Hello Zindians, please can anyone share an idea on how I could extract additional information from the Uber dataset. I have a json data and a sublocation data for the Time travels.
I made an api for that purpose. Go to that readme and see how to use it. It uses the uber hexagons and gets the travel time for that. It returns a map/dictionary for hourl times and a list of maps for dailytravel time request. It also returns a null when the travel time is missing from the uber dataset.
It supports Uber travel times from 2017 first -2019 3rd quarter.
The json data basically contains the sublocation polygons. You can extract the polygons from the json data and use Shapely to determine which of the sublocations the individual pickup points and destination points fall into. With that, you can fit all of your pickup and destination points into distinct clusters and find the mean (or median) of travel times in those clusters.
'/%3e%3c/defs%3e%3cpath fill='%23fff' d='M-120-80h240V80h-240z'/%3e%3cpath d='M-120-80h240v48h-240z'/%3e%3cpath fill='%23060' d='M-120 32h240v48h-240z'/%3e%3cg id='b'%3e%3cuse xlink:href='%23a' stroke='%23000'/%3e%3cuse xlink:href='%23a' fill='%23fff'/%3e%3c/g%3e%3cuse xlink:href='%23b' transform='scale(-1 1)'/%3e%3cpath fill='%23b00' d='M-120-24v48h101c3 8 13 24 19 24s16-16 19-24h101v-48H19C16-32 6-48 0-48s-16 16-19 24z'/%3e%3cpath id='c' d='M19 24c3-8 5-16 5-24s-2-16-5-24c-3 8-5 16-5 24s2 16 5 24'/%3e%3cuse xlink:href='%23c' transform='scale(-1 1)'/%3e%3cg fill='%23fff'%3e%3cellipse rx='4' ry='6'/%3e%3cpath id='d' d='M1 5.85s4 8 4 21-4 21-4 21z'/%3e%3cuse xlink:href='%23d' transform='scale(-1)'/%3e%3cuse xlink:href='%23d' transform='scale(-1 1)'/%3e%3cuse xlink:href='%23d' transform='scale(1 -1)'/%3e%3c/g%3e%3c/svg%3e)
https://github.com/bettdouglas/uber-api/blob/docker-setup/README.md
I made an api for that purpose. Go to that readme and see how to use it. It uses the uber hexagons and gets the travel time for that. It returns a map/dictionary for hourl times and a list of maps for dailytravel time request. It also returns a null when the travel time is missing from the uber dataset.
It supports Uber travel times from 2017 first -2019 3rd quarter.
Uses PostGIS under the hood for processing.
Hi Gozie
The json data basically contains the sublocation polygons. You can extract the polygons from the json data and use Shapely to determine which of the sublocations the individual pickup points and destination points fall into. With that, you can fit all of your pickup and destination points into distinct clusters and find the mean (or median) of travel times in those clusters.
Thanks Bettdoug and Alchemist for your replies.
I'll try themthe
Hi! So, is this data helped?