## Inspiration

During extreme events such as natural disasters or virus outbreaks, crisis managers are the decision makers. Their job is difficult since the right decision can save lives while the wrong decision can lead to their loss. Making such decisions in real-time can be daunting when there is insufficient information, which is often the case.

Recently, big data has gained a lot of traction in crisis management by addressing this issue; however it creates a new challenge. How can you act on data when there's just too much of it to keep up with? One example of this is the use of social media during crises. In theory, social media posts can give crisis managers an unprecedented level of real-time situational awareness. In practice, the noise-to-signal ratio and volume of social media is too large to be useful.

I built CrisisTweetMap to address this issue by creating a dynamic dashboard for visualizing crisis-related tweets in real-time. The focus of this project was to make it easier for crisis managers to extract useful and actionable information. To showcase the prototype, I used tweets about the current coronavirus outbreak.

## What it does

* Scrape live crisis-related tweets from Twitter;
* Classify tweets in relevant categories with deep learning NLP model;
* Extract geolocation from tweets with different methods;
* Push classified and geolocated tweets to database in real-time;
* Pull tweets from database in real-time to visualize on dashboard;
* Allows dynamic user interaction with dashboard

## How I built it

* Tweepy + custom wrapper for scraping and cleaning tweets;
* AllenNLP + torch + BERT + CrisisNLP dataset for model training/deployment;
* Spacy NER + geotext for extracting location names from text
* geopy + gazetteer elasticsearch docker container for extracting geolocation from locations;
* shapely for sampling geolocation from bounding boxes;
* SQLite3 + pandas for database push/pull;
* Dash + plotly + mapbox for live visualizations;

## Challenges I ran into

* Geolocation is hard;
* Stream stalling due to large/slow neural network;
* Responsive visualization of large amounts of data interactively;

## Accomplishments that I'm proud of

* A working prototype

## What I learned

* Different methods for fuzzy geolocation from text;
* Live map visualizations with Dash;

## What's next for CrisisTweetMap

* Other crises like extreme weather events;
## Inspiration

Currently the insurance claims process is quite labour intensive. A person has to investigate the car to approve or deny a claim, and so we aim to make the alleviate this cumbersome process smooth and easy for the policy holders.

## What it does

Quick Quote is a proof-of-concept tool for visually evaluating images of auto accidents and classifying the level of damage and estimated insurance payout.

## How we built it

The frontend is built with just static HTML, CSS and Javascript. We used Materialize css to achieve some of our UI mocks created in Figma. Conveniently we have also created our own "state machine" to make our web-app more responsive.

## Challenges we ran into

>
> I've never done any machine learning before, let alone trying to create a model for a hackthon project. I definitely took a quite a bit of time to understand some of the concepts in this field. *-Jerry*
>
>
>

## Accomplishments that we're proud of

>
> This is my 9th hackathon and I'm honestly quite proud that I'm still learning something new at every hackathon that I've attended thus far. *-Jerry*
>
>
>

## What we learned

>
> Attempting to do a challenge with very little description of what the challenge actually is asking for is like a toddler a man stranded on an island. *-Jerry*
>
>
>

## What's next for Quick Quote

Things that are on our roadmap to improve Quick Quote:

* Apply google analytics to track user's movement and collect feedbacks to enhance our UI.
* Enhance our neural network model to enrich our knowledge base.
* Train our data with more evalution to give more depth
* Includes ads (mostly auto companies ads).
*"This is the time for facts, not fear.
This is the time for science, not rumors.
This is the time for solidarity, not stigma.
We are all in this together."*

-- The tweet from UNGeneva after a public health emergency of international concern over coronavirus.

## Inspiration

Due to the outbreak of Novel coronavirus 2019, many people and organizations face the lack of resources. With social media being an important channel for these people to find help, we find many help requests on Weibo (like Chinese Twitter) but they only get attention from the public if they're lucky enough to be reposted by KOLs. We want to find a more efficient way to solve this problem and enable these requests more easily to be seen.

## What it does

An online platform with map visualization that can

1. gather help requests from social media (such as Weibo, Twitter) automatically
2. People that need help can also register and submit their help request to the platform directly

In the McHacks, we primarily focus on the help requests from hospitals in China because they face the most severe lack of resources now.

## How we built it

Data Science:

1. Scraping on Weibo with selected keywords and hashtags
2. Transform semi-structured texts to structured data using keywords detection (and entity analysis using IBM and Google API for those with little structure)

Backend:

1. Java Spring Boot framework using Java 8
2. Create RESTful APIs
3. Using Hibernate and JPA to generate PosgreSQL
4. Attempted deploying using Heroku

Frontend:

1. The modern front-end framework React along with ES6 Javascript.
2. Google-Map-React to generate the map with hospitals
3. Bought the domain coronatlas.space from domain.com

## Challenges we ran into
4. Many NLP APIs don't support Chineses or have worse performance in Chinese
5. Unfamiliar with web app development (but learned a lot!)

## Accomplishments that we're proud of

We are glad we can use what we learn to help fighting against the plague (Two of us are from Hubei).

## What we learned

How to build a map app with a team!

## What's next for Coronatlas

Not only for Novel coronavirus! Not only for Chinese!

1. Add functionality for volunteers
2. Connect with Twitter