- Google Timeline Visualizer turns exported location JSON into maps, routes, visits, and activity segments.
- Best starting point: Export your Timeline files, then drop one or more JSON files into the browser tool.
- Useful filters: Switch between years, inspect individual dates, and compare movement patterns over time.
- Privacy focus: Browser-based processing keeps files on your device unless you choose an optional cloud import.
- Supported data: Records.json, Semantic Location History, Phone Timeline Export, and related metadata files.
What Google Timeline Visualizer Does
Google Timeline Visualizer is a browser-based data visualization tool for exploring exported Google location history. Instead of treating your archive as a collection of unreadable JSON files, it presents routes, place visits, movement types, dates, and map points in a visual timeline.
The tool is useful when Google Timeline is no longer available in the same web format or when you want to examine your own archive independently. You do not need to connect a Google account to inspect files locally. Open the Google Timeline Visualizer and add your exported JSON files to begin.
Use the visualizer when you want to rediscover travel patterns, confirm a past visit, review daily movement, or study how your habits changed across multiple years.
Routes
View individual paths and movement points on an interactive map. Routes can reveal daily commutes, road trips, and longer journeys.
Place Visits
Inspect locations with arrival and departure details when those values are available in the exported data.
Activity Types
Separate walking, driving, cycling, transit, stationary periods, and other semantic activity segments.
Year Filters
Focus on one year at a time to compare routines, travel frequency, and changes in geographic activity.
The visualizer can combine multiple files from different export methods. This is especially helpful when your archive contains monthly records alongside a newer phone-generated Timeline export.
| Visualization Feature | What It Shows | Best For |
|---|---|---|
| Calendar view | Dates with available location activity | Finding a specific day |
| Interactive map | Points, paths, and visited areas | Reviewing routes |
| Activity labels | Walking, driving, cycling, transit, or stationary periods | Comparing movement types |
| Year filter | Data grouped by selected year | Studying long-term changes |
| Place details | Locations and visit timing | Recalling forgotten stops |
Supported Google Timeline File Formats
Google location exports can appear in several structures. File names and fields vary depending on whether the data came from Google Takeout, a phone export, or an older Semantic Location History archive. The visualizer is designed to detect common formats automatically, so you usually do not need to convert the files manually.
Make a backup before editing, renaming, or moving your JSON files. Location archives can contain years of personal movement data, and a second copy makes recovery easier.
The main formats have different strengths. Records.json is usually the largest and most granular file, while semantic files are often easier to interpret because they include visits, addresses, and activity segments.
| File Format | Typical Contents | Main Strength | Common Consideration |
|---|---|---|---|
| Records.json | Raw GPS records in E7 coordinate format | Detailed location points | Can be large and slower to process |
| Semantic Location History | Monthly visits, addresses, and activities | Readable travel context | Usually split into multiple monthly files |
| Phone Timeline Export | semanticSegments and timeline paths | Newer mobile export structure | Field names differ from older archives |
| Settings and TimelineEdits | Preferences and timeline adjustments | Additional metadata | May not contain the main route history |
Records.json and Semantic Location History
Records.json focuses on raw location pings. It can show a highly detailed trail, but the file may contain hundreds of thousands of points. Semantic Location History organizes information into monthly files and adds higher-level descriptions, such as a visit to a place or a segment identified as driving.
If you have both types, upload them together when practical. Combining files can provide a broader view, although duplicate records or overlapping time periods may require careful interpretation.
Phone Timeline Export
The phone export format is designed around newer Timeline data stored on mobile devices. It may include semanticSegments and timeline paths rather than the structures found in older Takeout archives. Uploading the original JSON files gives the visualizer the best chance of recognizing the available fields.
Step-by-Step Data Visualization Workflow
A reliable workflow starts with file organization and ends with targeted exploration. Avoid opening a very large archive without first knowing where it came from. Labeling folders by export method and year makes it easier to identify duplicates and isolate incomplete files.
Start with one small or clearly labeled file. Confirm that dates and locations appear correctly, then add larger files or additional months in batches.
Locate Your Export
Find the Google Timeline JSON files saved from Google Maps or Google Takeout. Common candidates include Records.json, monthly Semantic Location History files, and Phone Timeline Export files.
Open the Visualizer
Visit the browser-based tool and wait for the interface to load. No Google account connection is required for local visualization.
Drop JSON Files Into the Page
Drag one or more supported files into the upload area. Multiple files can be combined when you need to reconstruct a longer period or merge export methods.
Choose a Year or Date
Use the calendar and year controls to narrow the display. Begin with a known trip, commute, or place visit so you can quickly confirm that the data loaded correctly.
Inspect Routes and Activities
Select map points, paths, place visits, or activity segments. Compare movement labels with the map route and remember that exported classifications may not always reflect your exact activity.
The following sequence is useful when reviewing a large archive:
| Review Stage | Action | Result |
|---|---|---|
| Load test | Open one representative JSON file | Confirms the format is recognized |
| Date check | Select a known day or year | Verifies timestamps and calendar coverage |
| Map check | Inspect a familiar route | Confirms points appear in the expected area |
| Activity check | Compare walking, driving, or transit labels | Shows how semantic data was classified |
| Archive review | Add related files in batches | Expands coverage while limiting confusion |
When a file takes time to process, keep the browser tab open and avoid repeatedly dropping the same archive. Large raw-record files require more work than a small monthly semantic file. A staged review is easier to manage than loading every archive at once.
Insights You Can Discover in Your Location Data
A timeline map is more than a route viewer. Once the archive is organized, it can help you identify recurring patterns and compare different periods. Use the visualizer as a personal data exploration tool rather than assuming every point represents a precise factual record.
GPS accuracy, missing signals, device settings, and automated activity labels can affect the display. Treat unusual points as clues for review, not as definitive evidence of movement.
Travel and Routine Patterns
Year-by-year filtering makes long-term comparisons easier. You can look for changes in commute distance, the number of places visited, or the balance between local routines and longer trips. A map may also reveal neighborhoods, parks, restaurants, or transit routes that you remember only after seeing them visually.
Place Visits and Forgotten Memories
Place visits are often more useful than isolated GPS points because they provide context around a stop. Review arrival and departure times where available, then compare the place with nearby route segments. This can help distinguish a short pass-through from a longer visit.
Activity and Movement Segments
Activity classifications may include walking, driving, cycling, transit, and stationary periods. Comparing these categories across years can help you understand how your travel habits evolved. For example, a period with more walking segments may correspond to a new neighborhood, workplace, or travel routine.
| Insight Area | Questions to Ask | Useful View |
|---|---|---|
| Daily routine | Which routes appear repeatedly? | Map and date view |
| Travel history | Which years contain the most long-distance movement? | Year filter |
| Place discovery | Which locations have repeated visits? | Place details |
| Activity balance | How often do walking and driving segments appear? | Activity labels |
| Data gaps | Which months or dates show little coverage? | Calendar view |
Comparing the Visualizer With Other Tools
A heatmap emphasizes density, while a timeline visualizer emphasizes individual points, routes, place visits, and activity segments. These views answer different questions.
| Tool Type | Primary View | Strength | Limitation |
|---|---|---|---|
| Timeline visualizer | Points, paths, visits, and dates | Detailed chronological review | Large files may need more processing time |
| Heatmap generator | Location density | Quickly shows frequently visited areas | Less detail about individual visits |
| Google Maps Timeline app | On-device Timeline access | Convenient for current mobile data | Depends on device availability and backup state |
| Cloud import service | Persistent account-based history | Easier long-term access across sessions | Requires uploading selected data |
For a private archive review, local browser visualization is the most direct starting point. If you need a permanent, synchronized history, review the destination’s privacy terms before using an optional cloud import.
Privacy, Performance, and Archive Management
Location history is highly sensitive because it can reveal home areas, workplaces, routines, travel, and personal appointments. A privacy-first workflow limits unnecessary uploads and keeps the original archive under your control.
The browser tool processes visualization locally according to the referenced tool description. The optional “Save to my Dawarich account” action is different because it uploads the selected file for cloud import. Decide which workflow fits your needs before choosing that option.
Do not select a cloud-saving option casually. Check what you are importing, remove unrelated files, and read the current service privacy information before sending location data away from your device.
Performance Planning
Large files can take longer to parse, especially when they contain raw GPS records. Processing in batches helps keep the browser responsive and makes it easier to identify which file caused a problem.
| File Size or Scope | Suggested Approach | Why It Helps |
|---|---|---|
| One monthly file | Load directly | Fast format and date check |
| Several monthly files | Add them in logical groups | Simplifies troubleshooting |
| Large Records.json archive | Allow extra processing time | Raw points require more parsing |
| Multiple export methods | Test each type, then combine | Reduces duplicate or format confusion |
| Very broad archive | Review by year | Keeps the map easier to interpret |
Privacy Checklist
Before You Visualize:
- Create a backup of the original JSON archive
- Confirm that the files belong to the intended account and date range
- Start with a small file before loading a large archive
- Avoid cloud import unless you understand the upload behavior
- Close the browser tab after reviewing sensitive location data
If the map appears incomplete, check whether the archive covers the expected period. Missing months may indicate that the export did not include every available file, that the data was stored only on a device, or that an older backup was not preserved.
Google Timeline Visualizer FAQ
The answers below focus on practical setup, supported files, privacy, and interpretation. Keep the original exports available if you plan to compare results across different visualization tools.
If nothing appears, verify that the file is valid JSON, try a smaller export, and check whether the archive uses one of the supported Timeline structures.
Q: Is Google Timeline Visualizer safe for private location data?
The referenced browser tool is designed to visualize files locally, so your data can remain on the device while you explore it. The optional Save to my Dawarich account action is different because it uploads a file for cloud import. Review the current privacy terms before using any upload feature.
Q: Which files can I upload to Google Timeline Visualizer?
Common supported formats include Records.json, monthly Semantic Location History files, Phone Timeline Export files containing semanticSegments and timeline paths, plus related Settings and TimelineEdits metadata. The visualizer can auto-detect formats and combine multiple files.
Q: Can I view Google Timeline data without Google Maps?
Yes. After you export your Timeline data, you can open the JSON files in the browser-based visualizer without connecting a Google account. The tool provides a calendar, map, route details, place visits, and activity information from the files you supply.
Q: Can I visualize several years of location history?
Yes, provided the files are available and readable. Large archives may take longer to process, so review them by year or in smaller batches. Use the year filter to focus on specific periods and watch for missing months or duplicate exports.