camera-trap-processing

To create detection histories for occupancy models, transform raw camera trap detections into independent events and site × occasion matrices. This process applies independence thresholds and estimates trap effort to generate model-ready outputs.

Filtering independent events from camera trap CSV outputs requires applying configurable time thresholds to raw image records. This removes duplicate detections, creating cleaned event datasets suitable for ecological activity studies and abundance analysis.

Yes, you can estimate camera trap effort across multiple stations using Python libraries like pandas and numpy. The processing scripts read CSV outputs to calculate deployment timelines and generate effort checks for each location.

Converting raw image records into site × occasion matrices involves applying independence thresholds to raw detections. This generates production-ready detection histories structured for direct input into occupancy models and abundance analyses.

Yes, this data processing approach supports activity analysis alongside occupancy modeling. By generating cleaned independent events and simple per-species summaries, the workflow outputs production-ready data for both activity studies and occupancy models.