What problem does it solve?
This Skill provides guidelines and decision frameworks for spatial analysis, helping users select appropriate methods, diagnostics, and interpretation strategies based on their research questions and data contexts.
Core Features & Use Cases
- Guideline-Driven Analysis: Offers decision tables and guardrails for selecting spatial methods, diagnostics, and interpretation strategies.
- Data Readiness: Assesses data quality and readiness, including CRS, data quality checks, and multi-dataset integration.
- Spatial Weights: Recommends and justifies spatial weights for spatial statistics.
- Analytical Approaches: Provides guidelines for exploratory spatial data analysis, regression, clustering, prediction, accessibility, network analysis, interpolation, and temporal-spatial analysis.
- Diagnostics and Robustness: Offers guidelines for diagnostics and robustness checks, including residual Moran's I, Breusch-Pagan, Jarque-Bera, VIF, Cook's distance, AICc comparison, and spatial CV.
- Visualization: Guidelines for map requirements, plot types, and interpretation.
- Interpretation and Reporting: Provides structure and guardrails for reporting spatial analysis results.
- Guardrails Summary: Summarizes common mistakes to prevent and their corresponding guidelines.
- Outputs: Generates outputs such as question classification, analysis report, model comparison, scripts, figures, and results.
Quick Start
Use the spatial-analysis skill to analyze spatial data for your research question.
