For test purposes, we collected data from an art auction website which contains data about each item on auction:
We downloaded a test set of 200 visual art auction pages and found out that 27% was made with oil, 7 % were prints, and 3 % painted with acrylic. The top 4 art selling cities are Berlin (15 % of available art in the auction), Otzberg (6 %), Frankenthal (3 %), and Essen (3 %).
Art auction data gives a new insight into art: what is available, what are the average or median prices of different art forms such as acrylic paintings, or what are the most active art selling cities.
Perhaps such data could also be used for following art trends among specific geographical regions. For example, based on our dataset, Planegg turned out to be a very lively art selling municipality. We also noticed interesting niche (?) art forms, such as “Afrikabild”
During the day two, our efforts were mainly focused on processing the data of rents in Berlin. These three visualizations portrait the average price of rental apartments, price is euros per square meter. The data is scraped from the net, we did the calculations and visualization during our workshops at DMY Berlin.
This one is by Ottavia:
This one is by Anu:
And this one by Chiara:
We scraped the data of 10 000 flats for rent in Berlin. Today we have been processing the data in our workshop at DMY Berlin festival. One interesting result is this, the most expensive apartment in Berlin, if you look at price per square meter. This aparment in party-street Simon-Dach-Strasse costs 28 euros per square meter, 1400 euros per month.
(Link to google cache)
Here’s a very simple visualisation of German Bundesregierung supporters. All these companies have donated money to the two governing parties FDP and CDU. Look previous post for sums.
This is made with Cytoscape and the data is from www.parteispenden.unklarheiten.de
Where does the German government get it’s money? Here’s the list of ten top campaign donors from beginning of 2000′s. I’m collecting the data to make network analysis. Here’s the source.
|German Bank AG
|Daimler Chrysler AG
|German Bank AG
|VB of the Chemical Industry Association
|Substantia AG (VM Holding AG)
|VB of the metal and electrical industry NRW e. V.
|Bancassurance German Investment Advisory Ltd. DVAG
Markus Gräser, a berliner open data enthusiast visited our booth. He is interested in mapping services, and suggested we should map our rent data.
He has also made one map-based service himself: kinoatlas.de
It shows movies going in Berlin on a map. Great work!
We are currently having the workshop on how to process open data at DMY Berlin. Our idea is to gather data on housing prices on Berlin. We have scraped 10 000 house prices, locations and sizes and are currently transforming them to excel file.
People have also brought their ideas to us. Here’s few:
- www.berlinerpool.de has information on artists. How could we visualize it?
- www.parteispenden.unklarheiten.de has info on party spending. How to make it more easily understandable?
The We Love Open Data team is now in Berlin – ready to rock. Here are some first photos from Wednesday from the opening evening of the DMY. Welcome to do open data every day at 15.00!
We have three workshops at DMY Berlin festival. Feel free to participate.
Gathering Open Data
The We Love Open Data team is on a mission to find interesting open data sources from Germany. The data will be used in the data visualization workshops during the two following days. We are looking for suggestions for interesting data sources – please come and have a chat with us.
Time: THURSDAY 2 JUNE, starting at 15:00.
Processing Open Data
Open data does not have much value unless it’s in a format that is easy to manipulate. Our basic tutorial will give you some valuable tips on how to get started with working on open data. First we gather some data from the web, then we process it. The advanced workshop will focus on complex data sets with current political/cultural relevance. Please bring a laptop, data will be provided. Tools: spreadsheets, database programs, web-based platforms.
Time: FRIDAY 3 JUNE, starting at 15:00. Duration 1-2 hours (basic tutorial) or 4-5 hours (advanced). Max 10 participants.
Visualising Open Data
Time: SATURDAY 4 JUNE, starting at 15:00. Duration 1-2 hours (basic tutorial) or 4-5 hours (advanced). Max 10 participants.