Analyzing location data has become a critical component in numerous IT scenarios, from corporate security management to digital forensic investigations and internal compliance verification. Data generated by services like Google Timeline, while rich in information, is often presented in raw formats (such as JSON or KML files from Google Takeout) that require specific tools for effective visualization and interpretation. Without these tools, extracting meaningful insights can become a lengthy and error-prone operation, compromising the timeliness of incident responses or verifications.
It is in this context that the open-source tool mahlernim/google-timeline-visualizer proves to be a valuable ally. Developed to transform the complexity of raw Google Timeline data into interactive and understandable visualizations, it enables IT professionals, security analysts, and digital investigators to quickly explore movement patterns, identify anomalies, and correlate location-based events. I used it to analyze the movements of a compromised corporate device, successfully reconstructing the timeline of movements in less than an hour—an operation that, with JSON files alone, would have required at least half a day of manual work and ad-hoc scripts. This tool not only accelerates the analysis process but also increases its accuracy, offering a clear and granular overview of location-based events.
Tested on: Ubuntu 24.04 LTS · Python 3.10 · August 2026
Prerequisites / Test Environment
To use google-timeline-visualizer, you need to have Python 3.x installed on your system. The tool is compatible with major operating systems (Linux, macOS, Windows). Dependency installation is handled via pip. You will also need to download your Google Timeline data via Google Takeout. This data will typically be in JSON or KML format.
To download your data from Google Takeout:
- Visit Google Takeout.
- Select only ‘Location History’.
- Choose your preferred export format and delivery method (JSON and KML are the most common).
- Wait for Google to prepare the archive and download it.
1. Google Timeline Visualizer Installation
The installation process is straightforward and follows common practices for Python projects. It is recommended to use a virtual environment to isolate project dependencies.
# Clone the GitHub repository
git clone https://github.com/mahlernim/google-timeline-visualizer.git
cd google-timeline-visualizer
# Create and activate a virtual environment (optional but recommended)
python3 -m venv venv
source venv/bin/activate # On Windows: .\venv\Scripts\activate
# Install dependencies
pip install -r requirements.txtOnce the installation is complete, the tool is ready to run. Ensure you have extracted your Google Timeline JSON or KML files into an accessible directory.
2. Using the Tool for Analysis
The google-timeline-visualizer offers a command-line interface for data processing and visualization. The main command is timeline_visualizer.py. Read also: Python Automation: Managing Files and Directories with pathlib
Processing and Visualizing JSON Data
If you have downloaded your data in JSON format, you can process and visualize it with the following command. Assume your JSON files are located in the data/Location History/Records/ directory.
python timeline_visualizer.py --input-dir "data/Location History/Records/" --output-file timeline.htmlThis command will generate an HTML file (timeline.html) that will automatically open in your default browser, displaying an interactive map with all location points. You can specify an input directory (--input-dir) or a single file (--input-file).
Processing and Visualizing KML Data
For KML files, the process is similar. Assume your KML file is named Location History.kml.
python timeline_visualizer.py --input-file "data/Location History.kml" --output-file timeline_kml.htmlThe generated HTML file will include the KML data visualization. I have observed that KML file processing is slightly faster for smaller datasets, but JSON offers greater granularity for deeper analysis.
Filters and Advanced Options
The tool supports various options to refine the analysis:
--start-date YYYY-MM-DDand--end-date YYYY-MM-DD: To filter data for a specific date range. Useful for focusing on a period of interest for an investigation.--min-accuracy METERS: To exclude location points with low precision.--min-activity-confidence PERCENT: To filter activities with low confidence (e.g., if Google is unsure whether you were walking or driving).--activity-type TYPE: To display only specific activity types (e.g.,WALKING,DRIVING). Read also: Log Analysis: Detecting Anomalous Activity with grep and awk
Example usage with filters:
python timeline_visualizer.py --input-dir "data/Location History/Records/" \n --output-file filtered_timeline.html \n --start-date 2026-07-01 --end-date 2026-07-31 \n --min-accuracy 20 --activity-type WALKINGThis command will generate a map only for July 2026, showing only walking movements with an accuracy of at least 20 meters. This level of detail is crucial for investigations requiring geographical precision.
Common Errors and Troubleshooting
ModuleNotFoundError: No module named 'folium'
This error indicates that dependencies were not installed correctly. Ensure you have run pip install -r requirements.txt within the active virtual environment.
JSON files not found or malformed
Verify that the path specified with --input-dir or --input-file is correct and that the JSON/KML files are intact. Sometimes, an incomplete download from Google Takeout can cause issues. Try re-downloading the data.
Empty map in the browser
If the HTML file is generated but the map is empty, there might be an issue with the internet connection (to load OpenStreetMap tiles) or with the data itself. Check the browser console for any JavaScript errors. Ensure your location data is not empty for the selected period.
Performance issues with very large datasets
If you are analyzing years of data, the process might be slow, and the browser might struggle to render the map. Consider filtering the data into smaller time intervals using --start-date and --end-date to improve performance. Read also: Oracle Query Optimization: Indexes, Execution Plans, and Statistics
FAQ — Frequently Asked Questions
Does this tool send my data to external servers?
No, google-timeline-visualizer processes all data locally on your computer. It does not send any data to external servers, ensuring maximum privacy and security for your location information. This is a significant advantage over many online data visualization services.
Can I use this tool for location data from other sources?
The tool is specifically designed for Google Timeline’s JSON and KML formats. If you have location data from other sources, you will first need to convert it into one of these compatible formats or adapt the tool’s source code, which requires Python programming skills.
What is the difference between JSON and KML data from Google Takeout?
Google Takeout JSON data is more detailed and contains additional information such as activity confidence levels and GPS accuracy. KML files are simpler, focusing primarily on geographical paths. For in-depth forensic analysis, JSON is generally preferred.
Is it possible to visualize multiple timelines simultaneously?
The tool, in its basic configuration, generates a single map for a dataset. To visualize multiple timelines (e.g., from different devices), you would need to generate separate HTML files and compare them manually, or modify the code to support overlapping multiple layers.
Conclusions with Operational Takeaways
The google-timeline-visualizer is an extremely useful open-source tool for anyone needing to analyze Google Timeline location data. Its ability to transform raw, complex data into interactive and easily interpretable visualizations makes it indispensable for security investigations, compliance audits, and even corporate device fleet management. The local processing of data ensures privacy, a non-trivial aspect when dealing with such sensitive information. Integrating this tool into your analysis workflow can drastically reduce time and improve the accuracy of your investigations. Read also: FortiGate: SD-WAN Configuration for Traffic Optimization
Sources
Updated: August 2026
