Today a colleague at the Netherlands Space Office showed me a new Copernicus service, the ground motion service (EGMS). Quite an amazing data service to explore. Earlier I wrote about the European forest fire information service (EFFIS), and its use as a proxy for the fighting going on due to the Russian invasion of Ukraine. EGMS is another service based on satellite remote sensing, here radar telemetry tracking the subsidence or rising of the ground. As far as I understand it can’t ‘see’ soft materials (peat land subsiding e.g.), only sees hard materials (solid ground, or buildings on softer grounds).
The images are quite amazing, and the data is provided right alongside it.

First an overview of northern Europe. Blue is rising ground, red is sinking ground. Sweden and Finland show rising ground, this is still the bounce back of the earth since the last ice age ended when the tremendous weight of glaciers was removed. At the tip of the arrow you see subsiding ground, this is the result of gas extraction in Groningen province.

Zooming in on Groningen province, here’s the data for a single house, subsiding 4 centimeters in the past 6 years. No wonder many homes are getting damaged in that area, both from subsidence as well as from the earthquakes that accompany it.

For comparison, here’s the data from the street I live on. It shows a subsidence of 6 millimeters in the past 6 years.

And here’s the same data as in the graph in the image above, but exported from the Copernicus services as an SVG, and pasted here as text.

-14-12-10-8-6-4-202468101214Displacement mm2016011120160428201608142016113020170318201707042017102020180211201805302018091520190101201904192019080520191121202003082020062420201010Measurement dateORTHO Vertical: 20dXRnBSzzDataset: Point ID: Position: Mean velocity: RMSE: ORTHO Vertical20dXRnBSzz3242050.00 N 4007550.00 E -0.60 m-1.10 mm/year0.40 mm

People often ask me how I stay informed, and always seem to know even about smaller initiatives around the topics I work on. Part of that is what I call ‘Radar’. With Radar I automatically collect all the Twitter messages that mention keywords I am interested in, and detect the web addresses they mention. Those web addresses are evaluated on their type (is it a blog, a video, a general site, a presentation, a photo?) and counted as to how often they are mentioned.

runningtotalsradar

Running totals for Radar: found 350k people, mentioning over 1 million URLs

Radar then presents me with overviews of all URLs mentioned on Twitter in the past day, or week, on the key words I follow. This way I find not just the ‘big’ websites, but also the smaller events, initiatives and discussions, that are mentioned by smaller communities. Next to URLs Radar also tracks who is mentioning certain topics, which basically gives me a list of suggestions of who to maybe follow on Twitter, or who’s profile I may want to look at to see if they also blog about the topics I am interested in.

urlmentionsopendata

Most mentioned URLs in 4566 tweets on Open Data in past 24 hours

peoplementioningfablab

The 47 people tweeting about FabLabs today, new people highlighted

What comes out of my Radar then may get added to my feedreader, or to my bookmark collection, or to my notes collection in Evernote. Radar is the serendipity antenna that scoops up a wide variety of things. To me, whatever is being mentioned on Twitter is like the froth on the waves: it is not all that meaningful by itself, but shows me where there is movement and energy of interaction. That points me to the places and people that make up the wave below the froth. Which is where the significant info is.

Radar at first was a bunch of php scripts I wrote myself that ran on my laptop and which I started manually in sequence. My coding skills aren’t all that great though, so ultimately I asked Flemming Funch to clean things up for me. That meant he coded the scripts from scratch, with only my original outline of what I wanted remaining. Now it runs permanently on my VPS with a basic web front-end for me to explore the output (see screenshots).