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Anonymised human location data in England for urban mobility research.

Chen Zhong1, Zhengzi Zhou2, Nilufer Sari Aslam2

  • 1Centre for Advanced Spatial Analysis, University College London, London, W1T 4TJ, UK. c.zhong@ucl.ac.uk.

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Summary

This study presents a reproducible framework for processing human mobility data from mobile apps. The methods enhance data transparency and generate reliable mobility indicators and origin-destination flow matrices for research.

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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.