Overview

Competition: LA Hacks 2019

Team Formation: 4 software engineers and 1 UX designer  

Goal: To create an app in 36 hours which helps LA citizens find the safest route among all routes Google Map provided

Inspiration

According to a 2014 crime survey, 37% of adults feel unsafe walking alone at night in the US. Los Angeles, in particular, has a crime rate higher than 84% of the state's cities and towns of all sizes. Keeping this in mind, we believe it is crucial for residents and visitors alike to be aware of the safest streets to take at any time. We decided to build an app in order to help passengers choose the least risky path they should take, focusing on the LA area. 

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According to a 2014 Crime Survey, 37% of adults feel unsafe walking alone at night in the US. In particular, Los Angeles has a crime rate higher than 84% of the state's cities and towns of all sizes. Keeping this in mind, we believe it is crucial for residents and visitors alike to be aware of the safest streets to take at any time. Therefore, we decided to build an app in order to help passengers choose the least risky path they should take, focusing on the LA area. 

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"Safe Route" is the dream app for pedestrians who wish to navigate their daily life safely and efficiently! Once the passenger inputs a destination on Google Map, based on historical data on crime rates, the application automatically calculates its safety score (-2 to 2) for each route. The user can preview routes and decide which path to take. 

Furthermore, "SafeRoute" provides an up-to-date and accurate estimation for every two hours of a day. 

Machine Learning model using the LA Crime Data Set

How We Built It

"Safe Route" was built using Google's new Flutter language as the front-end, and the machine learning algorithm as the back-end. Specifically, we coupled the publicly released LA crime datasets with the robust Google Maps APIs to enable us to provide an insightful safety score. This score is determined by calculating the relative frequency of crime in each area and surrounding areas. 

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We want to make sure everyone could look at our safety score to get a quick understanding of what to expect for a given path, so we incorporated visual representations on the UI design. Furthermore, to ensure the safety score is accurate, we reorder our data set according to the time input and run a separate algorithm every 2 hours.  

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The GitHub repo is here. 

Users can input their locations in the text area or point on the map, and then press the "Safe Route" button. 

The app automatically calculates safety scores for all paths. 

Users can preview the selected route and start navigation. 

Major Roles

1. Design the user interface and user experience. 

2. Document the design thinking process, including target users, unique features, user scenarios, etc.

3. Assist with front-end mobile programming using python and flutter.io

Takeaways

1. Gain precious real-life experience in app development and Interactive Design. 

2. Machine Learning algorithms and data visualization are essential to represent a safety dataset. 

3. Strong communication and great teamwork made the construction of this application possible.