Faster Insights: qualitative analysis of wetland conversion

Evidano is an AI-powered qualitative data analysis platform that speeds transcript workflows, supports AI-assisted coding, enables cross-segment analysis, and provides secure handling for sensitive research data.

The PLOS ONE study (published July 2, 2026) documents about 3, 355 ha converted and a 48.8% wetland loss (1985–2021), and finds drivers including distribution to landless youth and institutional incentives.

• High-confidence Landsat classification in the study: overall accuracy 97.27% and Kappa 87.19% for 2021, enabling reliable mapping of wetland-to-cropland change.
• Social data scale: household survey n = 396 (Mar–May 2022), experts n = 75, and 12 KIIs provide the qualitative evidence that complements LULC change.
• Use a reproducible pipeline (transcription → AI-assisted coding → cross-segment analysis → exports) to shorten synthesis time while preserving audit trails for peer review.

Fast take: The PLOS ONE paper (published July 2, 2026) found 3, 355 ha of wetlands converted to farmland in three sites (Kotlan, Foket, Wadera) and reports drivers including distribution of wetlands to landless youth and institutional incentives.

• Why it matters: high-accuracy LULC (overall accuracy ≥95%, 2021 Kappa ~87%) plus rich qualitative data equals policy-ready recommendations if synthesis is rigorous.
• Payoff for researchers: replicate the study’s mixed-method inference faster and with reproducible coding, quote management, and cross-segment testing.
• Full study: PLOS ONE.

This section explains how the authors combined high-quality Landsat composites and a multi-source social dataset to infer drivers of wetland conversion.

The authors merged Landsat composites (1985, 1995, 2000/2010, 2021) and supervised classification with a multi-source social dataset: a face-to-face household survey (n = 396), a 75-person experts survey, and 12 audio-recorded KIIs.

• Remote sensing: Google Earth Engine plus ArcGIS, maximum likelihood classification, accuracy assessment via confusion matrix.
• Surveys: translated Amharic questionnaires, SPSS for descriptives; STATA v18 for ordered probit.
• Qualitative: audio to transcription to thematic coding with smallest meaningful units (manual thematic analysis).

This section lists a compact, reproducible 7-step pipeline to get from raw files to policy-ready findings.

• 1) Collect and version files: Landsat outputs (geotiffs), survey spreadsheets, KII audio. Store originals and working copies.
• 2) Transcribe and translate KIIs in Evidano, using a custom dictionary for local terms (for example, kebele, cheffee).
• 3) Import survey sheet, harmonize respondent IDs to link transcripts to survey rows.
• 4) Auto-suggest codes in Evidano, then create a verified codebook reflecting provisioning, regulating and cultural services and drivers (land redistribution, youth employment).
• 5) Run thematic frequency and co-occurrence analyses, slice by segment (age, landholding, office).
• 6) Export coded quotations and cross-tab tables for ordered probit replication in STATA; produce a visual brief (map plus top themes plus representative quotes).
• 7) Package an executive brief and a reproducible appendix (codebook, exportable CSVs) for stakeholders.

Wrapping up: You can cut synthesis time while keeping rigor by combining reproducible transcript workflows, AI-assisted coding, and cross-segment analytics for mixed-methods land-use studies like the Bure/Womberma paper.

• Ready to try it: upload a small batch (3–5 KIIs plus one survey CSV) into Evidano to auto-transcribe, generate candidate themes, and produce a 2-page stakeholder brief in under a day.
• Try Evidano for free to run a pilot, keep your data encrypted and research-only, link qualitative themes to LULC change, and make policy recommendations that stand up to peer review.