**In times of disaster, there is an outpouring of desire to help from the public. We built a platform which connects people who want to help with people in need.**

## Inspiration

Natural disasters are an increasingly pertinent global issue which our team is quite concerned with. So when we encountered the IBM challenge relating to this topic, we took interest and further contemplated how we could create a phone application that would directly help with disaster relief.

## What it does

**Stronger Together** connects people in need of disaster relief with local community members willing to volunteer their time and/or resources. Such resources include but are not limited to shelter, water, medicine, clothing, and hygiene products. People in need may input their information and what they need, and volunteers may then use the app to find people in need of what they can provide. For example, someone whose home is affected by flooding due to Hurricane Florence in North Carolina can input their name, email, and phone number in a request to find shelter so that this need is discoverable by any volunteers able to offer shelter. Such a volunteer may then contact the person in need through call, text, or email to work out the logistics of getting to the volunteer’s home to receive shelter.

## How we built it

We used Android Studio to build the Android app. We deployed an Azure server to handle our backend(Python). We used Google Maps API on our app. We are currently working on using Twilio for communication and IBM watson API to prioritize help requests in a community.

## Challenges we ran into

Integrating the Google Maps API into our app proved to be a great challenge for us. We also realized that our original idea of including a blood donation as one of the resources would require some correspondence with an organization such as the Red Cross in order to ensure the donation would be legal. Thus, we decided to add a blood donation to our future aspirations for this project due to the time constraint of the hackathon.

## Accomplishments that we're proud of

We are happy with our design and with the simplicity of our app. We learned a great deal about writing the server side of an app and designing an Android app using Java (and Google Map’s API” during the past 24 hours. We had huge aspirations and eventually we created an app that can potentially save people’s lives.

## What we learned

We learned how to integrate Google Maps API into our app. We learn how to deploy a server with Microsoft Azure. We also learned how to use Figma to prototype designs.

## What's next for Stronger Together

We have high hopes for the future of this app. The goal is to add an AI based notification system which alerts people who live in a predicted disaster area. We aim to decrease the impact of the disaster by alerting volunteers and locals in advance. We also may include some more resources such as blood donations.
## Inspiration

Disasters can strike quickly and without notice. Most people are unprepared for situations such as earthquakes which occur with alarming frequency along the Pacific Rim. When wifi and cell service are unavailable, medical aid, food, water, and shelter struggle to be shared as the community can only communicate and connect in person.

## What it does

In disaster situations, Rebuild allows users to share and receive information about nearby resources and dangers by placing icons on a map. Rebuild uses a mesh network to automatically transfer data between nearby devices, ensuring that users have the most recent information in their area. What makes Rebuild a unique and effective app is that it does not require WIFI to share and receive data.

## How we built it

We built it with Android and the Nearby Connections API, a built-in Android library which manages the

## Challenges we ran into

The main challenges we faced while making this project were updating the device location so that the markers are placed accurately, and establishing a reliable mesh-network connection between the app users. While these features still aren't perfect, after a long night we managed to reach something we are satisfied with.

## Accomplishments that we're proud of

WORKING MESH NETWORK! (If you heard the scream of joy last night I apologize.)

## What we learned

## What's next for Rebuild
Inspiration:
As native Houstonians, part of our team witnessed and experienced the damage wreaked by Hurricane Harvey firsthand. Throughout the fear, uncertainty, and chaos associated with that month of our lives, we developed a feeling that more could have been done to prepare for such disasters. We could have had better tools, better resource flow, and better communication platforms specifically designed for such times of crisis.

What it does:
During Harvey, social media was not just a daily time sink that we took with our morning coffee. It became the single most useful way for us to keep track of friends and loved ones. Twitter was flooded with tweets sending out prayers and offering resources as well as desperate cries for help. Disasterrelief takes filtered tweets from a natural disaster. It categorizes them into tweets that are asking about food/water, being stranded, donations, power outages, clothing. It sorts out these tweets into tweets for help and tweets of support. Disasterrelief allows people to visualize the hotspots where help is needed after the impacts have occurred and it allows people to figure out how best to help their neighbors in an organized way.

How we built it:

* Split a dataset of Hurricane Sandy tweets into those from people who needed help and those who didn't need help but were simply offering thoughts, prayers, and commentary about the situation.
* Used Naive Bayes, KNN, Decision Trees, and Support Vector machines to figure out the model that gave the highest accuracy most efficiently in categorizing the data into help and non-hep Tweets
* Used Google Maps API to display Tweets by location. Displayed a separate marker by Tweet
* Further filtered tweets using decision trees into categories such as food/water, shelter, people without power, and donations
* Also filtered Tweets by state in order to show people what was needed closest to them

Challenges we ran into:

* Integrating google apps api with current framework and updating it with tweets
* Rerouting DNS through Google Cloud so as to work with IPV6 records
* Filtering through the tweets accurately
* We trained our dataset with roughly 3% of the size of the actual dataset, due to time and computer memory constraints. This gives somewhat artificially inflated values, due to the test sets being much smaller. (for reference, when we tried training the GNB classifier with the full dataset, the acccuracies were ~5% lower.)

Accomplishments that we're proud of:

* Able to successfully tag tweets with 80% accuracy using decision trees
* Able to use google maps api to integrate a visualization of hotspots

What we learned:

* Getting data is hard.
* There are a lot of react resources available that can be used to develop highly interactive websites with google maps.

What's next for Disasterrelief:

* Setting up help centers and resource flow paths based on Tweet updates in real time
* Better way to figure out who has been helped and who hasn't
* Include other forms of social media like Facebook