Sa-Lee-Ma

The application aims at minimizing the risks of violence in public areas as well as improving women's and youth's mental health. It is an AI enabling crowdsourcing platform which provides real-time safety measures for urban pedestrian mobility (walking, running...). The main goal of this application is to provide safer late night mobility for women and children.

Even in one of the most relatively perceived safe countries around the world such as New Zealand, 36.5 % of women feel relatively safe when walking in the city after dark in comparison to 61.3% of males. 

In Sin el Fil alone studies have shown that youth that witness any form of violent incidents is about 50% (depending on the parts) of which approximately 75 % are women

This shows a massive difference between men and women who feel safe even in their own homes. This was a massive sign that indicates that something must be done whether nationally or locally to reduce such an issue.

The population whos lives we are working on directly and who's lives we want to improve are women and youth. We based our analysis on both field study and research. The field study was done by the youth committee at the municipality of Sin el Fil which showed the statistics written above. Three members of our team are part of the committee and we are working together to reach the best possible solution that targets the issue of violence in public areas as revealed by the studies. This application should help minimize the exposure of women and youth to threatening situations.

Basically, we have created an artificial intelligence crowdsourcing platform which includes:

1- safety optimal routing: this feature provides users with the safest routs to reach a certain destination based on several criteria including the number of people available in the area (on the streets) with the help of google maps ( which provides the live street view of it). 

2- After the artificial intelligence indicates whether the destination is safe or not based on several criteria (police reports on the history of the area, newspaper reports, street view analysis, traffic and human flow, opening hours of known stores, lighting...), it will proceed to give the user possible advice based on the level of risk in his current location.

3- Users provide feedback after taking their routs and the level of safety reinforcing the predictions of our AI. This means that the user will write his own reviews and rate the application and the destination. This will the grow and as we possess more and more reviews, we will be able to give more accurate results and hence more safety. 

4- If routing is not considered safe, the application will suggest and can suggest the best possible action to take place: Take an Uber, call a nearby friend, call the police in severe conditions etc... 

Note that the user can choose a list of favorites from which to call based on the distance and personal preference when in need.