How To Find Uber Hot Spots

Elegant main interface of the app
Privacy setting inquiry enabled
Detail about a upshot in New York
Map view of SF
Map view of Evanston
Help page of the app

Inspiration

The thought for Mapify began during a conversation with a driver during an Uber trip. Existence an Uber driver is difficult - earnings can be low and hours can be long. We found that drivers rely on experience and word-of-oral fissure when deciding where to station themselves for best earnings. Mapify, a new tool for drivers to predict pickup hotspots, would enhance the Uber commuter experience and reduce pickup times for passengers.

What it does

Mapify is a beautiful hotspot mapping app designed for Uber/Lyft drivers. It's the just tool available for Uber drivers to predict pickup hotspots beforehand. Using the schedule/itinerary of big well-attended events such as concerts and conventions as indicators, Mapify highlights locations on a map where passenger pickups are most likely at whatsoever given fourth dimension. For case, on a Saturday night, Mapify returned the top 47 agile events in Evanston alone, out of all 197 events found. In the Chicago Loop area, Mapify constitute thousands of events, and returned the acme 500. Mapify volition truly alter how Uber/Lyft drivers drive.

How we built it

Mapify is comprised of ii parts. We wrote a backend in Django using an Amazon Web Services EC2 instance and a front end for iOS in Swift. The iOS app was made completely in Swift in Xcode and uses Apple's built in MapKit API to display our information. Our iOS awarding makes calls to our server which then makes calls to the relevant APIs and returns the data back to our app which and so displays the information beautifully on the map.

Challenges we ran into

We ran into the challenge of gathering accurate and informative data from various upshot planning and ticketing website. Nosotros initially tried Facebook'southward Graph API which fails to deliver information well-nigh events effectually a certain locations and Lyft API which does not provide enough integration with Python script we run in the backend. Debugging the back-cease architecture is another challenge. Since we generate a JSON file in our python script running on AWS and parse information technology into swift dictionary in Xcode, nosotros want the script to run whenever an query is sent to the host so as to update the current locations in the script. Nosotros tried many method including Supervisor and successfully solved information technology by using Flask python bundle to run it smoothly in the back-end.

Accomplishments that nosotros're proud of

Maypify aims to increment the information matching betwixt Uber or Lyft commuter and where potential passengers are on a dynamic map that constantly updates hotspot pins. We are proud of the idea that the app tin be very effective to better direct drivers and reduce waiting time for passengers. Also, we are proud of our front-end interface and functionality every bit well equally back-end back up and pattern to make the projection possible.

What's side by side for Mapify - Your Hotspots Mapified

We want to improve the accuracy of Mapify by calculation additional API calls in our backend. Nosotros'd love to be able to query the Uber, Lyft, or Facebook APIs to gather more data. With more data we can improve accurateness and usefulness. The immediate next footstep will be to release it to the App Store to start to go feedback straight from drivers.

Nosotros built this project to compete for JP Morgan Hunt & Co - Best Social Good Hack, Watch Dogs® 2 Best Device Privacy Hack and Amazon Web Services - Best Use of AWS