## Inspiration

Witnessing the atrocities(protests, vandalism, etc.) caused by the recent presidential election, we want to make the general public (especially for the minorities and the oppressed) be more safe.

## What it does

It provides the users with live news update happening near them, alerts them if they travel near vicinity of danger, and provide them an emergency tool to contact their loved ones if they get into a dangerous situation.

## How we built it

* We crawl the latest happenings/events using Bing News API and summarize them using Smmry API.
* Thanks to Alteryx's API, we also managed to crawl tweets which will inform the users regarding the latest news surrounding them with good accuracy.
* All of these data are then projected to Google Map which will inform user about any happening near them in easy-to-understand summarized format.
* Using Pittney Bowes' API (GeoCode function), we alert the closest contacts of the user with the address name where the user is located.

## Challenges we ran into

Determining the credibility of tweets is incredibly hard

## Accomplishments that we're proud of

Actually to get this thing to work.

## What's next for BeSafe

Better UI/UX and maybe a predictive capability.
## Inspiration

We realized that while there are many news sources out there, with this, we can see what countries in the world are news hotspots - the ones that have the most things going on. You can view the world from a new perspective with our World New Map, seeing which countries are the centers of action and events.

## What it does

It uses UiPath for web scraping to gather data from news sites. The program will then create a heat map based on the number of news reports sorted by category and the severity. The mobile app will then notify it's users when there is a problem with an area that they are in or is near them.

## How we built it:

#### WebScraping(UI Path)

We used UiPath to gather data from news sites via web scraping. We accumulated around 5000 different data entries and exported them into CSVs, which are then put in a MongoDB Atlas database.

#### Cloud Storage(MongoDB Atlas)

We created a main database and sorted our data into about 100 different subfolders for different countries around the world.

#### Web Application (NodeJS)

Using Node.js and Google Charts we created a heat map based on the media coverage of the areas. We exported the mongoDB as a json and created it into a graph. We had a seperate file for each country to show the news. Clicking on Canada would show each province and show many articles each province has.

#### Android App(Radar.io)

We used Kotlin for the moblie app and used Radar.io to get the location of the user and notify them if they are in an area with a safety concern.

## Deploy with Heroku

We deployed this software to the web with Heroku. Originally we had it running locally for testing, afterwards, we converted to a version that we could deploy with Heroku.

## Assign a domain name

We used the free domain code to assign it to a .online domain.

## Challenges we ran into

Radar.io software was complicated, there was very little documentation. Radar.io also had a bug on their end that did not allow the location tracking to work properly which forced us to hard code the app. There was also numerous issues with Heroku as we struggled to convert it from the local to a version we could deploy.

MongoDB's official documentation was vague and confusing, we had to resort to third-party documentation to use it.

We also had to create a heat map, which we were originally going to make in plot.ly.

## Accomplishments that we're proud of

Learning how to use Radar.io, UiPath, and MongoDB Atlas.

## What we learned

How to use Radar.io, UiPath, MongoDB Atlas, Heroku, Custom Domains, Static web-hosting.
We also learned fast json manipulation to create graphs and output files.

## What's next for WorldNewsMap

We could use machine learning to allow the application to predict future media coverage. We could then alert users that there could be potential danger in the area in the future. We also need more data from UIPath, the data should be proportional.
## Inspiration

After learning about the current shortcomings of disaster response platforms, we wanted to build a modernized emergency services system to assist relief organizations and local governments in responding faster and appropriately.

## What it does

safeFront is a cross between next-generation 911 and disaster response management. Our primary users are local governments and relief organizations. The safeFront platform provides organizations and governments with the crucial information that is required for response, relief, and recovery by organizing and leveraging incoming disaster related data.

## How we built it

safeFront was built using React for the web dashboard and a Flask service housing the image classification and natural language processing models to process the incoming mobile data.

## Challenges we ran into

Ranking urgency of natural disasters and their severity by reconciling image recognition, language processing, and sentiment analysis on mobile data and reported it through a web dashboard. Most of the team didn't have a firm grasp on React components, so building the site was how we learned React.

## Accomplishments that we're proud of

Built a full stack web application and a functioning prototype from scratch.

## What we learned

Stepping outside of our comfort zone is, by nature, uncomfortable. However, we learned that we grow the most when we cross that line.

## What's next for SafeFront

We'd like to expand our platform for medical data, local transportation delays, local river level changes, and many more ideas. We were able to build a fraction of our ideas this weekend, but we hope to build additional features in the future.