Street Savvy
Master's Final project
A mobile web app that aggregates and maps time-sensitive safety data so walkers can make more informed decisions about which route to take. Winner of U.C. Berkeley's 2014 Chen Award for UX.
Applying UX Research with the goal of user empowerment.
Screenshots of working mobile mapping app integrating large data sets, user preferences, and NLP.
Abstract
Pedestrians get stressed when they are routed through unexpected areas by existing mobile mapping applications which account for traffic jams, tolls, and hills—but not safety. StreetSavvy is a web-based mobile mapping decision-support tool that aggregates data pertinent to female pedestrians and provides easy-to-remember directions. StreetSavvy provides users with a combination of contextual time-sensitive data about safety, an easy way to define their own safety preferences, and memory devices to help them navigate a route “hands free.” We successfully researched, identified, and applied UX principles that also encouraged walkers to filter and explore safety data in new ways that challenge negative neighborhood stereotypes. This project aims to improve the pedestrian experience by helping users make informed and thereby confident decisions about which route to walk, increasing the likelihood that women will choose to walk more.
Role
Product Manager
UX Researcher
Exploratory Data Analysis
Assisted with minor NLP and Data Engineering tasks
Data acquisition
Writer
4 Person Team
Poster presentation
