Today, more and more digital data are generated by urban dynamics. Yet, the generated data is extensive and heterogenous. Datasets are large, multi-sourced, often noisy, and come in various formats and standards. In addition, a particularity of urban data is its mix in terms of level of restriction. While geolocation data is private and sensitive, public transit schedules are open data. In this context, there is a need (i) to provide a framework to re-unify the multiformity of urban dynamics data, (ii) to articulate open and restricted data, (iii) to cohere offer and demand data, and (iv) to keep track of a privacy metric. This project proposes to develop and release an open Python package to address these four needs and therefore contribute to Urban Mobility Open Research Data practices.