spatial-analysis

name	spatial-analysis
description	Guideline-driven spatial analysis skill. Given a research question and data context, provides decision frameworks for selecting appropriate spatial methods, diagnostics, and interpretation strategies. Adapts to available data, spatial units, and analytical objectives — Claude Code determines the optimal workflow. Use when user says "spatial analysis", "analyze spatial data", "run spatial regression", "check for clustering", "map this", or needs to go from a research question to a complete spatial analysis.
argument-hint	["research-question-or-analysis-goal"]
allowed-tools	Bash(*), Read, Write, Edit, Grep, Glob, Agent

Spatial Analysis: Guideline-Driven Decision Framework

Analyze: $ARGUMENTS

Purpose

This skill provides guidelines, decision tables, and guardrails for spatial analysis — not a fixed procedure. The sequence and combination of methods should be determined by Claude Code based on:

1. The research question and its analytical objective
2. The available data (type, size, quality, spatial unit)
3. The study context (domain, audience, publication target)

Read skills/knowledge/spatial-methods.md for reference implementations. This skill decides when, why, and under what conditions to use them.

Constants

• OUTPUT_DIR = output/spatial-analysis — Default destination for all analysis artifacts.
• MAX_FEATURES = 15 — Soft cap on predictors before recommending dimensionality reduction.
• MGWR_MAX_N = 3000 — Subsample threshold for MGWR.
• GWR_MAX_N = 5000 — Subsample threshold for GWR.
• SIGNIFICANCE_LEVEL = 0.05 — Default alpha unless user specifies otherwise.
• SPATIAL_CV_FOLDS = 5 — Default spatial cross-validation folds.

Override via argument, e.g., /spatial-analysis "question" — significance: 0.01, max features: 8.

---

1. Research Question Classification

Before selecting any method, classify the research question. This classification drives every downstream decision.

1.1 Analytical Objective Mapping

Objective	Signal phrases	Typical method families
Description	"what is the spatial pattern of...", "how is X distributed"	Choropleth, KDE, summary statistics, ESDA
Explanation	"what factors explain...", "why does X vary across..."	Regression ladder (OLS → spatial → local)
Comparison	"does X differ between regions..."	Stratified analysis, interaction terms, regional subsetting
Prediction	"can we predict...", "where will X occur..."	ML + spatial CV, feature engineering
Clustering / hot spots	"are events clustered...", "where are hot spots..."	Moran's I, LISA, Getis-Ord Gi*, DBSCAN
Association	"is X related to Y spatially..."	Bivariate Moran's I, spatial regression
Accessibility	"who has access to...", "what areas are underserved..."	Network analysis, 2SFCA, isochrones
Temporal-spatial change	"how has X changed over time across space..."	Panel methods, spatiotemporal aggregation
Causal inference	"does X cause Y..."	Spatial DiD, IV, RDD — flag limitations explicitly
Interpolation	"what is the value at unsampled locations..."	Kriging, IDW, cross-validation

If the question maps to multiple objectives, identify the primary one and treat others as supporting. If the question is too vague, ask the user to clarify the outcome, spatial unit, and study area.

1.2 Should Spatial Methods Be Used at All?

Not every geographic dataset requires spatial statistics. Evaluate:

Question	If "no"	If "yes"
Is spatial dependence theoretically plausible?	Standard methods may suffice	Spatial structure likely matters
Is the spatial structure itself the research question?	Spatial methods are optional	Spatial methods are mandatory
Would ignoring spatial structure bias results?	OLS may be adequate	Spatial adjustment needed

Guideline: If all three are "no", recommend non-spatial analysis and explain why. Do not force spatial methods where they add no value.

1.3 Write Question Classification

Save classification to output/spatial-analysis/question_classification.md:

# Research Question Classification

**Question**: [user's question]
**Date**: [today]

## Analytical Objective
- Primary: [objective type]
- Supporting: [if any]

## Key Components
- Outcome variable: [or N/A]
- Explanatory variables: [list or TBD]
- Spatial unit: [point / polygon / raster / network]
- Temporal structure: [cross-sectional / panel / time series]
- Study extent: [description]
- Estimated N: [if known]

## Spatial Methods Needed?
- [Yes / No / Conditional on diagnostics]
- Reasoning: [why]

## Method Candidates
1. [method — why it fits this question]
2. [method — why it fits this question]
3. [method — conditional on X]

---

2. Data Readiness Guidelines

These guidelines apply regardless of analytical objective. Evaluate data readiness before proceeding to any analysis. The depth of preparation depends on the situation.

2.1 CRS Decision Framework

This is non-negotiable. Wrong CRS invalidates distance, area, and density calculations.

If the analysis involves...	CRS requirement	Recommendation
Distance or density calculations	Projected (meters)	Local UTM or national grid
Area calculations	Equal-area projection	Albers, Mollweide, or national equal-area
Only display / web mapping	Any (geographic OK)	WGS84 (EPSG:4326)
Spatial joins / overlays	Both layers must match	Reproject to analysis CRS first
Mixed (distance + display)	Analyze in projected, display in geographic	Two CRS in workflow

Guardrail: If data is in EPSG:4326 and the analysis involves distances, areas, or density — project first. Never compute Euclidean distance on lat/lon.

UTM zone estimation (when no local CRS is obvious):

centroid = gdf.geometry.unary_union.centroid
utm_zone = int((centroid.x + 180) / 6) + 1
hemisphere = 'north' if centroid.y >= 0 else 'south'
epsg = 32600 + utm_zone if hemisphere == 'north' else 32700 + utm_zone

2.2 Data Quality Checklist

Evaluate these as needed — not all checks apply to every dataset:

Issue	When to check	How to handle
Missing CRS	Always	Ask user or infer from coordinate range
Invalid geometries	Before spatial operations	gdf.geometry.buffer(0) to fix
Duplicated geometries	Before spatial statistics	Investigate context before dropping
Multipart features	When analysis requires single-part	gdf.explode()
Empty geometries	Always	Drop or investigate
Missing values	Always	Document spatial pattern of missingness — clustered missingness biases spatial statistics

2.3 Multi-Dataset Integration

When combining datasets, address these issues in the order they arise:

Issue	Guideline
CRS mismatch	Reproject all layers to a common analysis CRS before any spatial operation
Temporal mismatch	Document the assumption that spatial patterns are stable over the time gap; flag if gap > 2 years
Resolution mismatch (MAUP)	Document aggregation/disaggregation method; warn about ecological fallacy; consider sensitivity analysis at different scales
Boundary mismatch	Use areal interpolation if administrative boundaries don't align

2.4 Variable Preparation Guidelines

Situation	Guideline
Suspected outliers	Inspect in spatial context — an "outlier" may be a real local phenomenon. Do NOT auto-remove
Skewed distributions	Log-transform only if theoretically justified AND skewness > |2|
Many predictors (> MAX_FEATURES)	Apply domain-driven selection or dimensionality reduction before modeling
Multicollinearity (VIF > 10)	Drop or combine correlated predictors
Preparing for GWR/MGWR	Standardize predictors (mean=0, std=1) so bandwidths are comparable

---

3. Spatial Weights Selection Guide

Spatial weights underpin most spatial statistics. The choice is consequential and must be justified.

Data type	Recommended weights	Rationale
Regular polygon tessellation (counties, tracts)	Queen contiguity	Captures all adjacency relationships
Grid-like polygons	Rook contiguity	Corner adjacency often not meaningful
Point data	KNN (k=5–8)	Adapts to varying point density
Irregular polygon sizes	KNN or distance band	Contiguity unreliable with very different sizes
Interaction decays with distance	Distance band	Captures distance-decay process

Guardrails:

• Always check for islands: W.islands. Islands break spatial statistics — add manual connections or switch to KNN.
• Always row-standardize: W.transform = 'R'.
• Document your choice and reasoning. If results are sensitive to weights choice, report this.

---

4. Analytical Approach Guidelines

Select your approach based on the analytical objective from Section 1. These are not sequential steps — choose the relevant section(s) and adapt.

4.1 Exploratory Spatial Data Analysis (ESDA)

When to use: Almost always — ESDA should precede formal modeling in most situations. Skip only if the question is purely about accessibility/network analysis with no distributional component.

What to include depends on the question:

If the question is about...	ESDA should include
Spatial patterns or distribution	Choropleth/KDE maps, global Moran's I, LISA or Gi*
Regression / explanation	Distribution of outcome + predictors, correlation matrix, global Moran's I to determine if spatial modeling is needed
Prediction	Feature distributions, spatial autocorrelation of target variable, visual inspection for spatial structure
Clustering	Global clustering test first (Moran's I or General G), then local tests

Map classification guidance:

Data distribution	Best classification scheme
Roughly uniform	equal_interval
Skewed (common)	quantiles (equal-count bins)
Multimodal or natural groupings	natural_breaks or fisher_jenks
Need to highlight deviation from mean	std_mean (only if roughly normal)

Color scheme rules:

Variable type	Use	Never use
Sequential (counts, rates)	viridis, YlOrRd, Blues	rainbow/jet
Diverging (residuals, change)	RdBu_r, coolwarm, PiYG	sequential colormap
Categorical (clusters)	Set2, tab10	continuous colormap

Global Moran's I interpretation:

Result	Implication
p < 0.01	Strong spatial autocorrelation — spatial methods likely needed
0.01 ≤ p < 0.05	Moderate — spatial methods recommended; compare with non-spatial
p ≥ 0.05	Not significant — non-spatial methods may suffice; still check residuals after modeling

Local pattern detection — LISA vs Getis-Ord Gi:*

Use LISA when	Use Gi* when
You care about both clusters AND spatial outliers (HH, LL, HL, LH)	You only care about hot spots and cold spots
You want to identify areas that deviate from neighbors	You want to identify concentration of extreme values

Guardrail: Multiple testing — with N spatial units, you run N local tests. Report the number of significant clusters and note potential false positives. Consider Bonferroni or FDR correction.

4.2 Explanatory Analysis (Regression)

When to use: The research question asks why a spatial pattern exists or what factors explain spatial variation.

Decision framework — choose the model based on diagnostics, not assumption:

Start with OLS baseline (always)
    │
    ├── Check residual Moran's I
    │   ├── p ≥ 0.05 → OLS is adequate. Report and stop.
    │   └── p < 0.05 → Spatial dependence in residuals. Continue below.
    │
    ├── Determine the nature of spatial dependence:
    │   ├── Substantive (spillover: outcome in i depends on neighbors)
    │   │   → Spatial Lag Model
    │   │   Example: crime spillover, housing price contagion
    │   │
    │   ├── Nuisance (unobserved spatially-structured factors)
    │   │   → Spatial Error Model
    │   │   Example: unmeasured soil quality, regional culture
    │   │
    │   └── Both LM tests significant → Use Robust LM tests
    │       ├── Only Robust LM-Lag remains significant → Spatial Lag
    │       ├── Only Robust LM-Error remains significant → Spatial Error
    │       └── Both remain significant → Spatial Durbin Model
    │
    ├── Do relationships plausibly vary across space?
    │   ├── Yes AND theoretical justification exists
    │   │   ├── N ≤ MGWR_MAX_N → MGWR (preferred — per-variable bandwidth)
    │   │   ├── N ≤ GWR_MAX_N → GWR
    │   │   └── N > GWR_MAX_N → Spatially stratified subsample, or regional submodels
    │   │
    │   └── No theoretical reason → Do NOT run GWR/MGWR
    │
    └── Compare all fitted models: AICc, R², residual Moran's I
        └── Report best model with full diagnostics

OLS diagnostics to check:

• R², Adjusted R², RMSE, MAE, AIC/BIC
• Residual Moran's I (spatial autocorrelation)
• Breusch-Pagan (heteroskedasticity)
• Jarque-Bera (residual normality)
• VIF (multicollinearity), Condition number (< 30 preferred)

GWR/MGWR guardrails:

• Only run when there is a theoretical reason to expect spatially varying relationships
• Always standardize predictors first
• Coordinates must be in projected CRS
• Interpret bandwidths: < 50 neighbors = local process; 50–200 = regional; > n/3 = effectively global

Model comparison table (always produce when multiple models are run):

| Model | R² | Adj. R² | AICc | RMSE | Residual Moran's I | p(Moran) |
|-------|-----|---------|------|------|--------------------|----------|

Selection logic: (1) Best AICc with > 2 difference being meaningful. (2) Residual Moran's I closest to 0. (3) If AICc and Moran's I disagree, prefer the model that resolves spatial autocorrelation. (4) Prefer simpler model when differences are marginal.

4.3 Clustering and Hot Spot Detection

When to use: The question asks whether or where spatial clustering exists.

Decision framework:

Situation	Approach
Testing for global clustering	Global Moran's I (spatial autocorrelation) or Getis-Ord General G (concentration of high/low values)
Locating specific clusters	LISA (clusters + outliers) or Gi* (hot/cold spots only)
Detecting clusters without predefined weights	DBSCAN or other density-based methods
Analyzing event/count data	Normalize by population at risk first — raw counts cluster where people live

Guardrail: If analyzing event counts (disease cases, crime incidents), always normalize by population at risk or use standardized rates. Raw count clusters reflect population density, not elevated risk.

4.4 Prediction

When to use: The goal is to estimate values at locations where the outcome is unknown.

Key guidelines:

Guideline	Rationale
Include spatial features	Coordinates, distance to landmarks, spatial lag of predictors, neighborhood summaries — these capture spatial structure
Use spatial cross-validation, NEVER random CV	Random CV leaks spatial autocorrelation and overestimates predictive accuracy
Compare models by spatial CV performance	Not in-sample fit
Check residual Moran's I even for ML models	Remaining spatial structure means the model misses a spatial predictor or process

Spatial CV approaches:

• Grid-based blocks (simple, may be imbalanced)
• K-means clustering on coordinates (more balanced folds)
• Buffer-based exclusion (strongest protection against leakage)

Choose based on the spatial structure of the data and the prediction task.

4.5 Accessibility and Network Analysis

When to use: The question concerns reachability, service coverage, or spatial access to facilities.

Guideline: Only invoke network analysis when the research question specifically requires it. Do not add network analysis to a regression workflow just because spatial data is involved.

Common approaches: Street network analysis (OSMnx + NetworkX), isochrone construction, 2-step floating catchment area (2SFCA), service area delineation.

4.6 Interpolation

When to use: Estimating values at unsampled locations from point observations, assuming spatial continuity.

Key guidelines:

Guideline	Rationale
Always inspect the variogram first	The variogram reveals the spatial structure; fitting without inspection is reckless
Cross-validate to choose method	Compare Kriging variants, IDW, etc. by leave-one-out or k-fold spatial CV
Do NOT interpolate categorical variables	Interpolation assumes spatial continuity — categorical data is not continuous
Distinguish from regression-based prediction	Interpolation leverages spatial proximity, not covariates

4.7 Temporal-Spatial Analysis

When to use: The question involves change over time across space.

Guideline: Choose approach based on data structure:

Data structure	Approach
Repeated cross-sections (same areas, multiple time points)	Panel methods, fixed/random effects with spatial terms
Two time points	Change analysis, spatial pattern of change
Continuous time series at fixed locations	Spatiotemporal modeling, temporal faceting
Irregular temporal observations	Aggregate to consistent time windows first; document the choice

---

5. Diagnostics and Robustness Guidelines

Apply diagnostics proportional to the complexity of the analysis and the stakes of the conclusions. Diagnostics are a menu, not a checklist. Pick only the ones that are relevant to the research question, the chosen method, and the data — running every spatial diagnostic on every project is wasteful and frequently misleading.

5.1 Core Diagnostics (apply only when the trigger is met)

Diagnostic	Apply ONLY when	Skip when
Residual Moran's I	A regression / ML model is fit AND spatial dependence is theoretically plausible AND inference depends on residual independence	Question is purely descriptive, predictive on i.i.d. data, or non-spatial; or the unit of analysis has no plausible neighborhood structure
Breusch-Pagan	Regression with formal inference on coefficients	Predictive-only modeling, ML pipelines reported by CV error
Jarque-Bera	Regression where you rely on parametric inference	Robust / nonparametric / large-N CLT cases
VIF	Multiple regression with multiple plausibly-correlated predictors	Single predictor; orthogonal-by-design features
Cook's distance	Regression where leverage of individual observations could flip a substantive conclusion	Large N where single points cannot dominate
AICc comparison	Multiple competing nested or comparable models are fit	Single chosen model justified a priori
Spatial CV metrics	Prediction task on spatially structured data where leakage is plausible	Non-spatial prediction; spatial structure already removed by design (e.g., independent draws)

If unsure whether a diagnostic is necessary, default to asking the user rather than running it. See Section 5.3.

5.2 Robustness Checks — Apply When Conclusions Are Sensitive

Check	Apply ONLY when	Skip when
Alternative spatial weights	The headline claim is a clustering result or spatial regression coefficient that could plausibly flip under a different W	Pure description, prediction by CV error, or W has no causal role in the claim
Alternative spatial scale (MAUP)	The unit of aggregation was a researcher choice and the conclusion is about magnitude, ranking, or causation across units	Unit is fixed by the data-generating process; question is at a single scale by design; result is about presence/absence rather than magnitude
Boundary effects	Study area has hard administrative or natural boundaries AND inference relies on neighborhood-based statistics near those edges	Question is interior-only or edge units are excluded a priori
Temporal mismatch sensitivity	Combining datasets from different years AND the spatial pattern is plausibly non-stationary over that gap	Single time slice; gap < 1 year; pattern known to be stable
Subset analysis	Study area is heterogeneous AND a regional effect is plausible	Homogeneous area or N too small to subset reliably

Guideline: Report robustness checks that you performed AND explicitly list checks you considered but skipped, with the reason (e.g., "MAUP not assessed — unit of analysis is the individual sensor reading, not aggregated"). It is acceptable — and often correct — to skip MAUP, GWR, alternative weights, or spatial CV when the research question does not depend on them.

5.3 Human Checkpoint — Adding or Skipping Spatial Checks

Geospatial diagnostics (Moran's I on residuals, MAUP sensitivity, GWR/MGWR, alternative spatial weights, spatial CV, LISA / Gi*) are powerful but not universally required. Apply them only when the research question genuinely depends on them. When in doubt, PAUSE and ask the user rather than running them by reflex.

Honor the HUMAN_CHECKPOINT flag in CLAUDE.md (default: true). When true, request explicit user approval before either of the following; when false, log the decision (and reasoning) to output/PROJ_NOTES.md and the Diagnostics and Robustness section of the report and proceed.

Trigger	Show before pausing
About to add a heavyweight spatial check that the question may not need (GWR/MGWR, MAUP sensitivity sweep, alternative-W sweep, spatial CV when the task is not predictive on spatially structured data)	Which check, why it might be relevant, the cost (time / compute / interpretive load), the simpler alternative, and a one-line recommendation
About to skip a spatial check that a strict GIScience reviewer would expect (e.g., regression on aggregated areal data with no MAUP discussion, spatial regression with no residual Moran's I)	Which check, why this question / dataset arguably does not need it, and the explicit caveat that will go into the report

Default rule: prefer the lightest analysis that answers the question. If a choropleth and summary statistics answer it, do not run MGWR; if the question is non-spatial in substance even though the data have coordinates, do not force spatial methods.

---

6. Visualization Guidelines

6.1 Map Requirements

Every map must include: title, legend with units and classification scheme, scale bar, CRS in caption, consistent color scheme across related maps. Add north arrow and source attribution if publication conventions require them.

6.2 Which Plots to Include

Choose based on what was analyzed — do not produce plots that add no information:

Plot type	Include when
Choropleth / KDE map	Describing spatial distribution (almost always)
Residual map	Any regression model was fit
LISA cluster map	Local clustering was detected
Gi* hot/cold spot map	Hot spot analysis was performed
Coefficient surface map	GWR/MGWR was run and coefficients vary meaningfully
Local R² map	GWR/MGWR and local fit varies
Moran scatter plot	Reporting Moran's I (visual complement)
QQ plot of residuals	Regression diagnostics, normality in question
Model comparison bar chart	Multiple models compared

---

7. Interpretation and Reporting

7.1 Report Structure

Write to output/spatial-analysis/analysis_report.md:

# Spatial Analysis Report

**Research Question**: [question]
**Date**: [today]
**Data**: [description — N, spatial extent, time period]

## Data and Study Area
[Data sources, spatial units, sample size, key variables, CRS used]

## Exploratory Findings
[Distribution of outcome, initial spatial patterns, global Moran's I result if computed]

## Analytical Results
[Method(s) used and why, model comparison if applicable, key findings]

## Diagnostics and Robustness
[Residual checks, sensitivity analysis, caveats]

## Implications
[What the results mean for the research question, limitations, next steps]

Adapt the depth to the complexity of the analysis. A descriptive analysis needs 2–3 paragraphs. A full regression comparison may need 5–6. Do not pad simple analyses with unnecessary sections.

7.2 Interpretation Guardrails

Guardrail	Rationale
Report effect sizes, not just significance	p-values alone are uninformative about practical importance
Never claim causality from cross-sectional observational data without explicit justification	Spatial association ≠ causation
Acknowledge MAUP when results could plausibly depend on the unit of aggregation	Results at county level may not hold at census tract level — but a fixed-unit study (e.g., per-sensor measurements) does not need a MAUP statement
Note boundary effects when the analysis relies on neighborhood statistics near hard edges	Fewer neighbors = less reliable local statistics; skip if interior-only
Distinguish statistical significance from substantive importance	A Moran's I of 0.02 with p < 0.01 is significant but trivially small

---

7.5 Human Checkpoint — Data Synthesis

Honor the HUMAN_CHECKPOINT flag in CLAUDE.md (default: true). Spatial analysis frequently creates derived layers (interpolated surfaces, predicted maps, simulated permutations, areal-interpolated reaggregations) that downstream skills may treat as observations. When HUMAN_CHECKPOINT is true, PAUSE and request explicit user approval before any of the following; when false, log the decision to output/PROJ_NOTES.md and the analysis report's Diagnostics and Robustness section, and proceed.

Trigger	Show before pausing
Producing an interpolated surface (Kriging, IDW, regression-Kriging) that will be saved to output/spatial-analysis/ and may be reused as input by another skill	Method, variogram model, search neighborhood, CV error, and whether downstream skills should treat outputs as observations or as model predictions
Producing GWR/MGWR coefficient surfaces or local R² maps that will be exported as data (not just figures)	Bandwidth choice, standardization, and a warning that local estimates are not independent observations
Areal interpolation between non-aligned boundaries (e.g., reallocating block-group counts to a custom grid)	Source / target geometries, the weighting variable (population, area, dasymetric), and the MAUP risk introduced
Imputing missing values for any variable that enters a regression or clustering result reported in analysis_report.md	Imputation method, share of values imputed, and a sensitivity analysis plan (or explicit decision to skip it)
Permutation / Monte Carlo runs whose realizations will be persisted (not just summarized)	N permutations, seed, what each realization represents, and how it will be cited in downstream artifacts
Pseudo-absences / random-background points generated for a presence-only model	Sampling region, density, exclusion rules, seed, and circularity risk if presence and pseudo-absence share predictors
Any number entering the report that was not produced by an executed code cell logged in output/spatial-analysis/scripts/	The exact source and why it is acceptable evidence

Synthesized layers must be saved with a .meta.json sidecar marking synthetic_or_derived: true, recording the recipe, and noting Synthesis approved by user: YYYY-MM-DD.

---

8. Guardrails Summary

These are mistakes this skill is designed to prevent. Claude Code should internalize these as hard constraints:

Mistake	Prevention
Using lat/lon for distance calculations	CRS guidelines require projection for any distance/area/density analysis
Applying Moran's I without thoughtful weights choice	Weights selection guide requires explicit justification
Interpreting raw count clusters as rate clusters	Clustering guidelines mandate normalizing by population at risk
Running GWR/MGWR by default	Regression guidelines require theoretical justification — and Section 5.3 requires user approval before adding it
Forcing MAUP / GWR / spatial CV on questions that do not need them	Section 1.2, Section 5, and Section 5.3 make these conditional, not mandatory; skipping with documented reasoning is a valid outcome
Random CV on spatial data when the prediction task is spatial	Prediction guidelines mandate spatial CV in that case; non-spatial prediction tasks remain free to use standard CV
Reporting OLS when residuals are spatially autocorrelated and inference depends on residual independence	Regression decision framework escalates to a spatial model in that specific case
Misleading map classification	ESDA guidelines match classification scheme to data distribution
Rainbow/jet colormaps	Color scheme rules explicitly ban them
Overclaiming causality	Objective mapping distinguishes explanation from causal inference
Mixing incompatible spatial resolutions silently	Data integration guidelines require documenting resolution mismatch
Overcomplicating when simple methods suffice	Question classification checks whether spatial methods are even needed

---

9. Outputs

• output/spatial-analysis/question_classification.md — Research question classification and method candidates
• output/spatial-analysis/analysis_report.md — Analysis narrative (depth proportional to complexity)
• output/spatial-analysis/model_comparison.md — Model comparison table (if regression)
• output/spatial-analysis/scripts/ — Python scripts used (for reproducibility)
• output/spatial-analysis/figures/ — Maps and diagnostic plots
• output/PROJ_NOTES.md — One-line findings appended
• output/results/ — JSON results (if feeding into geo-experiment pipeline)

---

10. Key Principles

• Research question first. Never start with a method. The question determines everything.
• Justify every method choice. If you cannot explain why a method is needed for this question, do not use it.
• Parsimonious workflows. If a choropleth and summary statistics answer the question, do not run MGWR.
• Geospatial checks are conditional, not mandatory. MAUP, GWR/MGWR, alternative-W sweeps, spatial CV, residual Moran's I all require a triggering reason from Section 5. When unclear, ask the user (Section 5.3) instead of running them by default.
• Adapt to the data. The guidelines above are decision frameworks, not checklists. Skip what doesn't apply; go deeper where the data demands it.
• Honest interpretation. Report what the analysis actually shows, including null results and limitations.
• Reproducibility. Save scripts, log parameter choices, document CRS decisions.
• Do not fabricate results. Only report numbers from executed code.
• Respect computational limits. GWR ≤ 5,000 obs; MGWR ≤ 3,000. Subsample spatially if needed.
• Large file handling: If the Write tool fails due to file size, retry using Bash (cat << 'EOF' > file). Do not ask permission — just do it.

---

11. Composing with Other Skills

/lit-review "spatial topic"       → literature context
/generate-idea "spatial direction" → research ideas
/refine-research "spatial problem" → method refinement
/spatial-analysis "research question" ← you are here
/geo-experiment                    → formal experiment execution with sprint contracts
/result-to-claim                   → validate claims against actual results
/auto-review-loop                  → adversarial review of the analysis
/paper-figure                      → publication-quality figures
/paper-write                       → write the paper sections

Integration points:

• From geo-experiment: If geo-experiment runs OLS/GWR/MGWR, this skill interprets those results. Read from output/results/.
• To paper-figure: Draft figures in output/spatial-analysis/figures/. The paper-figure skill polishes them.
• To result-to-claim: Model comparison table feeds directly into claim validation.
• Knowledge base: Read skills/knowledge/spatial-methods.md for code snippets, CRS reference, and detailed method parameters.