Tallinn Ride Hailing App
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Table of Contents
Introduction #
I acquired a fun little data set for a well-known ride-hailing app recently. I performed a pretty detailed analysis at the request of my source, including some clustering of the ride start locations. The idea was to help drivers plan ahead to get into position before times of peak demand. There’s no NDA and this data is no longer very fresh, so I thought it would be nice to show the results in an interactive Tableau viz.
Mapping With Tableau #
Mapping with Tableau is an absolute breeze. Just set the Latitude and Longitude points as discrete or dimension and be sure that the geographical role is set. Tableau usually figures this out unless the column names are not obviously inferred as Lat and Long.
For this viz, because there are hundreds of thousands of data points, I had to set up the clustered and aggregated sheets first and add the filters. Only after that, was I able to add the full non-aggregated data for all the individual rides, making sure that filters were applied across all sheets. This was because Tableau Public was not very responsive when trying to render all the marks for the whole data set.
Result #
Here is a simple dashboard, showing maps of the cluster centres and the individual rides.
You can see which zones have the greatest number of rides during each time slot. Filters allow you to compare different time slots.
Normally, Tableau adjusts the bubbles sizes on a bubble map relative to the data returned after any filtering. In order to make the bubbles resize relative to the total number of rides in the data set, I had to add a couple of calculated fields:
- Total Rides (Fixed): A level of detail (LOD) field to return the fixed total ride count for the data set,
- Ride Count: A field for the default ride count aggregation per cluster that returns the number of rides after applying filters,
- Relative Rides = Ride Count / Total Rides (Fixed)
Final Thoughts #
The less dense clusters will be harder to service because they would attract fewer drivers while covering larger catchment areas. This could result in longer rider wait times. It would be advisable to perform a hierarchical cluster using just the subsets from the sparsest clusters (centred on Linnamäe tee, Vana-Mustamäe tn, and Vanasauna tee).