Interactive Dashboard – Los Angeles Bike Share 4th Quarter 2018
Hello everyone. David from Data Oracle here.
Bike share facilities are popping up everywhere. Considering the global stage, bike share in China has been considered a public menace due to oversupply. However, in many other countries, like some parts of the USA, they have been mostly welcomed.
For the purpose of this data visualisation, I thought I would look at the bike usage in Los Angeles for the 4th quarter of 2018. There are several bike share dispensers in various locations in LA, so the data-set is well populated with a diverse range of bike movements per day.
The automated visualisation is located on the Data Oracle Website – click-here and the dashboard version is located on Tableau Public – click-here. Feel free to view the automated visualisation or interact with the dashboard live on Tableau Public.
The data set used is publicly available at the following website: Metro_Bike_Share. The data set consists of the following parameters:
trip_id – Trip Identification Number
duration – Duration of the trip in minutes
start_time – Start date and time
end_time – End date and time
start_station – Station ID number at the dispenser of origin
start_lat – Origin latitude
start_lon – Origin longitude
end_station – Station ID number at trip termination
end_lat – Termination point latitude
end_lon – Termination point longitude
bike_id – ID representing the bike used
plan_duration – Number of days of planned usage, zero representing a single day
trip_route_category – Round or one way trips
passholder_type – Name of pass holders plan
bike_type – Bike type used
As noted above, there are a number of parameters we could choose to visualise. However, I was particularly keen to obverse the bike movements between all of the dispensers in all their respective locations. To fulfil this visualisation desire, a map with lines indicating the path of bike travel between the dispensers was deemed suitable – see the top left of the dashboard in figure 2.
Secondly, I wanted to visualise the duration of each trip. Were people using the bikes for short or long periods of time? A good visualisation technique to answer this question is to depict bike usage duration on a radial time chart – see the bottom of the dashboard in figure 2.
Lastly, I wanted to run a bike theme throughout the visualisation. Hence the usage of bike graphics and wheels as displayed in the general dashboard.
What can we determine from the visualisation?
We can determine the following trends:
Most popular disperser locations
Basic usage and general duration at the 3 broad locations
Which days produce the highest general usage
Which days produce the lowest general usage
Which events, public holidays, etc. draw the highest/lowest usage and at which locations
Answer the question: do people generally venture between the 3 broad locations?
Was the assumption made that the coastal locations would produce the best usage? If so, does the data confirm or otherwise whether that assumption was correct?
We could also look into revenue, demographics and many other aspects. So obviously, we could greatly expand this list, but for now, this is sufficient.
How does the visualisation function?
Dashboard on the Tableau Public Website Click-Here.
On the map, you can select a trip path (a line joining the dispenser of origin and dispenser at completion) or a trip dot (where the dispenser at origin and completion are the same). The selection shows the tool tip (data of the trip) at the top right of figure 3 and the duration of that particular trip at the bottom of figure 3.
To see the duration data, just hover your mouse over the duration point, as shown in figure 4 below.
Dashboard on the Data Oracle Website
There are two methods to access the automation. Click ‘Press Play’ of the dashboard located on the Tableau public website. This will redirect you to the Data Oracle website where you can observe the video version of the dashboard streaming through the dates. Or, click-here.
When the page loads, the visualisation will automatically play the video of the dashboard streaming through the dates. When finished, just hit play to watch it again or close the screen to finish.
I hope you enjoyed this brief article and the visualisation.