Participatory sensing systems in which people actively participate in the data collection process must account for both the needs of data contributors and the data collection goals. Existing approaches tend to emphasize one or the other, with opportunistic and directed approaches making opposing tradeoffs between providing convenient opportunities for contributors and collecting high-fidelity data. Instead, this work explores a new, hybrid approach, in which collected data–even if low-fidelity initially–can provide useful information to data contributors and inspire further contributions.

We realize this approach with 4X (eXplore, eXpand, eXploit, eXterminate), a multi-stage data collection framework that first collects data opportunistically by requesting contributions at specific locations along users' routes and then uses collected data to direct users to locations of interest to make additional contributions that build data fidelity and coverage. 4X scaffolds data in four stages. People first contribute opportunistically, marking new landmarks for tracking and responding when queried for information as they pass existing landmarks (eXplore). Collected data then draws people to target locations outside of their path, where they can be queried for additional contributions (eXpand). People may also be queried en route to the target location for contributions (eXploit). As data at a location fills (indicated by the size and the darkness of circles), users are directed to other regions and locations in need of contributions (eXterminate).

To study the efficacy of 4X, we implemented 4X into LES, an application for collecting information about campus locations and events. Results from two field deployments (N = 95, N = 18) show that the 4X framework created 34% more opportunities for contributing data without increasing disruption, and yielded 49% more data by directing users to locations of interest. Our results demonstrate the value and potential of multi-stage, dynamic data collection processes that draw on multiple sources of motivation for data, and how they can be used to better meet data collection goals as data becomes available while avoiding unnecessary disruption.