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City DataParty

February 3, 2012 in Cities, Data Party, Events

If you have fun working with data or would like to learn how to do some data-crunching, please come to our virtual DataParty on City Data on Wednesday, February 8 @ 5pm GMT / 6pm CET / 12pm EST. To join the DataParty, please enter your skype ID in the DataParty Etherpad. If you are in London, you can also come to the #C4CC at 16 Acton Street, WC1X 9NG. We will gather disaggregated data on city and regional level for cities around the world and add them to the Datahub.

Are you interested in what drives cities? Regional and city data can much more interesting than national averages, as it reflects the spatial agglomerations of economic and social activities. Analysing regional level data could deliver insights about the unequal economic development – whether patterns of development are due to geographical devisions or institutional factors.

What do you value personally in a city? Maybe the employment opportunities, the low crime rates, the environmental quality and good weather or the concentration of cultural and academic activities… Do you want to live in a densely- or sparsely-populated city, one with many schools and few car accidents? Probably you consider some of those factors really important and others not decisive at all. And you would be right to put a different weight on the various factors which constitute a city. Probably you would also like to know what your perfect city would be like. The next Open Economics project will build an application to determine the Best City in the World to submit to the BuzzData & EIU – Data Mash-Up & Visualization Contest: “Where is the best city in the world to live?”.

Spatial economists and econometricians, as well as interested data journalists and citizens are also welcome to join – building a dataset, based on comparable NUTS3 statistics of Eurostat for European countries, we can analyse the relationships between the labour market, education, health and spending. You are welcome to share and practice data analysis techniques and initiate follow-up activities.

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