google timeline visualizer: Local Processing Setup Guide

Google Timeline Visualizer turns exported Google location history into an interactive map with routes, points, place visits, activity segments, and date filters. The google timeline visualizer workflow is designed for people who want to inspect their travel history without relying on the discontinued web timeline interface.

The tool reads JSON files directly in the browser. The files can include raw GPS records, monthly Semantic Location History exports, newer phone timeline formats, and supporting metadata. Multiple files can be selected together so data from different exports can be combined for exploration.

Video Highlights:

• Export Google Timeline JSON before attempting visualization.
• Run rendering locally without uploading the source file to a third-party server.
• Test a short video before processing a large location archive.
• Filter dates and select camera behavior for route animations.

The browser-based workflow displays a calendar, map, and day-level details. Depending on the file format, you may also see walking, driving, cycling, transit, stationary, and moving segments. A year filter helps narrow large archives before the browser processes every available point.

The visualizer cannot reconstruct history that is no longer present in your export or on your device. Start by locating every available Google Timeline backup, phone export, or Google Takeout archive. Keep the original files unchanged and make a separate working copy for visualization.

Android and iOS use different menu paths. The exact labels can vary by app version, but the export should produce JSON data that can be saved to a persistent local folder or the Apple Files app.

Use these steps to prepare the data:

The main formats have different strengths. Records.json generally contains raw GPS points and may be the largest file. Semantic Location History files organize visits, addresses, and activity segments by month. Newer phone exports may use fields such as semanticSegments and timeline paths. Settings and edit files can provide additional metadata but may not be sufficient by themselves.

Location history is unusually sensitive. A timeline can expose a home address, workplace, regular appointments, travel dates, and daily routines. Local processing reduces the number of systems that handle this information because the JSON file is interpreted on your own device.

The visualizer described in the available reference material renders data in the browser using JavaScript. Files remain on the device during normal visualization and are removed from the active browser session when the tab is closed. That does not mean every action associated with the broader service is local.

The optional “Save to my Dawarich account” action is different from ordinary visualization. Using that action uploads the selected file to Dawarich Cloud for account import. Treat it as a separate cloud workflow and use it only when you understand the destination and purpose.

Follow these privacy practices before loading a file:

• Work from a private device profile rather than a shared computer.
• Keep exported JSON files in an encrypted or access-controlled folder.
• Avoid uploading files to unrelated websites for format conversion.
• Close the browser tab after reviewing sensitive locations.
• Remove temporary copies when the project is finished.
• Review any optional cloud, account, or sharing control separately from local visualization.

After the map loads correctly, create a short validation video before starting a long render. A minimal test helps identify malformed JSON, missing semantic information, unsupported permissions, and memory pressure without committing the device to a lengthy job.

The video workflow can produce an MP4 from the selected timeline range. Use exact dates to isolate a vacation, a calendar year, or a specific sequence of journeys. The live summary below the date controls can help confirm the selected point count, distance, places, and movement time before rendering.

Use this rendering sequence:

1. Open the visualizer and choose the video creation option.
2. Select the exported JSON file or files.
3. Accept the privacy notice after reviewing the processing behavior.
4. Choose a short duration, such as a 10-second test preset.
5. Preview the route and confirm that the geometry is plausible.
6. Select the date range, camera profile, and final duration.
7. Start the full render and wait for the MP4 to finish.
8. Check the device media library for the completed local file.

A warning about “raw locations only” usually indicates that the export lacks semantic processing. The map may still contain coordinate points, but place names, movement categories, or visit summaries can be limited. For large archives, process one year at a time if the browser becomes unresponsive.

Most problems come from one of four areas: missing local data, unsupported or incomplete exports, excessive archive size, or privacy settings that were misunderstood. Use the checklist below before treating a failed render as a data loss event.

The visualizer has been tested with very large files in the available reference material, including an archive with more than 630,000 location points across more than 15 years. A 170 MB file was reported to take roughly 20–30 seconds on a modern computer, but actual performance depends on browser memory, processor speed, map rendering, and file structure.

For power users, a Python command-line interface is also available in the project documentation for desktop batch conversions. A command-line workflow can be useful when you need repeatable processing across multiple folders, but keep the same privacy standards: work from local copies, verify the script source, and avoid sending files to an untrusted endpoint.

For additional context, review the Google Timeline Visualizer tool page for supported formats, browser-based processing details, and current visualization capabilities.