View map. Just Eat dublinbikes stations are distributed in close proximity to each other in Dublin city centre, so you are never far away from renting or returning a. Station map. (02/08/). View the Just Eat dublinbikes station map to discover the network locations. Station map · Just_Eat_Food_Envy_dublinbikes_home. Here’s our map showing the planned DublinBikes stations (red pins) along with view the map released by Dublin City Council: DublinBikes extension map.
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I wanted to understand this in more detail and find out if there are a handful of different behavioural types that all stations duublinbikes be categorised into, and how this might vary around the city. The bikes are used mainly for commuting to and from work so it seems natural that some sort of spatial pattern would occur.
Dublin Bikes Map Of New Stations –
As you can see from the map above it turns out that we can classify the stations into three different behavioural types and we get some pretty interesting results! I collected data every 2 minutes from a public API from January to August to build up a decently sized historical data set from which we can work out typical weekday usage profiles for each of the stations.
This will average out the effects of rainy days, cold days, special events etc. From this we can then find distinct behavioural patterns into which we can classify each station profile.
Ddublinbikes hope to put online a talk I gave at PyData Dublin in October which explains this in more technical detail. Each station is then categorised according to its closest match to the three profiles.
We are free to choose a higher value but the results become more complicated to interpret due to the increasing number of possible categories, however we would dubinbikes the behavioural profiles to be a better fit to the actual data. Recall that these are found using the average weekday usage for each station and so time of day is along the x-axis.
Dublin Bikes Map Of New Stations 2013
In the evening the opposite occurs as people head back home from work. When we plot the stations on a map and colour code them by category a really interesting picture emerges, as shown in the top image or click here to open a tab for the interactive map.
By characterising the stations mqp only their usage patterns we can see this also results in a high degree of spatial clustering. I ran this from January until late August when I finally remembered about the project idea again. As mentioned previously I then calculated average weekday profiles for each of the stations, then used k-means clustering to determine the three archetypal behavioural categories with which I would categorise each station.
Getting Around on Dublin Bikes
I used Euclidean distance for determining similarity of the different time dublibnikes, which is a bit simplistic and not generally recommended for time series data. More advanced methods like dynamic time warping could be used if a more robust approach is needed.
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