--- name: geospatial-viz-guide-85e340ff4e67 description: "Create maps, choropleths, and spatial data visualizations for research" metadata: openclaw: emoji: "🗺️" category: "analysis" subcategory: "dataviz" keywords: ["geospatial", "choropleth", "maps", "GIS", "spatial visualization", "geopandas"] source: "wentor-research-plugins" --- # Geospatial Visualization Guide A skill for creating maps, choropleths, and spatial data visualizations for research publications. Covers coordinate systems, choropleth maps, point maps, Python geospatial libraries, and cartographic best practices for academic papers. ## Geospatial Data Fundamentals ### Common Spatial Data Formats ``` Vector data (discrete features): - Shapefile (.shp): Legacy standard, multi-file - GeoJSON (.geojson): Web-friendly, single file - GeoPackage (.gpkg): Modern SQLite-based, recommended - KML (.kml): Google Earth format Raster data (continuous surfaces): - GeoTIFF (.tif): Georeferenced image - NetCDF (.nc): Climate and atmospheric data - HDF5 (.h5): Satellite and remote sensing data Key concepts: - CRS (Coordinate Reference System): How 3D Earth maps to 2D - EPSG:4326 (WGS84): Latitude/longitude (most GPS data) - EPSG:3857: Web Mercator (Google Maps, web tiles) - Always check and document your CRS ``` ## Choropleth Maps ### Building a Choropleth with GeoPandas ```python import geopandas as gpd import matplotlib.pyplot as plt def create_choropleth(shapefile_path: str, data_column: str, title: str, cmap: str = "YlOrRd") -> None: """ Create a choropleth map from a shapefile. Args: shapefile_path: Path to shapefile or GeoPackage data_column: Column name to visualize title: Map title cmap: Matplotlib colormap name """ gdf = gpd.read_file(shapefile_path) fig, ax = plt.subplots(1, 1, figsize=(12, 8)) gdf.plot( column=data_column, cmap=cmap, linewidth=0.5, edgecolor="0.5", legend=True, legend_kwds={ "label": data_column, "orientation": "horizontal", "shrink": 0.6, "pad": 0.05 }, ax=ax ) ax.set_title(title, fontsize=14, fontweight="bold") ax.axis("off") plt.tight_layout() plt.savefig("choropleth.pdf", bbox_inches="tight", dpi=300) ``` ### Joining Data to Geometries ```python import pandas as pd def join_data_to_map(gdf: gpd.GeoDataFrame, data: pd.DataFrame, geo_key: str, data_key: str) -> gpd.GeoDataFrame: """ Join tabular data to geographic features. Args: gdf: GeoDataFrame with polygons (e.g., country boundaries) data: DataFrame with your research data geo_key: Column in gdf to join on (e.g., 'ISO_A3') data_key: Column in data to join on (e.g., 'country_code') """ merged = gdf.merge(data, left_on=geo_key, right_on=data_key, how="left") missing = merged[merged[data.columns[1]].isna()] if len(missing) > 0: print(f"Warning: {len(missing)} regions have no data (will appear blank)") return merged ``` ## Point Maps and Proportional Symbols ### Mapping Research Sites or Events ```python def create_point_map(gdf_base: gpd.GeoDataFrame, points: gpd.GeoDataFrame, size_column: str = None, color_column: str = None) -> None: """ Create a point map with proportional symbols. Args: gdf_base: Base map (country or region polygons) points: GeoDataFrame with point geometries size_column: Column to scale point sizes color_column: Column to color points """ fig, ax = plt.subplots(figsize=(12, 8)) # Base map gdf_base.plot(ax=ax, color="lightgray", edgecolor="white", linewidth=0.5) # Points sizes = points[size_column] * 2 if size_column else 30 colors = points[color_column] if color_column else "red" points.plot( ax=ax, markersize=sizes, color=colors, alpha=0.6, edgecolor="black", linewidth=0.3 ) ax.axis("off") plt.tight_layout() plt.savefig("point_map.pdf", bbox_inches="tight", dpi=300) ``` ## Interactive Maps with Folium ```python import folium def create_interactive_map(center: tuple = (20, 0), zoom: int = 2) -> folium.Map: """ Create an interactive web map (useful for supplementary materials). Args: center: (latitude, longitude) center point zoom: Initial zoom level """ m = folium.Map(location=center, zoom_start=zoom, tiles="CartoDB positron") # Add markers, choropleth layers, or heatmaps as needed # folium.Marker([lat, lon], popup="Label").add_to(m) return m ``` ## Cartographic Best Practices ### Publication Standards ``` 1. Projection choice: - Global maps: Robinson or Equal Earth (not Mercator for thematic maps) - Country/region: Appropriate local projection - Mercator distorts area -- misleading for choropleths 2. Color schemes: - Sequential: Low-to-high values (YlOrRd, Blues, Viridis) - Diverging: Values around a midpoint (RdBu, BrBG) - Qualitative: Categorical data (Set2, Paired) - Use colorbrewer2.org for perceptually uniform palettes - Test for colorblind accessibility 3. Required map elements: - Title - Legend with units - Scale bar - North arrow (if orientation is non-standard) - Data source attribution - CRS/projection information 4. Ethical considerations: - Disputed borders: Use dashed lines or note in caption - Data gaps: Show "no data" regions explicitly (do not leave blank) - Privacy: Aggregate point data to protect individual locations ``` ## Free Data Sources | Source | Data | Format | |--------|------|--------| | Natural Earth | Country/region boundaries, physical features | Shapefile, GeoJSON | | GADM | Administrative boundaries (all countries, all levels) | GeoPackage, Shapefile | | OpenStreetMap | Roads, buildings, land use | PBF, Shapefile | | WorldPop | Population density grids | GeoTIFF | | NASA SEDAC | Socioeconomic and environmental data | GeoTIFF, Shapefile | | USGS Earth Explorer | Satellite imagery, elevation | GeoTIFF | --- Source attribution: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/analysis/dataviz/geospatial-viz-guide/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 Wentor AI Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/analysis/dataviz/geospatial-viz-guide/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Education & Research, Marketing Roles: Data Analyst, Marketing Analyst, Researcher Specialties: API, Database Tools: Geopandas Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 96dd5e86f5d5e3c6094061b75426060ea1b254a89d1154e2ac2a2fba1b0acc16