geospatial-viz-guide

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

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)