grind-lab-core/night_owl_research_agent/spatial-analysis

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

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:

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.

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:

2.3 Multi-Dataset Integration

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

2.4 Variable Preparation Guidelines

3. Spatial Weights Selection Guide

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

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:

Map classification guidance:

Color scheme rules:

Global Moran's I interpretation:

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

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: