How to Analyze Your Google Location History with Python (might help with USCIS)

Introduction

Ever wondered how to visualize and analyze your travel history using your Google Location data? This blog post will guide you through the process of extracting your location history from Google, processing it with Python, and visualizing your travel patterns.

Disclaimer

Please note that this procedure is not foolproof and has its limitations. The accuracy of the analysis is dependent on the quality of the location data from Google and the country shapefile used. This guide is intended for educational purposes and should be used as a starting point for your location data analysis.

Step 1: Downloading Your Location History from Google Takeout

Google Takeout allows you to export your data from various Google services, including your Location History from Google Maps.

1. Visit Google Takeout.

2. Deselect all products and then select only "Location History".

3. Choose the JSON format for the export.

4. Click on "Next step" and follow the instructions to download your data.

Step 2: Setting Up Your Python Environment

To process the location data, you'll need Python installed on your computer. If you don't have Python, download it from python.org.

Install Required Libraries

You'll need a few Python libraries: Pandas, GeoPandas, and Shapely. Install them using pip:

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pip install pandas geopandas shapely

Step 3: Downloading the Country Shapefile

A shapefile containing country boundaries is required to identify the countries associated with your location points.

1. Visit Natural Earth and download the "Admin 0 – Countries" dataset.

2. Unzip the downloaded file to a known directory.

Step 4: Processing the Location Data with Python

Create a Python script to process the JSON files from Google Takeout. The script will read the location data, match each location point with a country using the shapefile, and aggregate the data into trips.

Sample Python Script

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# Python script code goes here

Running the Script

Run the script in your Python environment. The script will generate a CSV file with your travel history, including the countries visited and the duration of each visit.

Limitations and Constraints

• Data Accuracy: The analysis is as accurate as the location data from Google and the country boundaries in the shapefile.

• Location Gaps: Some location points might not match any country, especially if they are in the ocean or in disputed areas.

• Privacy Concerns: Be cautious with your location data as it contains sensitive information about your whereabouts.

Conclusion

By following these steps, you can gain insights into your travel patterns using your Google Location History. This process opens up possibilities for further analysis and visualization of your travels.