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Grüessech!

My name is Timo Grossenbacher, I live in Switzerland, and I work as reporter/coder for SRF Data, the data journalism unit of Swiss Radio and TV, based in Zurich. There I analyze and visualize data, investigate data-driven stories and manage projects.

I’m a geographer by training. I hold a MSc degree in Geographic Information Science from the University of Zurich.

I also teach data journalism for political science graduates at the University of Zurich and for interaction design students at the Zurich University of the Arts. Over at DataCamp, I'm an instructor for «Communicating with Data in the Tidyverse» and «Introduction to Relational Databases». I co-organize Hacks/Hackers Zurich and created Rddj.info to help data journalists get started with R.

Check out my latest blog posts or the growing list of projects I have published.

Contact me through timo(at)timogrossenbacher.ch (PGP), check out my GitHub account and follow me on Twitter.

Disclaimer: This site uses the free and open source Matomo for web analytics. All collected data remain on my server.

Recent blog posts

Bivariate maps with ggplot2 and sf

In this post we show how to create highly aesthetic bivariate choropleth maps, including annotations and a custom legend –exclusively in R.

(Big) Data Journalism with Spark and R

At Swiss Public TV and Radio (SRF) we recently published an investigation of the “Collection #1-5” password leaks. In this post, I show how I searched through 900GB+ of data with Spark and R.

Ist E-Voting in der Schweiz sicher?

Eine journalistische Einschätzung zur E-Voting-Schwachstelle, die er Chaos Computer Club Schweiz veranschaulicht hat.

Categorical spatial interpolation with R

How I used the kknn and ggplot2 packages together with some parallel computation to spatially interpolate several hundred thousand points.

A selection of projects I was involved in

Here’s what we found in the Collection #1-5 password leaks

Image: srf.ch March 2019. Direct link. This was an investigation deep into the heart of the “Collection #1-5” password leaks that appeared in the web in early 2019. We showed that more than 3 million Swiss email addresses and – more disquietingly – over 20’000… Read more

Deep Fakes – indistinguishable from magic

When so-called “deep fakes” popped up in late December on Reddit, they caused quite a stir. At SRF Data, about half a year later, we wanted to explain the technology with self-made examples.

The dark chamber of the Swiss prosecution system

For the first time, my team has profoundly investigated the (often hidden) statistics behind the secret court orders that govern invasive actions by law enforcement. The number that resulted in the end is rather disquieting: 97 percent.

The Swiss criminal justice system employs an intransparent algorithm to assess its inmates

In this research we could show that the Swiss criminal justice system uses a simple, but intransparent algorithm to categorize inmates into three risk classes: A, B and C. Under our pressure, the inner workings of the algorithm were finally published.

The Swiss police use a dubious risk assessment software

In this investigation, I could show that the Swiss police uses a dubious software to assess the risk of individuals of being a “potential danger for the public”. While the use of the software had been publicly known until that point, I exclusively dug out some studies that show that the software actually performs not very well. In fact, out of three people it deems “potentially dangerous”, two actually weren’t.

Identifying a large number of fake followers on Instagram

Being a so-called influencer is the dream job of the moment for a lot of young people. However, our analysis shows that a large part of the business is based on fake Instagram accounts.

Pendlerland – A personalized view on Swiss commuting patterns

For some, Switzerland is one big city with high-speed trains and highways functioning as tram and bus lines – people commute from Bern to Zurich like they would from a district to another. Indeed, Switzerland has some of the highest commuter rates in the world, and there’s a plethora of statistics available.

Mapping urban sprawl in Switzerland

Urban sprawl is one of Switzerland’s (few) biggest environmental problems. In this interactive explainer, we provided readers with a very personal perspective on the phenomenon.

Here’s how 670’000 people speak German

Based on crowdsourced answers to 25 dialect questions (e.g. how somebody pronounces a word), we produced very detailed maps of the regional distribution of German word pronunciations. We collected over 670’000 points representing residencies of people and aggregated them to hexagons in order to display them on interactive maps.

Vested interests of Swiss universities

In our largest data-driven research so far we examined the vested interests of Swiss universities. We researched, among other things, more than 1000 secondary employments of professors and more than 300 sponsored professorships. The investigation resulted in publications in dozens of different radio and television programs of the Swiss Public Broadcast SRF.

Rddj.info

As I outlined in a post, the statistical software environment R is becoming more and more popular among journalists. However, finding an entry point to the R programming language is not that easy, especially for people without programming experience. That’s why I build the continuously updated Rddj.info – a resource collection for learning how to do data journalism with R.

Implications of the new Swiss surveillance law

End of September 2016, the Swiss people accepted a new federal law that grants the intelligence agency new competences, e.g. the agency can now search Internet traffic that “leaves or enters” the country for suspicious keywords (similar to XKEYSCORE).
Questioning whether one can actually speak of an “outside” or “inside” when it comes to Internet traffic, we wanted to explain the reader if it is theoretically possible to be surveilled when browsing a Swiss website.

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Machine Learning in R: Start with an End-to-End Test November 13, 2019 David Lindelöf
The Colour of Everything November 13, 2019 Data Imaginist
When Cross-Validation is More Powerful than Regularization November 12, 2019 Nina Zumel

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