Social good and economic impact! We were inspired by the rampant amount of homeless people who struggle to find a meal everyday and low-income families struggling financially, which is also exacerbated by the Covid-19 pandemic. Due to high unemployment rates, many people are now struggling to put food on the plate and restaurants are going out of business. Everyday, due to underwhelming demand, we realized that many restaurants are forced to discard excess food at the end of the day. We thought that the same food that is thrown out would happily be accepted by those who don’t know where their next meal is coming from. We put two and two together and decided to create this app in order to not waste that excess food and put it to good use by having it donated to homeless shelters to help those that have become impoverished because of the pandemic as well as those that were already low-income. This app is especially useful as long as we’re in a recession and in the pandemic, but is also useful even after because of how there are always low-income families that are in search of food.

What it does

Dish Drop is a food delivery app that supports the homeless and low-income families as well as restaurants by connecting the two, allowing for restaurants to donate their excess food to homeless shelters instead of throwing it and also giving participating restaurants tax credits for their donations. Restaurants can send out a notification stating that they have food they have ready to donate. Homeless shelters can then send a request for the food, triggering the transaction. A Dish Drop driver is located, picks up the restaurant’s excess food, and delivers it to the homeless shelter or meal program (soup kitchen, food bank).

How we built it

We split up the work between the four of us by front-end and back-end work. Two of us worked on designing the User Interface and User Experience, which was created using Storyboard/UIKit in Xcode. The other two of us used Swift in order to code the back-end portion and utilized both Google Cloud’s Firebase Firestore and Real Time Database. We used Firestore in order to store information about the different restaurants and meal programs, while using Real Time Database in order to keep track of orders, order status, driver information, and driver location. This helped us provide this data in real-time to all parties involved regarding where the food is.

Challenges we ran into

We ran into challenges when trying to figure out the whole back-end flow of how the information should be passed. We originally thought that we would utilize a system where homeless shelters and other meal programs can request food and restaurants would fulfill them, but we decided to make the design change to make it so that restaurants would indicate food they have to donate at a certain time, and then notify homeless shelters about the available food, which they could claim. This information would now be passed onto a driver, who would be able to reserve a delivery and then go and carry it out.

Accomplishments that we're proud of

We were able to utilize tools that we knew about in order to create a socially applicable project that has wide implications. We are proud of creating a food delivery application that can support the homeless and help restaurants during the pandemic and also after.

What we learned

We learned a lot about how to utilize location data. We also learned about how to use Swift and UIKit and MapKit in order to utilize Apple Maps to display the data. We also learnt how to use Google Cloud’s Realtime Database in order to see the orders coming and being accepted in real-time as well as the driver’s location.

What's next for Dish Drop

As of right now, Dish Drop provides a layout for how restaurants will be able to donate food to other homeless shelters. We’d work on also contacting more restaurants and homeless shelters to use the app in order to be able to feed more people as well as save more food wastage. We’d also largely target incentivizing drivers to join the program, either through a payment service which restaurants can use to pay drivers to deliver the food. We will also work on creating a rating system for drivers in order to give more information to restaurants and meal programs about who the driver is and more information about them. We would work on also creating profiles for restaurants and homeless shelters to give more statistics on the amount of people that live there, where they usually order from, etc. ( for homeless shelters) and give statistics on how much food they usually give and average ratings that they have gotten from meal programs about the quality of food. We’d also want to potentially implement machine learning to be able to predict meal ordering patterns for meal programs as well as placing meal information for restaurants and add scheduling features.

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