Recent developments in citizen science, community sensing, and crowdsourcing suggest the possibilities of massive data collection about the natural world supported by many people. The ubiquity of sensor-rich, mobile devices inspires participatory community sensing as a promising approach to enable large-scale data collection with crowds. Despite opportunities, in practice, large-scale data collection about physical environments is currently difficult and not widespread. One, participants need to be interested in contributing, and the efforts for participation may be too high. Two, related to one, is that large-scale data collection needs to be low-effort in order for participants to contribute daily and while on the go. Participation requires focus from the individual, which may be too much effort for everyday participants. Third, participating may require actively figuring out where to look, which is also too much of an ask for the participant. We hypothesize that by enabling low-effort contribution opportunities in a user’s environment, we can gather useful data.

We introduce Gaze, an approach for people to gather information about physical environments while on the go. Gaze is a backend system that collects information on people’s responses to physical spaces. To do this, Gaze connects people to their physical environment by asking them a question about their surroundings, and allows for users to answer.

The main contribution of this paper is the idea of opportunistically enabling low-effort contributions to citizen science or participatory community sensing by connecting people to short data contribution opportunities while on the go. The fundamental technical contribution of this research is a design pattern called Identify-Focus-Capture. With Gaze, we introduce Identify-Focus-Capture as a design pattern for enabling low-effort, on-the-go community sensing (see above). Identify works in the background to search for nearby physical crowdsourcing opportunities that would interest the user given the current situational context. Focus presents cues that draw user’s attention to the opportunity, and Capture prompts the user to contribute by means of answering a question, taking a picture, or simply ignoring the notification. To test the effectiveness of our design pattern, we developed a user facing iOS app to prove that the design pattern can be applied in real world contexts.

Through our pilot, we had a total of 30 subjects participate in the FixTheCity deployment. Together, over the course of the week, they successfully gathered 50+ data points about various tasks around Northwestern’s campus. There were 20 tasks scattered across Northwestern’s campus ranging from asking about full trashcans to open bike racks to cracks in the sidewalk. These numbers proved the Identify part of the Identify- Focus-Capture design pattern was working and that the interaction was low-effort.