Crowdsourced data collection in citizen science experiments offers valuable insights but also raises significant privacy and usability challenges. When contributors collect data using personal devices, without adhering to strict data collection guidelines, the resulting geolocated data may reveal sensitive information, such as home or workplace locations and mobility patterns. At the same time, uneven spatial coverage in the collected data can lead to an unbalanced distribution, reducing its broader analytical value. This study explores strategies to enhance both privacy and usability of already collected crowdsourced data by refining data processing methods. By addressing issues related to spatial density, movement patterns, indoor/outdoor classification, and geographic contextualization, the aim is to improve the balance between protecting user privacy and preserving the integrity of crowdsourced datasets for scientific research.