Our research presents a methodological framework for analyzing bicycle-sharing systems, using the self-service bike operations of JCDecaux in Toulouse as a case study. The objective was to identify a method for obtaining a cleansed and structured attribute list that could be useful in evaluating and optimizing the placement of bicycle rental docks. Utilizing open data, our approach involves developing a Python script within QGIS to create new layers around each of the 288 studied bicycle rental stations, based on a selected 100-meter buffer. This buffer size is chosen to reduce data overlap in dense urban settings. The script is designed to collect urban features within these buffers that register as multipolygons (mainly buildings) or points (amenities, transportation features), moreover it applies categorization of data, such as identifying and marking the different building types. The method includes a basic visualization of potential data in QGIS using OpenStreetMap.