Activity trackers are being deployed in large-scale physical activity intervention programs, but analyzing their data is difficult due to the large data size and complexity. As such large datasets of steps become more available, it is paramount to develop analysis methods to deeply interpret them to understand the variety and changing nature of human steps behavior. In this work, we explored ways to analyze the heterogeneous steps activity data and propose a framework of dimensions and time aggregations to interpret how providing a city-wide population with activity trackers, and monetary incentives influences their wearing and steps behavior. We analyzed the daily step counts of 140,000 individuals, walking a combined 74 billion steps in 305 days of a city-wide public health campaign. We performed data mining clustering to identify 16 user segments, each with distinctive weekly streaks in patterns of device wear and recorded steps. We demonstrate that these clusters enable us to interpret how some users increased their steps level. Our key contributions are: a new analytic method to scalably interpret large steps data; the insights of our analysis about key user segments in our large intervention; demonstrating the power to predictive user outcomes from their first few days of tracking.