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Aaag data analysis

Skill brycewang-stanford/Awesome-Journal-Skills/Annals-of-the-American-Association-of-Geographers-Skills/skills/aaag-data-analysis

Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的 Claude Code/Codex 期刊技能包，从选题、识别策略到表格规范与审稿回复全流程，助你快速发论文。

Install
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill aaag-data-analysis

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What its author says it does

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Use when running and reporting the analysis for an Annals of the American Association of Geographers manuscript — spatial statistics and modeling, remote-sensing accuracy, or qualitative coding and interpretation. Sets analysis and reporting norms across the four areas; it does not choose the design.

SKILL.md

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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

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