23 March 2020 / product

Location history of app users as a timeline

Mobile phones have powerful sensors to log detailed information about your app users' movements with their permission. Replaying the location time-series as a polyline on the map makes the journey come to life. Splitting the polyline into activity segments helps understand the key moments in the journey. Augmenting the activity segments with cool data like steps, distance and addresses gives useful context about the journey. In case the device could not be tracked for a period of time, seeing those outage segments in the timeline with precise reasons and locations makes it actionable to handle exceptions.

Comparison with Google Maps timeline

Some of you might be familiar with the timeline feature in our Google Maps app (requires always on location access). We compared the HyperTrack timeline with Google Maps on the same device.

HyperTrack timeline versus Google Maps timeline

HyperTrack had far more granular and accurate data compared to Google. In the side-by-side screen recording above, Google Maps recorded a 14 minute drive of 2.5 miles with relatively straight lines. HyperTrack recorded the same drive as a 16 minute drive of 3.8 miles with higher location accuracy. The actual distance for the drive is accurate with HyperTrack and inaccurate by 30% with Google Maps.

Add timeline tracking to any app

While Google Maps timeline is available only through Google Maps app, HyperTrack is an SDK that may be added to any app. Tracking controls are available through the server, and rely on explicit permissions from app users to protect user privacy.

HyperTrack provides a detailed breakdown of the user's day including:

• Stops with addresses and steps during the stop
• Drives with accurate map-matched distances
• Walks with steps and distance
• Tracking outages (when we were not able to track the user)

With the HyperTrack SDK in your app, we harvest data from sensors like GPS and accelerometer through APIs made available by the phone Operating Systems. This data is securely ingested into the HyperTrack platform. On the platform, we apply clustering algorithms and heuristics to understand device movement, and generate activity and outage markers in our timeline summary.

Timeline for business on the move

The HyperTrack timeline gives you a deeper understanding of your business on the move. Dispute resolution, customer support escalations, expense management, timesheet automation, productivity and asset utilization are some use cases that users have implemented with the HyperTrack timeline. We track where and how your assets have spent their work day, and let you analyze and optimize your daily operations.

By nature, we track geographically distributed devices on phones controlled by your users. Even though we apply numerous techniques to ensure we track devices, there are circumstances that prevent us from tracking. Tracking outages happen when location permission is denied, the app is deleted or killed, the phone is switched off or rebooted, the phone enters power saver mode due to low battery, and so on. In case this happens, the timeline shows you an outage with information when it started and ended, along with the outage reason. This gives you actionable insights about how to correct this.

Integration with your application

HyperTrack timeline is available as web views that may be embedded in your web dashboards with useful customizations. Users may attach custom markers to the timeline through the app, or create trips to track specific journeys to a destination or geofences. Timeline views are available for individual trips as well.

Like all HyperTrack features, the timeline data is available through APIs. Separate API endpoints are available for devices and trips.

To get the data for a device, call the Device History REST API:

curl \
  -u {AccountId}:{SecretKey} \
  https://v3.api.hypertrack.com/devices/123E4567-E89B-12D3-A456-426655440000
/history/2020-02-02

And you will get receive a JSON object with a list of device_status markers that directly correspond to the timeline in our web views. See for example this snippet:

  ...
  "markers": [
    {
      "type": "device_status",
      "data": {
        "start": {
          "location": {
            "geometry": {
              "coordinates": [
                -123.4567,
                38.91234
              ]
            },
            "recorded_at": "2020-02-20T00:00:00.000Z"
          },
          "recorded_at": "2020-02-20T00:00:00.000Z"
        },
        "end": {
          "location": {
            "geometry": {
              "coordinates": [
                -123.4567,
                38.91234
              ]
            },
            "recorded_at": "2020-02-20T02:00:00.000Z"
          },
          "recorded_at": "2020-02-20T02:00:00.000Z"
        },
        "value": "active",
        "activity": "stop",
        "steps": 1491,
        "duration": 7200,
        "address": "123 Main St, Springfield, USA"
      }
    }
 ]
 ...

Experience it yourself

Product development teams around the world are building awesome applications and use cases with this rich movement data. To experience it yourself, sign up for a HyperTrack account, get the HyperTrack Live app and see your timeline in the HyperTrack dashboard. We would love to hear about your experience, and help you bring your ideas to life with this data.

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Thomas Raffetseder

Software Architect