Aaag Data Analysis

Data Analysis (aaag-data-analysis)

The Annals expects analyses that are spatially honest and reported with uncertainty, whatever the area. The standard is that a competent reader in the area could follow the logic from data to claim and see that the geography of the data was respected, not flattened.

When to trigger

• Estimating models, running spatial statistics, classifying imagery, or coding qualitative material
• A reviewer questioned uncertainty, robustness, spatial autocorrelation, accuracy, or interpretation
• Preparing the results section and deciding what to report

Spatial / quantitative

• Diagnose space first. Report spatial autocorrelation in residuals; if present, move to a spatial model (lag/error, GWR/MGWR, spatial regimes) and say why.
• Uncertainty everywhere. CIs/SEs (spatially robust where needed), not stars alone; for prediction, out-of-sample error from spatial/blocked CV.
• Robustness. Re-estimate across plausible areal units and bandwidths (MAUP/scale sensitivity); show the result is not a unit artifact. Report effect sizes in interpretable units.

Remote sensing / physical

• Accuracy with an independent sample. Confusion matrix, overall/producer/user accuracy, kappa or F1; for continuous outputs, RMSE/MAE and bias; map the spatial pattern of error, not just a scalar.
• Propagate uncertainty from inputs through to the reported quantity; state the validation design.

Qualitative / interpretive

• Transparent analytic trail. Coding scheme, how themes were derived, and how interpretations were checked (negative cases, member checks, triangulation) — credibility over counting.
• Evidence-to-claim mapping. Each interpretive claim is tied to identifiable (anonymized) evidence; avoid quote-mining that over-generalizes from one informant.

Mixed methods

• Show the integration. State where the strands converge and where they conflict, and how the conflict was adjudicated — do not report two parallel analyses and call it mixed methods.

Cross-cutting reporting bar

• Match every claim in the text to an exhibit or statistic; no orphan assertions.
• Report negative / null / scale-dependent results honestly; geography rewards scope conditions.
• Keep analysis reproducible: master script, seeds, pinned versions (see aaag-transparency-and-data).

Referee pushback → Annals-specific fix

• "Are these effects just spatial autocorrelation?" → Show residual Moran's I before/after a spatial model; report the spatial-error structure, not only a global coefficient.
• "Would the result change at a different scale/unit?" → Provide a MAUP/bandwidth sensitivity panel and state the scale at which the claim holds.
• "How accurate is the map?" → Area-adjusted accuracy from an independent sample + a map of where error concentrates, not a single kappa.
• "How do I know the qualitative reading isn't cherry-picked?" → Coding scheme, negative cases, and an excerpt-to-claim table.

Calibration anchors

• Uncertainty is mandatory, not optional. A coefficient or accuracy number without an interval is not yet a finding at this venue.
• Scale dependence is a result, not a nuisance. If the answer changes with the unit, say so — that is geographic knowledge.
• The spatial pattern of error is itself a finding for remote-sensing and prediction work.

Checklist

• Spatial autocorrelation diagnosed and addressed (quant)
• Uncertainty reported (CIs/SEs; out-of-sample error via spatial CV where relevant)
• MAUP/scale or bandwidth sensitivity shown (quant)
• Accuracy via independent validation + spatial error map (RS)
• Coding scheme + evidence-to-claim trail (qual); integration shown (mixed)
• Every textual claim maps to an exhibit/statistic

Anti-patterns

• Reporting OLS on spatial data with no autocorrelation check
• Stars-only tables with no effect sizes or CIs
• A single global accuracy number with no spatial error map
• Cherry-picked quotes standing in for an analytic trail
• "Mixed methods" that never integrate the strands

Output format

【Mode】spatial-quant / remote-sensing / qualitative / mixed
【Headline result】effect/accuracy/theme + its uncertainty
【Spatial honesty】autocorrelation / MAUP / spatial-CV / spatial error map handled? [Y/N]
【Robustness】checks run and what held
【Reproducibility】master script + seeds + versions? [Y/N]
【Next】aaag-tables-figures

Supplementary resources

• ../../resources/external_tools.md — spatial-stats, RS, and qualitative-analysis packages
• ../../resources/README.md — shared reporting-standards background (inference, robustness)

Source: brycewang-stanford/Awesome-Journal-Skills → Annals-of-the-American-Association-of-Geographers-Skills/skills/aaag-data-analysis/SKILL.md