Montreal Metro Incidents 2019 (jan 1) -2025 (may 1)
Concept & Approach
The dataset contains information about incidents at Montreal metro stations, including incident duration, identified primary cause, the metro line where the incident occurred, and more. However, it does not include geospatial data (latitude/longitude).
The dataset is in French. While it would have been straightforward to translate it using AI, I chose to work with it in the original language.
My idea was to create an interactive map that could be filtered by various parameters such as year of incident, incident duration, and primary cause. The size of each datapoint on the map reflects the number of incidents. To make this possible, I merged the incident dataset with a separate dataset containing metro station information, including latitude and longitude coordinates.
Outcome
The inset map with the colored lines—the four metro lines of Montreal—was also created from the metro station dataset, but was later used only as an overlay image.
The area charts were designed to complement the filtered map by providing a clear overview of incidents over the available timeframe. For example, the charts make it easy to see that most incidents were short in duration, and that delays due to repairs in 2024 occurred primarily at a few specific stations.
Example Includes
- An interactive map of Montreal metro stations, where filtering options allow you to see how often incidents occurred.
- A stacked area chart that shows the total number of incidents over a selected period, broken down by the categories you choose.
- A light/dark mode switch for a more flexible user experience.
Figure Friday is an initiative by the Dash/Plotly community, where each Friday a new dataset is shared and participants create a visualization or small app to extract insights from it. The following Friday at 18:00, there’s a Zoom session where some participants explain the thought process behind their work. In the community thread, people also share their code — and when possible, a live demo — to learn from each other.
