Bohyemian Lab | Hyemi Song

City Ways

This project was to reveal human's recreational patterns through visualizing datasets collected with self-tracking applications. By comparing the dataset and external factors especially weather, users can have their own suppositions and inquiries about the human's seasonal trip trends of cities, particularly in Boston and San Francisco.
Work Type
Tool

Commercial at MIT SCL
d3.js, Leaflet.js, Mapbox, HTML, CSS, Javascript

Project Site Full Process

Walkability in a city, Data from Self-Tracking Applications
We explore the cities of San Francisco and Boston using billions of data points collected via activity monitoring apps. Tens of millions of self-tracking applications are used by people eager to monitor how many calories they consume, how many hours they sleep, how many steps they take.

By analyzing such data, we can start to understand the factors that influence outdoor human activity—such as weather, urban morphology, topography, traffic, the presence of green areas, etc.