Created in 2014 by Josep Marc Mingot from Arcvi – Full Screen Map
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The data was extracted from the Barcelona City Council report about the 2012 elections. This data has already been integrated in the R package bcndataaccess.
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The visualization has been done using the CartoDB map visualization software that let you visualize maps and share the results as interactive plots. The administratives divisions of Barcelona (by districts) can be found at the GeoportalBCN as shape files . The shape files can then be esaliy updated to CartoDB.
Created in 2014 by Nikolay Nenov from Datalect – Original Post
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Did you know that Spain has the greatest number of bicycle sharing systems in the world? Having a high demand for these makes a lot a sense – the mild winter leaves one no bad-weather-excuses for not biking; and in the summer the bicycle is the most pleasant transport method for going to the beach. But if you have ever tried to go to the beach on the bici (that’s how the public bikes are called in Barcelona) on a lovely Sunday morning, you’ve probably stumbled on your closest station being empty. So you walk under the scorching Spanish sun to the next station, and quite possibly – to the one after that, until you can find a bicycle. Once you finally arrive to the beach, all the stations are now full, and there’s a line of people waiting to return their bicing.
Each time this happened to me, it got me thinking that there should be a way to forecast the demand in order to improve the supply. Fortunately, the guys at CityBikes have provided an API with the momentary availability of bikes. So, I scraped a several days worth of data, averaged the bikes per station and produced the following image.
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The white dots are the stations that are on average (almost) full and the reds are (again almost) always empty.
Great, but this doesn’t help at all with the forecast, because even though the station which is closest to my apartment is in yellow (meaning that on average there are bikes), it is always empty in the morning and always full at night. The obvious solution would be to average the bikes on each station by time of the day and then create an animation with that. And I did. Here’s the result, conclusions and R source code to generate it.http://www.nikefreerunshoesplus.com nike free shoes