Visualisation allows us to process a lot of information by quickly ascertaining trends that are visually apparent in charts and graphs; that would otherwise escape notice in a sea of similar looking numbers.

In the course, the visualisation tool we learnt to use is called Tableau, a wonderful tool that combines the vast query structure of SQL with visualisation prowess surpassing that of any powerpoint software.

I have createda visualisation in  Tableau, that can be viewed publically by following the following link:

https://public.tableau.com/views/CourseraTableau_16895261835420/Cyclistic

It is also embedded below for immediate visual presence. 

The visualisation is split into the following six Dashboards:

1.  Ride_Count

2. Ride_Ratio

3. Ride_Distribution

4. Ride_Duration

5. Preferences

6. Popular_Stations
   
   and we will be discussing them accordingly.

Ride_Count

This is just the basic raw ride count by membership status over time, and we can see some obvious trends over time.

We can see the seasonal variations affect ridership. The service is based in Chicago, and we can see how ridership rises in the nice warmer summer months and then plummets as the weather gets chilly, and rises back up again as spring comes.

This cyclic variation is normal, and we can see that over the year, member ridership numbers trump casual reide numbers by a healthy margin, so there is no particular month we can focus on for our marketing campaign.

Similarly on the hourly side,  we wee two peaks for membership riders at common  office opening and closing times, while only one peak for casual riders at the closing time. We will discuss this further  in the ride_distribution  dashboard discussion.

On the daily side is where we see some noticeable flip; while members still supercede casual numbers, on the weekends we can see casual ride stats to  match and perhaps slightly surpass membership ride numbers.

This indicates that casual riders mostly prefer to use the bikes for weekends when they are free, while members are likley to use their bike all week, combining it as part of their daily routines.

Ride_Ratio

This dashboard tracks the ratio of casual to members at any given time period.

We can see that for the most part that ratio is mostly in the favour member riders; we can see some spike in favour of casual riders very late at night, but from the previous chart we can see that overall numbers are very low, so that is not a significant result.

We can see the weekend flip repeated here, which indicated different preferences for each rider group..

While on a monthly basis we don't see any flip, around July we did see the number coming very close to even, so that suggests that that warmer weather plays a significant role in determining casual ridership numbers.

Ride_Distribution

This dashboards focuses on which time slots are focused on by each category of riders, by taking all the rides take over the timeperios and spreading out it's distribution.

While there are obviously seasonal variations, we can see evidently that causla riders have a far higher fluctuations. Most of the rides they took were in the warmer months, and very rarely did they venture out to ride in the colder months; while members would still be riding, though albeit at lower numbers than summer, but the ridership didn't drop that significantly.

We can see the weekend effect more clearly on the daily graph; casual riders overwhelming prefer the weekends while member riders spread their ridership out mostly evenly over the week with a slight dip on the weekends but not that  remarkable as the shift in casual ridership numbers.

On the hourly side is where we see an interesting trend. Membership riders have two significant peaks at around 8 AM and 5PM; these correspond to the most common working hours. Casual riders also have increased activity around these time slots, but the evening slot is more significant.

This implies that while members ride their bike both to and from work, casual riders mostly prefer to use it for the ride back when they are less worried about being sweaty, or reaching on time, and would rather take alternative transport in the morning

Ride_Duration

Ride duration monitors how long the median ride lasts for, at any given time period.

Here we see the charts flips with casual riders dominating and membership riders taking shorter trips on average. We can see that no matter the time period, the rides mostly fall in the 7-16 minute time band, regardless of membership.

When we take membership in account, we see that casual riders are overwhelming at the upper end of the band,  taking far longer trips than members.

We also note that membership ride durations rarely fluctuate and remain mostly even horizontally; meanwhile, the casual rider durations display all the usual fluctuations that match seasons, weekends or office timings.

This implies that riders who select membership have a narrow ride window (of around ~8 minutes) in mind. Since riders are charged by the minute, the more regular users have budget limit beyond which they do not use the bikes, while casual riders are more willing to splurge on the occasional long (and expensive) trips.

Preferences

This dashboard focuses on two preferences that riders can have; what sort of bike to use, and whether to take one way or round-trip (i.e. end at the same station they started at)

For the bikes, there exists a category called "docked bike" that is not used by members; this implies that there is perhaps a price or usability distinction made for docked vs undocked bikes for casual riders, that is not made for members, and hence rides that would be tagged as docked rides for casual riders are not tagged as such for members.

As far as uptake for classic (i.e. manually pedalled) vs electrical (bikes with electronic pedal assist or throttle functions), casual riders seem to be evenly split among both, while members do take more rides on classic.

This doesn't necessarily imply preference for manual bikes, but that they form much higher ridership numbers and probably there aren't enough electric bikes available at that very moment to satisfy demand.

As for one-way vs round trips, we can see that one-way trips form the overwhelming number of rides for either category, but we do slight but significant uptake among casual riders for round trips.

Docked trips have a significant proportion of round trips, this implies that something about this category is targeted towards casual riders who are more likely to return to the place they started from, and thus implies locations which suit this type of travel.

Popular_Stations

This dashboards highlights the most popular stations and trips.

The most popular starting and ending stations are mostly clustered together in one area, with one significant outlier being the station at "University Ave & 57th St" which we can check on an online map to confirm that it is at University of Chicago, and thus an important hub for bike riders.

We can analyse all such import stations that form the overwhelming majority of all rides taken on the network to see what patterns we can form, and if we can see which rides are worthy of focus.

The Treemap shows that for example, the most common trip of all is a round-trip from (and to) the "Streeter Dr & Grand Ave" station, while the most common one-way trip is from "DuSable Lake Shore Dr & Monroe St" to the same station mentioned previously. 

From the map we can see that both locations are on popular places on the Chicago coastline and thus indicate a trend for the kind of trips takes. In fact if we filter for casual riders, this exact trip forms the most common for them too,  indicating a trend for casual riders.

If we filter for members however, the trip between "Wells St & Huron St" and "Wells St & Elm St" forms the most common trip. Neither are these are near the shoreline, and a check on online maps suggests these are  from a station near Chicago's public transit to a location in downtown Chicago, indicating a different sort of rider.

There are other filters such as time and day available, if we wish to further drill down the sort of trips we wish to scrutinise.

This visualisation is meant to be shared not only with my colleagues in the marketing department, but also to my boss and the director of the Marketing department.

Together this will help us  answer the three questions that the Director of Marketing set us, and help guide our marketing strategy for the future.