## Inspiration

We are students from Vanderbilt University, Nashville, TN.
**Nashville, aka music city,** is also one of the most dangerous cities in the US. It ranks #15 last year for its crime rate. Violent Crime Rate = 1,101/per 100,000 people. 10 days ago, we went to a hookah bar very close to Vanderbilt, we did not stay there for very long but two security guards got shot at that bar the very same night. When we looked it up, we found that there had been previous instances of similar incidents happening in the same area and we had no idea about it.

This freaked us out. How could we enjoy the city safely? How could we avoid the unsafe areas?
The fact is, a lot of cities in the US are not safe to walk. How do we solve this problem?

As data science students, we wanted to find a data-driven solution that can help people know when they are walking through an area with violent crime.

This year, our theme is connecting the dots. When we think about **connecting the dots**, daily navigation come to our mind. Is it possible to use the crime event data to build a navigation application and help people know if they are about to be walking through a particularly unsafe area?

## What it does

This application will show you areas with violent crimes and help you navigate around them. It can also help you know if you are in a particularly crime-heavy area.

A lot of useful information such as Vanderbilt University Police Department contact info and nearest hospital to your location are provided as well.

## How we built it

1. We created data pipelines using python to extract Nashville open crime incident data, clean the data and add the features.
2. We use google maps api to help us with the navigation part and we push it to the flask.
3. We use Flutter to develop the mobile application.

## Challenges we ran into

1. No experience in phone app development.
2. No experience with using google maps api.

## Accomplishments that I'm proud of

Finishing the application to a decent extent.

## What I learned

Phone app development and developing pipelines between different components of a software.

## What's next for Walkbuddy

1. Route optimization calculation
2. Live location
3. Location Sharing
4. Safety scores of different paths

### References:

[Two Security Guards Got Shot At Nashville Hoookah Bar](https://www.wsmv.com/news/two-security-guards-shot-at-nashville-hookah-bar-overnight/article_0150204a-edcf-11e9-bffe-5fa2003f5895.html)

[World's Most Dangerous Cities](https://www.worldatlas.com/articles/most-dangerous-cities-in-the-united-states.html)
## Inspiration

Approximately **half** of the United States’ annual violent crimes are reported each year. Much of this discrepancy can be attributed to a widespread fear of calling 911. To provide a safeguarding mechanism for situations precluding conventional means of communication, our project undertakes the development of a sophisticated real-time monitoring and alerting system.

Identifying the difficulty of talking aloud in many situations showed us the immense potential that an audiocentric emergency reporting app has to reduce this statistic. This system is predicated upon intricate audio data analysis for the explicit purpose of crime detection and the facilitation of swift emergency response.

## What it does

At the heart of this convenient app lies a high-level architectural framework for audiocentric crime detection. The crux of our technical infrastructure is the application of advanced deep learning paradigms, notably the integration of stacking classifiers, which consists of multiple classifiers such as RandomForestClassifier, SGDClassifier, Support Vector Classifier, etc.

Augmenting this neural architecture is the strategic deployment of integration of classification models as well as data transformation and dimension reduction, thereby optimizing the intricate classification of audio clips based on the nature of the crime they encapsulate.

## How we built it

The backend of the pipeline was written in Python, using Flask and Django as the backbone. The backend is supported by a pipeline of stacking classification models that determines the type of crimes the users ran into by collecting the surrounding ambient sounds that could signal a dangerous environment.

For the frontend, we utilized Android Studio to create the interactive user interface of the project, which will enhance the user experience when using the application. The minimalist design for the frontend ensures that the users can spend the least amount of time interacting with our product as it is designed for emergency use, thus requiring minimum initiation steps. The integration of the interactive frontend with a convolutional, yet efficient backend are the characteristics of our product.

## Challenges we ran into

*ML Model*:
We struggled to find the right parameters for the classification model that resulted in a notable amount of time spent on adjusting minor values to maximize accuracy.

*Android Studio*:
It was difficult to create a minimal user interface that would recognize a finger holding the record button in loops of 5 seconds. There was some unexpected difficulty in using the “Audio Recorder” constructor of Android Studio that provides live feedback as the audio is being fed in.

## Accomplishments that we're proud of

The foundation of our app depends on the pipeline developed to classify types of violent crimes. Though the accuracy of the model could be improved on in the future, it was a fundamental step towards knowing that we could bring safety many steps closer to individuals in such dangerous situations.

## What we learned

Among others, we learned that it’s important to achieve the overall goal first - regardless of how well it was reached. We struggled to maximize our accuracy with the pipeline - having switched from CNN to our original method largely due to a lack of computation power - but had to realize that it is better to walk away with something over nothing.

## What's next for SilentSignal

Shorter timeframe & location
In the future, it would be greatly beneficial to be able to shorten the required timeframe of 5 seconds so that we can identify and report situations where even that is too long. Additionally, it is crucial to know where the user is located as we will be contacting local officials, so this is also an area of interest.
## Inspiration

As college students learning to be socially responsible global citizens, we realized that it's important for all community members to feel a sense of ownership, responsibility, and equal access toward shared public spaces. Often, our interactions with public spaces inspire us to take action to help others in the community by initiating improvements and bringing up issues that need fixing. However, these issues don't always get addressed efficiently, in a way that empowers citizens to continue feeling that sense of ownership, or sometimes even at all! So, we devised a way to help FixIt for them!

## What it does

Our app provides a way for users to report Issues in their communities with the click of a button. They can also vote on existing Issues that they want Fixed! This crowdsourcing platform leverages the power of collective individuals to raise awareness and improve public spaces by demonstrating a collective effort for change to the individuals responsible for enacting it. For example, city officials who hear in passing that a broken faucet in a public park restroom needs fixing might not perceive a significant sense of urgency to initiate repairs, but they would get a different picture when 50+ individuals want them to FixIt now!

## How we built it

We started out by brainstorming use cases for our app and and discussing the populations we want to target with our app. Next, we discussed the main features of the app that we needed to ensure full functionality to serve these populations. We collectively decided to use Android Studio to build an Android app and use the Google Maps API to have an interactive map display.

## Challenges we ran into

Our team had little to no exposure to Android SDK before so we experienced a steep learning curve while developing a functional prototype in 36 hours. The Google Maps API took a lot of patience for us to get working and figuring out certain UI elements. We are very happy with our end result and all the skills we learned in 36 hours!

## Accomplishments that we're proud of

We are most proud of what we learned, how we grew as designers and programmers, and what we built with limited experience! As we were designing this app, we not only learned more about app design and technical expertise with the Google Maps API, but we also explored our roles as engineers that are also citizens. Empathizing with our user group showed us a clear way to lay out the key features of the app that we wanted to build and helped us create an efficient design and clear display.

## What we learned

As we mentioned above, this project helped us learn more about the design process, Android Studio, the Google Maps API, and also what it means to be a global citizen who wants to actively participate in the community! The technical skills we gained put us in an excellent position to continue growing!

## What's next for FixIt

An Issue’s Perspective

 \* Progress bar, fancier rating system

 \* Crowdfunding

A Finder’s Perspective

 \* Filter Issues, badges/incentive system

A Fixer’s Perspective

 \* Filter Issues off scores, Trending Issues