Now that we've got the lay of the land a bit, let's take a look at a tantalizing bit of information in this data set, which is...

Gratuity

We all sort of wonder what others tip. 15%? If the service was poor, or you are light on funds, maybe less? Much less? Let's put on our sneaky social x-ray goggles and take a look at the terrain of generosity...

So those were the tipping rates based on where riders were picked up. But how might this terrain of tipping differ, when we focus rather on the destinations of these riders?

The landscape of tipping resulted in some distinct boundaries of generosity. And the disparity of pickups to drop-off locations illustrated some interesting regionality as well. But what about a look at what means riders used to pay their fare? Did they use...

Cash or Credit

How might New York look if we painted it according to the rate of riders that paid with cold hard cash or if they swiped a card? Are there implicit boundaries that arise based on access to credit?

So far, the tip amount, and method of payment, have pragmatically revealed part of the underlying economic tapestry of a city. What might we learn from the likelihood of splitting a cab vs going it alone? Let's take a geographic look at...

Passenger Count

Personal transportation. We've looked at the overall pattern of taxicab usage in the city. We've looked at the surprisingly tender aspects of tipping and payment method. We even found a distinct trend for cab-sharing to happen more frequently towards the city center. But what nearly every rider considers when stepping into a cab is, how long will this take me? Likewise, many riders stepping out of a cab at their destination will check their watch. So, armed with these millions and millions of data points, let's take one last look at the nature of a city; let's look at...

Ride Duration

Like most any social data, when seen in enough volume a sort of portrait emerges from the ensuing visualizations. This Yellow Cab data, spanning one year, is more than a massive array of trips and attributes. It is the raw material for the inspection of the pulse and tone of a place. The circuitous nature of humans needing to go somewhere, and then return, carries with it so much meta data about the underlying nature of those places and the people who live there.

Data is just data, a very thin abstraction of something real. And in a tabular form it is generally useful only as a compact means of sharing. But the quicker tabular data can be re-hydrated into a truer picture of the phenomenon it represents, the quicker it can begin to tell its story.  All maps are are stories, stories that happen to use the most intrinsic dimension that we can comprehend –where.

Thanks for reading! Happy mapping, John