Analytics & Time Series
MariMap analytics combines:
- Field surveys (what you observed)
- Satellite and environmental context (what conditions were like)
Use analytics for QA/QC and reporting readiness, not just dashboards.
Common analytics workflows
- Validate coverage: did we sample all intended units?
- Spot-check anomalies: unexpected spikes or drops can reveal data entry errors or real events.
- Compare baselines and repeats: use consistent geometry and comparable windows.
- Prepare reporting: choose metrics aligned to the project’s intended outcomes and disclosure needs.
Choosing metrics
When selecting metrics, align with:
- Protocol (survey type)
- Ecosystem (reef/seagrass/mangrove/facility)
- Reporting audience (internal vs funder vs regulator)
- Claim stage (inputs vs verified outcomes)
Reference pages:
Reading time series
Practical tips:
- Start with a broad window (1Y or All) to understand baseline behavior.
- Use shorter windows (1M, 3M, YTD) to inspect recent events.
- If data is sparse, try a different aggregation (daily/weekly/monthly), if available.
Interpretation notes:
- Satellite metrics are sampled at the site geometry (or derived coastal bands). Bad geometry can produce misleading values.
- Condition categories and heat-stress thresholds are contextual indicators, not “outcomes”.
Troubleshooting
- No values shown: confirm a site is selected and it has valid geometry.
- Unexpected values: check site boundary placement and confirm the indicator source/definition.
- Field vs satellite mismatch: document assumptions in survey/report notes and avoid over-claiming.