Martti, Kim and Miska took part in the “Invisible Cities” event, arranged by the Helsinki World Design Capital (HWDC), at Korjaamo/Helsinki, 4th February 20112, furthering knowledge of Open Data and government transparency.
We Love Open Data was there, working together with the Informaatiomuotoilu (Information Design) team. Together we spent the day, besides talking to visitors, gathering data and producing visualisations around various themes such as age distributions, income levels, political affiliations, and more, for different Helsinki districts. washington state tax id
Martti and Miska from the We Love Open Data team are currently participating in PICNIC Amsterdam 2011, as a part of the Open Data Kitchen gourmet project. Check the Open Data Kitchen website for videos and other stuff. Cialis
Meetup announcement – Anu Määtä about Fablabs + open design – Thursday 4th August 2011 Continue reading Classique Tourbillon ist wirklich die beste Breguet Replica Uhr aus Haus NoobFactory.
Let’s meet for an introduction and talk about Random Hacks of Kindness, 19th June 2011, 1830, at the Radisson Bus Plaza bar, right next to the National Theatre, Helsinki (more info below).
Continue reading 7-Zip
We have our third workshop on DMY Berlin. Kate McCurdy made this visualization about warming costs and building years of apartments. The result is clear: the building year does not affect the warming costs. The buildings made 100 years ago are as good as ones built last year.
We wanted to create a template for an interactive map of city districts in Berlin. The aim of this post is to help people to accomplish the same task individually at home. If you still cannot do it, ask us..
First, we searched for an SVG-map, which would have clear borders and colors. We opened the map in Adobe Illustrator for further editing and saved it in Illustrator file format. We moved all top level districts to their own layers (cut => paste in place), removed all shadows from the borders (select => Same – Stroke width) and combined the paths in a layer (select all => compound path). We compared the city districts in our initial data and districts in the map. They were not 100% identical, so we added couple of layers and divided some districts to two or three smaller districts. We named the original layer as text, moved it to the top and removed high level city district names. After that we saved the file in SVG format.
And here it is:
The next step is to combine the template with the data and visualize information in it.
Interested in renting an apartment in Kreuzberg? Well, look our map of available flats in this stadtteil. This is the theme of our workshop today.
For full link, look here.
There are several handy visualization tools available on the Internet. Some of these sites also provide data sets, which are freely available for use. We listed a few of them, which might be used today:
Processing – open source programming language and environment for creating images, animations, and interactions
Many Eyes (IBM)
Google Fusion Tables – Gather, visualize and share data online
Cytoscape – An Open Source Platform for Complex Network Analysis and Visualization
Open Heat Map – Turn your spreadsheet into a map
Protovis – A Graphical Approach to Visualization
R – for Statistical Computing
With the help of two Berliners we aimed to understand what would be interesting in our housing data. We learned that prizes have been rising a lot and in some areas long-term residents have to move out, because they don’t have money to pay the rent anymore. According to Berliners participating the workshop, one of the hottest districts at the moment is Neukölln. Protests have been organized against this development. Citizens are also becoming more critical against tourists, claiming that the city is turning into a zoo. It has not been very common practice to buy houses, but now when foreigners have started to buy some locals have followed the example.
We also learned that local magazines have been writing a lot about this issue and visualizations about the rents have been published. So, the question is: why to do this work in the first place, if they already did it? By combining data from multiple sources you can get more meanings out of it. We wanted to take hands on approach, so we will probably not get THAT far, but the goal is to show people that it can be done.
We found data about average rents for each city districts and the interesting thing would be to compare the average prize of the district to actual data. It could be compared in district level, or street level. Now we cannot include the time dimension, but it would add value to examine the explosion of rent prices in some areas over time. It would be interesting to compare how prices correlate with other statistics such as crime, new cafes, amount of available flats, social welfare, unemployment etc.