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)

Joining Data to Geometries

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

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

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