## Inspiration 💡

Our project, **Health Equity Compass** 🧭, is driven by the need to address healthcare disparities that disproportionately affect marginalized communities. Access to healthcare 🏥 is unequal across different demographics and regions, leading to disparities in health outcomes. We wanted to create a tool that helps visualize these inequities, offering public health organizations and policymakers actionable insights to improve healthcare access for all. This software is mainly targeted towards local government and public health organizations to educate and bring to light the disparities in access and quality of healthcare in local areas. The goal is to raise enough awareness as a call to action for the changemakers in our society to use their power to improve the wellbeing of the general public.

## What it does ⚕️

Health Equity Compass 🧭 is an interactive platform designed to track and analyze healthcare disparities across different geographic regions. It allows users to:

* Explore healthcare center distribution across zip codes
* Upload their own data and receive analysis in real-time based on an automate Python script
* Compare population data with healthcare access to identify underserved communities
* Learn about case studies from existing datasets
* Search for specific regions or demographics to assess healthcare access
* View educational resources on health equity and actionable insights to promote better healthcare allocation

## How we built it ⚒️

We used:

* **Google Colab** with Jupyter Notebooks to develop and run data analysis pipeline
* **Python** for data manipulation and analysis
* **Pandas** to clean and structure the datasets
* **Scipy** for conducting statistical analyses
* **Geopandas** for geospatial mapping of health centers
* **Matplotlib** and **Seaborn** to create visualizations that highlight disparities in healthcare access

## Challenges we ran into 🚧

One of the major challenges we faced was aligning different data sources. Health centers were mapped by zip codes, while population data was segmented by census tracts. We had to find the most accurate and relevant data to our topic, and aggregating the data to find meaningful insights took great effort since we were comparing separate numbers, categories, and statistics. Determining the type of analysis, graphs, plots, and statistical tests was also a challenge on our part, as we had to take into account the type of data we were using, most of which was categorical, and apply the relevant test to draw conclusions from.

## Accomplishments that we're proud of 🏆

We’re proud of successfully creating a tool that offers a clear visualization of healthcare disparities in Philadelphia, providing both general insights and the ability to zoom in on specific zip codes. We also integrated population and healthcare data to offer a more in-depth view of which communities are most affected by disparities. Finally, we implemented a file upload system that takes in user data and runs scripts in real time.

## What we learned 💪

Throughout the project, we learned a lot about how systemic healthcare inequalities affect different demographics. We gained experience in working with large datasets and combining various tools such as Pandas, Scipy, and Geopandas to create meaningful visual representations. By running so many tests and visualizations, we gained stronger skills in using Python and its libraries to perform statistical analysis on datasets. Lastly, the process of geospatial mapping using real data taught us about the technical challenges involved in merging diverse data sources.

## What's next for Health Equity Compass 🔮

Moving forward, we plan to expand Health Equity Compass 🧭 by:

* Integrating additional demographic data, including income levels, health outcomes, and poverty level
* Enhancing user interface and user experience for smoother navigation and better visualization
* Expanding the tool to cover more regions beyond Philadelphia
* Offering real-time updates and notifications when new healthcare data becomes available for the regions users are monitoring
* Partnering with public health departments to make our platform a tool for resource allocation and policy-making
## Inspiration

Charity Bazaar helps charities create economic and social value by working on a global scale with businesses to connect, collaborate and succeed.

## What it does

It provides a platform for businesses to donate their services to charities and also find their next potential clients. Also, it helps charities and non-profit organizations to further enhance their cause using technological solutions.

## How we built it

Alexandra - was responsible for the visuals
Anas - coded the frontend
Fady - coded the backend

## Challenges we ran into

Finding a consistent design language to use for the website

## Accomplishments that we're proud of

How it looks!! And we managed to create the final product.

## What we learned

Fady - How to build REST API
Anas - javascript animations
Alex - Design grids

## What's next for CharityBazaar

Develop the system for tracking the services and hours that businesses are donating to charities. Also, to assist businesses with their taxation. And to help charities understand the value of technological solutions available to them.
## Inspiration

Every year hundreds of thousands of preventable deaths occur due to the lack of first aid knowledge in our societies. Many lives could be saved if the right people are in the right places at the right times. We aim towards connecting people by giving them the opportunity to help each other in times of medical need.

## What it does

It is a mobile application that is aimed towards connecting members of our society together in times of urgent medical need. Users can sign up as respondents which will allow them to be notified when people within a 300 meter radius are having a medical emergency. This can help users receive first aid prior to the arrival of an ambulance or healthcare professional greatly increasing their chances of survival. This application fills the gap between making the 911 call and having the ambulance arrive.

## How we built it

The app is Android native and relies heavily on the Google Cloud Platform. User registration and authentication is done through the use of Fireauth. Additionally, user data, locations, help requests and responses are all communicated through the Firebase Realtime Database. Lastly, the Firebase ML Kit was also used to provide text recognition for the app's registration page. Users could take a picture of their ID and their information can be retracted.

## Challenges we ran into

There were numerous challenges in terms of handling the flow of data through the Firebase Realtime Database and providing the correct data to authorized users.

## Accomplishments that we're proud of

We were able to build a functioning prototype! Additionally we were able to track and update user locations in a MapFragment and ended up doing/implementing things that we had never done before.