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This study assesses Google Location Timeline accuracy for investigations. While GPS offers 52% accuracy, a predictive model achieved 76% accuracy for 2G networks, aiding evidence reliability.

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Area of Science:

  • Digital Forensics
  • Geospatial Analysis
  • Mobile Computing

Background:

  • Google Location Timeline records device locations, offering potential evidence for investigations.
  • Assessing the reliability and accuracy of this location data is crucial for legal and forensic applications.
  • Location data is presented as coordinates with a radius, defining a circular area of uncertainty.

Purpose of the Study:

  • To evaluate the accuracy of Google Location History Timeline data.
  • To identify variables influencing location accuracy, such as connectivity and movement.
  • To develop an initial model for predicting location error based on environmental factors.

Main Methods:

  • Conducted experiments comparing Google Location History data with a high-accuracy reference GPS device.
  • Defined 'Google error' as the distance between the provided and true location.
  • Defined 'hit' as Google error being less than the provided radius; 'Model hit' when actual observation falls within the model's 95% confidence interval.

Main Results:

  • Highest accuracy ('hit rate') achieved with GPS (52%), followed by 3G (38%) and 2G (33%). Wi-Fi yielded only 7%.
  • For 2G/3G, accuracy varied by transport: Still (9% hit rate) vs. Car (57% hit rate).
  • A predictive model using cell tower data, environment, and transport achieved 76% 'Model hits' for 2G networks, outperforming a 3G model (23% success).

Conclusions:

  • Google Location Timeline accuracy is variable and influenced by connectivity, environment, and movement.
  • A predictive model shows promise in estimating location error, particularly for 2G networks.
  • Further development of predictive models can enhance the reliability of location data for forensic evidence.