or avoid some traffic. Given this context, even the data collected using GPS and a phone app would pick up these inconsistencies and make it difficult to understand the main planning of the bus network. Additionally, whether all routes were in operation or not at the moment of data collection remained unknown because there were no global systems or timetables imposed upon the drivers.

Mapping the network

The TRIP Lab team had previously mapped these routes with a GPS and a stop log to know approximately how many, and which bus routes were active, as well as where they were stopping. With the data, they were able to produce a map of the network. The result of this exercise was a complex network of spaghetti lines that were geographically accurate, but difficult to follow. The only tags present were at the beginning and end of the routes, with no clear indication of where bus routes connected, where they shared a street segment or terminus or simply where they stopped

The first step was to understand the data collected during this mapping. Officially, route numbers go up to the 900s, but in actuality, there are less than 900 routes. The first observation was that there existed a densification of the routes towards the city center, as well as on the main highways and major intersections of the city. I wanted to know how the bus routes interacted with each other, and how commuters in Accra could benefit from a better understanding of these interactions.

The TRIP Lab provided me with a slightly untangled version of the map that represented roughly the stops and terminuses for the bus routes. This map showed how the bus routes interacted together and formed a rough hexagonal shape in the city center, with routes that extended beyond in the sprawls of the city.
I designed a network of 21 nodes that joined in a hexagonal shape in the city center. I had this map printed in large format, and started individually tracing routes so that they followed as best as possible the network of 21 nodes. This method was not optimal. I had to manually combine data from the terminus node map, the geographically accurate bus routes map, as well as with geographical data about roads, neighborhoods and landmarks. In a better world, the process could have been done programatically. Along the way, 44 extra nodes were created to better represent unique terminuses that had different names. So there it was: a network of 65 nodes. I just had to make it look nice.

Raw data from a trotro bus network are outlined with yellow lines. Numbers from 1 to 65 mark the most dense nodes of activity.

Major terminus nodes mapped QGIS 2016

A messy network of light purple lines on a dark canvas.

Cleaned nodes projected on major terminus map Illustrator, QGIS 2016

A simplified network of light purple lines on a dark canvas.

Cleaned nodes Illustrator 2016

A triangular grid fills a rectangular area. Dots are roughly placed at the intersections of the triangles and inter-connected by thicker purple lines.

Aligning the cleaned nodes on a 60° grid Illustrator 2016

A triangular grid fills a rectangular area. Dots are placed at the intersections of the triangles and inter-connected by thicker purple lines.

60° grid nodes Illustrator 2016

The general shape of a network aligned on a triangular grid is overlaid on top of the same unaligned network.

60° grid nodes projected on major terminus map Illustrator, QGIS 2016

Many dots connected by thin lines. The lines create a network that is difficult to read. Thicker green lines attempt to align the main connections of the network on a triangular grid.

60° grid nodes projected on major terminus map with line numbers. Illustrator, QGIS 2016

Schematizing the network

As we remember, this project was about designing a neat looking map to inspire a need for good visual communication of the public transit service. In a sense, it was never made to be useful or precise: It was made to be inspirational. With this information in mind, I knew the best approach to this would be inspired by modernist schematized maps. Maps such as Harry Beck’s London Tube map of 1920, as well as its contemporary descendant. Massimo Vignelli’s MTA map for New York of 1972. But surely the most influential maps were Vignelli’s Washington Metro trial maps. This is where I got the idea to use a 60° grid. This grid could fit the hexagon of Accra’s Trotro network, as well as provide control over the extension of the network beyond the city center.

A messy network of light purple lines on a dark canvas. Dots yellow and lines attempt to mark the most connected nodes of the network.

Schematized hexagon map projected on major terminus nodes map Illustrator, QGIS 2016

A simple network of dots and yellow lines. The main cluster of nodes is outlined by a dashed yellow line and forms a distorted hexagon.

Schematized hexagon map Illustrator 2016

A simple network of dots and white lines. The main cluster of nodes is outlined by a dashed white line and forms a distorted hexagon. Yellow lines show the network would conform to a strict triangular grid.

Aligning hexagon map Illustrator 2016

Several points on a dark canvas are connected by yellow lines. The network forms a series of triangular shapes.

Hexagon map Illustrator 2016

To help navigation, I split the network in six regions, roughly taken from the main nodes identified previously. North-East, East, South, South-West, West, North-West and center. In the city, these regions would also correspond to different areas along highways, landmarks or waterfronts. This segmentation into six pieces also helped understand how all these areas joined in the city center. Doing this, I also found out that multiple routes were taking similar paths across the city, between Accra’s city center and Kotoka International Airport, along highways and main arteries. In trying to help navigation, I ended up discovering patterns in the network that were previously invisible to me.

After schematizing the map, I ended up cleaning up my file, aligning and offsetting lines so they would not overlap and create false connections. I added route numbers onto the actual lines, so that we would understand which lines took what paths. I tried to add a tag at the beginning and at the end of each route. Some lines had different paths for round-trips, which added further complexity to the map.

A multicoloured network aligned on a triangular grid is overlaid on top of the same unaligned network.

Final map before refinements Illustrator, QGIS 2016

Six networks of points and lines are placed on top of each other. Labels describing the connections are roughly placed near the lines.

Placing route numbers on map Illustrator 2016

Dark lines on a light canvas demonstrate how to offset the lines of a triangular grid in a way where no line overlaps another.

Offsetting lines Illustrator 2016

Six networks of points and lines are placed on top of each other, offset in a way where lines never hides another. The offset grid is visible underneath the networks. Labels describing the connections are roughly placed near the lines.

Placing lines on offset guides Illustrator 2016

Six networks of points and lines are placed on top of each other, in a way where lines never hides another. Labels describing the connections are roughly placed near the lines.

Offset final map before aligment of route numbers Illustrator 2016

Conclusion

The final result is very pleasing and interesting to watch. Today, two years after the completion of this project, I still find it interesting to watch this map, and I still would like to know if it did inspire any sort of automated mapping project as we intended from the start. I find the idea of an organically scaling transit network fascinating. Especially when I see nearly empty buses driving around the sad Canadian suburb I live in, and keep wondering if the problem is the city, or the transit system...