Warm Up 1 to 15 September Online

Warm up with the city

Basel keeps a careful record of itself, and almost nobody ever reads it. Consider this an invitation.

A gentle start before October: open one of the datasets the city publishes about itself, follow whatever makes you curious, and show the rest of us what you found.

Open to everyone #WarmUpAmRhein

Before you talk yourself out of it

1

Alone or together

Enter on your own, or with friends. Everybody who worked on it gets credited.

2

However you like to work

There is no right way in. Explore it however suits you. The result just has to end up on GitHub.

3

An evening is enough

This is a warm-up, not a marathon. Small and finished beats big and half done.

4

Spotlight feature

The entries we like best get featured on this page, with everyone who worked on them named.

Submissions are closed

What you get out of it

Your names on this page

The entries we like best get a spotlight right here, with everyone who worked on it named, and a link to what you made.

A shoutout on LinkedIn

We post about the ones we feature and tag you, so your work reaches people well beyond Basel.

Results

Here is what people made.

Spotlight

When should you go for a walk?

Matthis Lahargoue

I built an interactive map that lets you move through the day and see how typical PM2.5 levels changed along Basel’s measured tram corridors, using historical measurements from sensors mounted on BVB trams. What surprised me was that 14:00–15:00 emerged as the cleanest observed window, with lower levels than at 10:00 and especially 20:00 on most comparable days. Next, I would extend the analysis to other seasons and weather conditions to see whether the pattern holds.

Why we picked it: Creative idea, great execution, and a map that makes the data very easy to explore. We were surprised that 14:00 to 15:00 is the cleanest window for a walk, and by how fast levels climb again afterwards. Well done.

View on GitHub

Spotlight

Analysis of public bike parking compared to income across Basel's districts.

Phil Leu

I tried to answer the question if public bike parking is influenced by the income of the district.

Why we picked it: A sharp question, answered honestly. We liked the per-inhabitant view of bike parking, the Bruderholz and old town outliers, and the finding that parking is otherwise spread quite evenly regardless of income. Well done.

View on GitHub

Spotlight

Stromkalender

Sandro

Die stillsten Werktage im Basler Stromnetz, zum Raten und Aufdecken. Ohne Wochenenden zeigt der Verbrauch einen Kalender. Welchen?

Why we picked it: This submission looked at something most of us rarely think about: the rhythm of Basel's electricity grid. A dataset created for infrastructure can end up telling you something about people.

View on GitHub

The challenge

The invitation

Basel keeps a remarkably detailed record of itself. Its trams measure the air as they cross the city. Every roof has an estimate of the sunlight that reaches it. The river has been screened for substances since 1993. Hardly any of it has been looked at by somebody who was simply curious.

That is what we are inviting you to do. Open one of the datasets, follow a question you actually want answered, and build something small around what you find.

What that might look like

The entries we enjoy most tend to do something rather than state something. A small page that lets a visitor explore the data for themselves. An interactive map where clicking a roof or a tram stop reveals what sits behind it. A notebook somebody else can run and change. A single chart still works when it makes one point sharply. The useful test is whether the person looking can discover something on their own, instead of being handed a finished picture.

Bring people if you like

You can work alone or with others and it makes no difference to how we read your entry. When an entry is featured we name everyone who worked on it, so nobody gets left off the credit.

Keep it small

One clear idea, finished, is worth more than an ambitious one abandoned halfway, and an evening is genuinely enough. How you go about it is up to you. The only thing we ask is that the result ends up in a public GitHub repository, so that we can look at it.

Get started

Choose a dataset and open it. Each one opens straight in your browser, and there is nothing to install before you begin.
Get the data out. Every dataset has an export button, and an API as well if you would rather work that way.
Follow your curiosity. Sort the columns, look at the outliers, and ask the question that occurred to you when you first read the title.
Build the smallest thing that answers it, and then write a couple of sentences about what you found.
Put it in a public GitHub repository, paste the link below, and name anyone who worked on it with you.

If you have never used GitHub before, open the box underneath. It takes four steps and no terminal.

You do not need to install anything and you never have to touch a terminal.

1 Make a free account at github.com.
2 Click the plus in the top right, then New repository. Give it a name, choose Public, and create it.
3 On the new page, click uploading an existing file and drag your files straight into the browser. A spreadsheet, an image, a PDF, a notebook, whatever you made.
4 Copy the address from the browser bar. That is the link you paste below.

A README file explaining what you made in a few sentences helps us a lot, but it is not required.

How entries are picked

We are looking for the entries that made us look twice.

Curiosity counts for more than complexity here. Something small that lets us discover one surprising thing beats something elaborate that only confirms what everybody already assumed. Choose a question you genuinely wanted answered about the place you live and the rest tends to follow.

The entries we find most creative are given a spotlight on this page, naming everyone who worked on them, and we write about them on LinkedIn with the hashtag.

Datasets

All open, all free to use. Start with whichever one looks interesting.

Particulate readings from the roofs of BVB trams

Open Data Basel-Stadt

PM2.5 and PM10 from microsensors mounted on tram roofs, so the measurements move through the city. 933,000 rows. The most interesting thing on the portal.

Licence: CC BY 4.0

Open dataset

Solar potential of every roof and facade

Open Data Basel-Stadt

Modelled solar yield for 34,955 surfaces across the canton, with geometry. Maps well, and the question of which roofs are wasted answers itself.

Licence: CC BY 4.0 combined with the OpenStreetMap terms. See the linked document.

Open dataset

Rhine monitoring station: substances in suspended solids

Amt für Umwelt und Energie, Open Data Basel-Stadt

The binational station at Weil am Rhein has screened the river for dozens of substances since 1993. Nearly 64,000 analyses. If you want the chemical industry question, this is it.

Licence: CC BY 4.0

Open dataset

Cantonal electricity consumption, every 15 minutes

Industrielle Werke Basel, Open Data Basel-Stadt

Every 15 minutes of grid draw across the canton, 513,000 rows. Industrial load has a shape, and holidays and shutdowns are visible in it without any modelling.

Licence: CC BY 4.0

Open dataset

BVB passenger numbers, 2020 onwards

Basler Verkehrsbetriebe via Open Data Basel-Stadt

Only 283 rows, weekly then monthly, straight through the pandemic. Small enough to read in one sitting. Check its licence before you use it: this one is not like the others.

Licence: Open use. Must provide the source. Commercial use only with permission from the data supplier.

Open dataset

We link to these, we do not host them. The data stays with whoever published it. Have a look at the licence before you use one, and credit the source in your entry.

Stuck? Ask us

There is no question too basic. Plenty of people in the Discord have never entered a hackathon either, and someone is usually around.

Ask on Discord

Or write to us info@hackamrhein.dev

Your entry

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