Anonymised human location data in England for urban mobility research

Area of Science:

• Geospatial science
• Data science
• Transportation studies

Background:

• Mobile applications generate vast amounts of human mobility data.
• Existing data processing methods lack transparency and reproducibility.
• Variability in processing necessitates tailored validation approaches.

Purpose of the Study:

• To provide a reproducible and replicable framework for processing location-point data.
• To enhance the transparency and reliability of human mobility data analysis.
• To facilitate broader research applications using mobility data.

Main Methods:

• Developed a modular workflow for processing anonymised mobility records.
• Implemented multi-stage validation techniques for stay-point detection and activity labelling.
• Utilized a case study of mobility data from England (November 2021).

Main Results:

• Demonstrated a framework for reproducible stay-point detection and activity labelling.
• Generated reliable mobility indicators and origin-destination flow matrices.
• Publicly released anonymised trajectories and flow matrices.

Conclusions:

• The proposed framework enhances reproducibility in human mobility data processing.
• The framework supports the generation of reliable mobility datasets for research.
• Openly available code and data promote transparency and further research.