|New York's cab data visualization from Uber's Engineering blog|
- Uber's growth through time (and specific activity growth in different wards)
- Figuring from historical time series which wards and routes have most traffic in which hour (this also should let us predict which areas may face surge pricing)
- See if the growth has saturated in any specific place (should give us upper threshold for that area)
- If an increase in Uber Demand directly co-relate to travel time (maybe the increased demand is causing traffic?)
- Can we load it up in kepler.gl (more specifically using this demo as a template) and have a nice timeseries visualization?