I had the chance to collaborate once again with Voilà on a scrollytelling project for CPP Investments about their goals to be carbon neutral. I'm pretty good at animating gooey blobs now. Try it here and see how it was done: https://chezvoila.com/project/changingworld/
I illustrated a long-form article about the shooting on the Christmas market in Strasbourg. It was a collaboration with the excellent Le Lab des DNA et de L'Alsace with Emmanuel Viau's team.
I had the chance to collaborate once again with Voilà on a scrollytelling project for the Canadian Climate Institute about the cost of climate change on the infrastructures. I helped with the scrolling animations. Try it here and see how it was done: https://chezvoila.com/project/infrastructure/
I illustrated a long-form article about the crash of Mount Sainte-Odile. It was a collaboration with the excellent Le Lab des DNA et de L'Alsace with Emmanuel Viau's team.
I had the chance to collaborate with Voilà on a dashboard project for an important client doing political surveys analytics. I can't disclose much about it. But it was a nice collaboration on a tool to filter stacked viz.
We made a prototype to visualize the 2018 Camp Fire, "the most destructive wildfire in California's history, and the most expensive natural disaster in the world in 2018 in terms of insured losses" [Wikipedia].
The Landsat pictures are stunning.
We needed a precise view of fire damage with vegetation fire index as a context.
The dataset is pretty big, but zooming and filtering on large dataset on maps is one strength of the OmniSci platform. So we can filter by damage level and zoom down to individual buildings.
Switching to a sattelite basemap, we can see how much the shapefiles match the buildings. This tool could be used in rescue missions or even to prevent fires in the future by scheduling maintenance or turning off parts of the electrical grid in high-risk areas.
We needed some new functionalities like:
- Better syntax highlighting, autocomplete, SQL formatting and validation
- Better table/column browser with filtering
- Query history with preview data
- Reusable query snippets
- Run multiple queries in the same context instead of notebook-style
- Shapefile query results
So after the phase of requirements gathering and research, we started wireframing.
I learned a lot about maps visualization and sensor data. Here is an early prototype.
That's where I developed Cirrus.js. Most data we currently have is raster data, but there's always the occasional time series.
And temporal coverage chart.
But most of the visualization work is on map polygons and raster data. Here is for example a screenshot of the colour palette dropdown.
The visualization below is interactive!