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Patterns of Life

Overview​

Patterns of Life analyzes movement and communication patterns from mobile network data, such as CDR and IPDR records. You load activity for one or more phone numbers over a time range and review trajectories on an interactive map. The app outputs clustered locations, anomalies, home/work hotspots, and short-horizon movement predictions. Results are model-driven and require analyst review, especially when you treat locations as sensitive conclusions.

When to Use This Application​

  • You need to understand a subject’s routine and movement patterns over days or weeks.
  • You need to identify significant locations, such as likely home or workplace.
  • You need to detect unusual activity (time or location anomalies) for targeted follow-up.
  • You need to compare activity patterns by weekday and hour to spot routines and deviations.
  • You need short-horizon forecasts to support operational planning.

Before You Begin​

  • Ensure CDR/IPDR data is available and mapped so the app can query positions and activity.
  • Decide the phone number identifiers you want to analyze (for example MSISDN).
  • Decide the time range you want to load for analysis.

Step-by-Step Walkthrough​

Step 1 — Load targets and time range​

Select the phone number target(s) and a time range for analysis. After load, the app renders the activity on the map and populates the sidebar tabs.

Step 2 — Choose a playback mode​

Use the map in one of two modes:

  • Static mode: shows a snapshot around the current slider position.
  • Playing mode: animates forward from the current slider position to the end of the loaded range.

In static mode, drag the time slider to jump to any moment. In playing mode, pause to freeze on a frame or stop to reset.

Step 3 — Set the track bucket and preserve positions​

Configure how the map frames time and what points remain visible.

  • Track bucket
  • Sets the time window represented by each frame. Values range from 5 min up to 48 h. Smaller buckets show finer movement detail. Larger buckets condense activity into fewer frames. Default is 15 min.
  • Preserve positions
  • Controls whether the map shows only positions inside the current frame window, or accumulates positions over time.
    • Off: shows only positions inside the current window.
    • On: accumulates all positions from the start of the range up to the current time.

Step 4 — Tune analysis settings (optional)​

Open Analysis Settings to tune clustering, hotspot classification, and anomaly detection. Adjust these settings:

  • Work start / end hour (default 09:00–17:00)
  • Sleep start / end hour (default 22:00–06:00)
  • Clustering radius in km (default 0.5 km)
  • Min stay duration in hours (default 2 h)
  • Weekend days (default Saturday and Sunday) After you change settings, select Apply Settings to recompute results.

Step 5 — Use the analysis tabs​

Use the sidebar tabs to explore different results. All tabs respect your time range and weekday filters.

Summary tab​

Use Weekday filter to include or exclude specific days. Review activity patterns in:

  • Activity bar chart (drag a window to set a time range)
  • Activity timeline (online and call activity by weekday)
  • Activity heatmap (weekday × hour matrix) [CONFIRM UI LABEL: Summary] [CONFIRM UI LABEL: Weekday filter]

Tracks tab​

Use this tab to understand movement trajectories.

  • Review total distance statistics.
  • Review the cell tower transitions table for frequent routes.
  • Enable Most common path to draw the most frequent route as a red line with start/end markers.
  • Review the daily distance chart to spot unusual movement days.

Anomalies tab​

Use Anomaly threshold to control sensitivity. Review:

  • Anomaly count by MSISDN
  • Anomaly table (first 150 anomalies) with time, coordinates, score, and severity label

Select an anomaly from the table or the map to center the view and load details. You can then narrow the time range around that anomaly:

  • Apply filter: focuses on a window around the anomaly using the track bucket interval.
  • Clear filter: returns to the full view.

Predictions tab​

Use Prediction horizon to choose how many hours ahead to forecast after the last known position. Use Minimum confidence filter to hide low-confidence results.

Predictions appear as:

  • Purple markers on the map with hour offset and confidence
  • A prediction table with coordinates, time offset, and confidence score

Clusters tab​

Use this tab to identify frequently visited locations.

  • Adjust minimum cluster size to control how many points are required to form a cluster.
  • Review cluster circles on the map (sized by visit count).
  • Open cluster details to see centroid coordinates, visit count, and visit periods.

Hotspots tab​

Use this tab to classify recurring locations as home and work based on visit patterns.

  • Home location appears as a green house-shaped marker.
  • Work location appears as a blue briefcase-shaped marker.
  • Hotspot markers appear as distinctive drop-shaped icons.

Hotspot scoring uses the work/sleep hours and weekend days defined in Analysis Settings.

Understanding the Output​

Patterns of Life produces map-based and tabular outputs that you interpret together.

Map Playback​

Shows points and trajectories over time. Static mode gives a snapshot around the slider time. Playing mode animates forward.

Tracks​

Shows total distance traveled and common routes. Use these outputs to spot travel days that differ from the usual pattern.

Clusters​

Groups positions into frequently visited locations and estimates time spent. Larger cluster circles indicate higher visit count.

Hotspots (Home/Work)​

Labels the highest-scoring recurring clusters as home and work using configured work/sleep hours and weekend weighting.

Anomalies​

Lists points that break routine by location novelty, time rarity, or time-location novelty. Anomaly scores range up to 1.0.

  • Below 0.2: normal
  • Above 0.8: extremely anomalous

Predictions​

  • Displays forecast locations as purple markers and a table. Predictions include a confidence score. Use the minimum confidence filter to focus on reliable results.

Tips for Best Results​

  • Start with the default Track bucket (15 min), then increase it when you need a higher-level view.
  • Enable Preserve positions when you need a cumulative trail. Disable it when you need a clean frame-by-frame view.
  • Set work/sleep hours and weekend days to match the local context before you interpret home/work outputs.
  • Use Weekday filter to separate routine weekdays from weekend behavior.
  • Use the anomalies table to drive targeted review, then apply a narrow time window around a selected anomaly.
  • Use the Minimum confidence filter in predictions to avoid over-interpreting low-confidence forecasts.

Known Limitations​

  • CDR/IPDR locations are approximations based on network events. They do not represent GPS ground truth.
  • Home/work classification is heuristic and can be wrong for irregular schedules or sparse data.
  • Anomaly detection depends on the chosen threshold and on historical coverage. Short time ranges reduce reliability.
  • Predictions are probabilistic. Low confidence values indicate weak patterns and should not drive decisions without supporting evidence.