Gis Mapping Humanities
by a5c-ai
Create spatial visualizations and geographic analyses for historical and cultural research questions using QGIS/ArcGIS
Skill Details
Repository Files
1 file in this skill directory
name: gis-mapping-humanities description: Create spatial visualizations and geographic analyses for historical and cultural research questions using QGIS/ArcGIS allowed-tools: Read, Grep, Write, Edit, Glob, Bash, WebFetch
GIS Mapping for Humanities
Create spatial visualizations and geographic analyses for historical and cultural research questions using GIS tools.
Overview
This skill enables spatial analysis for humanities research. It encompasses GIS mapping, spatial visualization, and geographic analysis to explore spatial dimensions of historical and cultural phenomena.
Capabilities
Spatial Data Creation
- Point digitization
- Polygon creation
- Line features
- Attribute data
- Georeferencing
Map Design
- Thematic mapping
- Cartographic design
- Legend creation
- Scale management
- Projection selection
Spatial Analysis
- Proximity analysis
- Density mapping
- Pattern detection
- Network analysis
- Change over time
Visualization
- Interactive maps
- Web mapping
- Story maps
- Animated displays
- Print cartography
Usage Guidelines
Mapping Process
- Define research question
- Identify spatial data needs
- Gather or create data
- Process and clean data
- Conduct analysis
- Design visualization
- Interpret results
Data Considerations
- Historical accuracy
- Source documentation
- Uncertainty representation
- Temporal precision
- Attribution
Design Principles
- Clear visual hierarchy
- Appropriate symbolization
- Readable labeling
- Contextual information
- Accessible design
Integration Points
Related Processes
- Spatial Humanities Mapping
- Data Visualization for Cultural Research
- Network Analysis for Humanities
Collaborating Skills
- metadata-standards-implementation
- topic-modeling-text-mining
- tei-text-encoding
References
- Spatial humanities resources
- QGIS documentation
- Historical GIS projects
- Cartographic design principles
Related Skills
Attack Tree Construction
Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.
Grafana Dashboards
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.
Matplotlib
Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.
Scientific Visualization
Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
Seaborn
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
Shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Query Writing
For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Scientific Visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.