Using Google Location History data to quantify fine-scale human mobility.

Area of Science:

• Social and Behavioral Sciences
• Epidemiology
• Demography

Background:

• Understanding human mobility is crucial for global health and social science issues like disease spread and population displacement.
• Existing data sources (surveys, GPS trackers) capture specific types of movement but fail to provide a comprehensive overview.
• There is a need for readily obtainable data that can address diverse human movement patterns across various scales.

Purpose of the Study:

• To evaluate Google Location History (GLH) data as a novel source for understanding human mobility patterns.
• To assess the feasibility of using GLH data to capture both fine-scale and long-distance/international movements.
• To validate GLH data against established methods like GPS tracking.

Main Methods:

• Collected and analyzed Google Location History (GLH) data from Android smartphone users.
• Validated GLH data by comparing it with GPS tracker data from Android users in the United Kingdom.
• Assessed the temporal span, spatial granularity, and types of movement captured by GLH data.

Main Results:

• GLH data provide long temporal coverage (over a year) and high spatial accuracy (within 100m), comparable to GPS trackers.
• GLH data capture a greater extent of international movement than traditional survey data.
• Passive data collection in GLH avoids compliance issues associated with GPS trackers and biases in self-reported travel.

Conclusions:

• Google Location History (GLH) data represent an underutilized and powerful dataset for human mobility research.
• Despite existing population biases, the increasing global adoption of Android phones makes GLH data relevant for diverse scientific inquiries.
• GLH data offer novel insights for infrastructure planning, infectious disease control, and disaster response.