Agent Skills
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aaag-data-analysis
GitHub为《美国地理学家协会年鉴》稿件提供数据分析与报告规范，涵盖空间统计、遥感精度及质性编码。强调空间诚实性、不确定性量化、鲁棒性检验及混合方法整合，确保分析逻辑可追溯且结果透明可信。
Trigger Scenarios
执行模型估计、空间统计、图像分类或质性材料编码
审稿人质疑不确定性、稳健性、空间自相关、精度或解释
撰写结果部分并决定报告内容
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aaag-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "aaag-data-analysis",
"description": "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."
}
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)
Version History
- 1839142 Current 2026-07-05 12:23
