Safe Maps is a web application that places citizens’ safety at the heart of urban design.
By analyzing crime and spatial data, Safe Maps helps build an understanding of the relationship between safety and the urban environment. It then uses machine learning to predict safety scores based on street features, aiding citizens in safely navigating the city and helping designers and planners make informed decisions regarding the safety implications of urban projects. Future developments of the app include allowing citizens to rate the routes they have taken and report any incidents, as well as integrating large language models (LLMs) to provide design suggestions to enhance safety, empowering both citizens and planners to make small, impactful changes in the urban fabric.
I. PROBLEM DEFINITION
Why Safe Maps?
Defining Safety in the Urban Environment
II. DATA COLLECTION
III. DATA ANALYSIS
IV. MACHINE LEARNING
V. ROUTING
VI. UX / UI
DISCLAIMER: This app does not guarantee safety.
Safe Maps only hopes to contribute citizen’s safety within the urban environment but unfortunately does not solves to the very complex, multilayered and systemic issue of safety. With that being said, we truly believe that with access to the right data and resources, this application can become a reality and can constitute a valuable addition to citizen’s sense of safety as they go about their daily lives within the urban fabric of their cities.
