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Tuomas Väisänen 📼🧟‍♂️✨ NEW TOOL FOR <a href="https://vis.social/tags/MOBILITY" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#MOBILITY</a> DATA <a href="https://vis.social/tags/DATAVIZ" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#DATAVIZ</a> ✈️🚚🚗🏃 Do your visualisations of origin-destination matrices look like a mess of criss-crossing hairs? Maybe you could benefit from our <a href="https://vis.social/tags/EdgeBundling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#EdgeBundling</a> tool by me, Oula Inkeröinen, <a href="https://mastodon.social/@miladmzdh" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@miladmzdh</a> and Olle Järv! 🛠️ The tool: <a href="https://doi.org/10.5281/zenodo.14532547" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://doi.org/10.5281/zenodo.14532547</a> This tool is an output from the <a href="https://vis.social/tags/MobiTwin" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#MobiTwin</a> project funded by the <a href="https://vis.social/tags/EuropeanUnion" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#EuropeanUnion</a> through the <a href="https://vis.social/tags/HorizonEU" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#HorizonEU</a> programme.

Tuomas Väisänen 📼🧟‍♂️To support using this data, we have also developed an <a href="https://vis.social/tags/EdgeBundling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#EdgeBundling</a> tool to aid in visualising complex origin-destination flow data: <a href="https://doi.org/10.5281/zenodo.14532547" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://doi.org/10.5281/zenodo.14532547</a> We also provide a ready-made edge-bundled network on NUTS 2 level lead by Oula Inkeröinen: <a href="https://doi.org/10.5281/zenodo.14380382" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://doi.org/10.5281/zenodo.14380382</a>

Tuomas Väisänen 📼🧟‍♂️Great! My abstract got accepted into <a href="https://vis.social/tags/LBS2025" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LBS2025</a> conference!I will be presenting my work on <a href="https://vis.social/tags/StudentMobility" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#StudentMobility</a> and an <a href="https://vis.social/tags/EdgeBundling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#EdgeBundling</a> tool.This year Location-based Services is in Espoo, so I can just ride the metro there. Travel budget will be around €15. Good times.Anybody else going to <a href="https://vis.social/tags/LBS" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LBS</a>?

Tuomas Väisänen 📼🧟‍♂️Alright, I will share my map for Day 2 of <a href="https://vis.social/tags/30DayMapChallenge" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#30DayMapChallenge</a> again. It depicts regional student <a href="https://vis.social/tags/mobility" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#mobility</a> across Europe a few years ago. As the data contains 200 000+ flows, straight line geometries make a fuzzy mess, but through aggregation and a technique called <a href="https://vis.social/tags/EdgeBundling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#EdgeBundling</a> one can reduce visual clutter in the image.The data and methods will be published openly in the near future. The work is a part of the <a href="https://vis.social/tags/MobiTwin" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#MobiTwin</a> <a href="https://vis.social/tags/EUHorizon" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#EUHorizon</a> project.

Tuomas Väisänen 📼🧟‍♂️For those working in <a href="https://vis.social/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Python</a> and <a href="https://vis.social/tags/mobility" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#mobility</a> or <a href="https://vis.social/tags/network" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#network</a> data, and who want to get <a href="https://vis.social/tags/EdgeBundling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#EdgeBundling</a> to work, check out this repository: <a href="https://github.com/xpeterk1/edge-path-bundling/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://github.com/xpeterk1/edge-path-bundling/</a>I tried with our project data and it works nicely. It calculates really fast and uses little resources.Potentially interesting to <a href="https://fosstodon.org/@underdarkGIS" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@underdarkGIS</a> and <a href="https://fosstodon.org/@martinfleis" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@martinfleis</a>

Tuomas Väisänen 📼🧟‍♂️Hey <a href="https://vis.social/tags/spatial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#spatial</a> <a href="https://vis.social/tags/R" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#R</a> folks, is it possible to extract geometries from <a href="https://vis.social/tags/ggplot" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ggplot</a> as files? That is, the point, line, and polygon geometries in the graphs to <a href="https://vis.social/tags/geopackage" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#geopackage</a> or <a href="https://vis.social/tags/shapefile" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#shapefile</a> etc.It is possible with <a href="https://vis.social/tags/seaborn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#seaborn</a> and <a href="https://vis.social/tags/matplotlib" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#matplotlib</a>, but that's Python not R. There's a nice and lightning fast <a href="https://vis.social/tags/EdgeBundling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#EdgeBundling</a> technique in the newest ggplot, which is very promising for the <a href="https://vis.social/tags/visualization" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#visualization</a> of massive <a href="https://vis.social/tags/mobility" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#mobility</a> data.There is also something in <a href="https://vis.social/tags/HoloViews" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#HoloViews</a> but I do not understand that library at all.

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