GAZE

Abstract

Developments in citizen science, community sensing, and crowdsourcing offer the opportunity of large scale data collection of the physical world because of the ubiquity of sensor-rich, mobile devices. Despite this opportunity, large-scale data collection about physical spaces is currently not widespread because of high-effort participation. In this paper, we explore the ability for people to contribute on the go. We developed Gaze, a system that will collect information about people's responses to physical spaces through low-effort feedback. To enable low-effort contributions for large scale data collection, we have developed a design pattern called Identify-Focus-Capture that identifies opportunities for users given current situational context, helps users to focus in on the opportunity, and captures useful data through simple actions or gestures. Through our pilot, users successfully helped collect 50+ data points about their environment, showing that useful data can be collected when the opportunity is low-effort.