Thailand COVID-19 Lockdown Mobility Trends

Google Community Mobility Reports track anonymized population movement across six location categories in Thailand during the COVID-19 pandemic. The dataset was used to forecast mobility trends with models like Facebook Prophet, ARIMA, and Feature Engineered XGBoost, analyzing the relationship between movement and case numbers during lockdown phases.

Use Cases

• Forecast mobility trends for six location categories using time series models like Facebook Prophet and ARIMA.
• Apply the Granger Causality Test to examine relationships between mobility patterns and COVID-19 case numbers.
• Train a Feature Engineered XGBoost model to predict human movement across different lockdown phases.
• Analyze mobility changes in specific location categories to inform public health intervention strategies.

Strengths

• Data originates from Google's authoritative Community Mobility Reports.
• Focuses on six distinct location categories for granular mobility analysis.
• License is CC BY 4.0, permitting open sharing and adaptation.

Limitations

• Dataset is very small at 13.5 KB, indicating limited scope and likely few rows.
• Specific column names, row counts, and temporal coverage are unknown from the input.
• Data is tied to a specific study and may not be a raw, comprehensive mobility time series.

Provenance

Source

Google Community Mobility Reports

Collection Method

Anonymized and aggregated location data from mobile devices.

Time Range

COVID-19 pandemic period (specific dates unknown).

Freshness

null

Geography

Thailand