Using the TransBigData Python Library for Mobile Signaling Data Processing, Analysis, and Visualization

Earlier we introduced using the TransBigData library to visualize taxi GPS data; this article extends the discussion to mobile signaling data, which are abundant in everyday life.

TransBigData is a Python package designed for processing, analyzing, and visualizing spatiotemporal transportation data such as taxi GPS, shared‑bike, and bus GPS records. It provides methods for data preprocessing, gridding, visualization, trajectory handling, map basemap, coordinate conversion, and special tasks like extracting order start‑end points or building GIS network topologies.

The library can be installed via pip install -U transbigdata and imported with import transbigdata as tbd . Mobile signaling data are read with pandas, timestamps are converted to datetime, and the dataset is sorted by user and time.

Using tbd.mobile_stay_move together with a grid defined by tbd.area_to_params , the data are converted to stay (activity) and move (travel) records, as illustrated by the resulting figures.

Home and work locations are identified from the stay records with tbd.mobile_identify_home and tbd.mobile_identify_work , which apply night‑time longest stay and weekday daytime longest stay rules respectively.

Finally, individual user activity can be visualized with tbd.mobile_plot_activity , producing an interactive plot where each color represents a distinct activity period.