How to Analyze Geospatial Data Using EDA for Better Insights

Exploratory Data Analysis (EDA) is a critical first step in understanding any dataset, and when applied to geospatial data, it provides powerful insights into spatial relationships, patterns, and anomalies that are often obscured in traditional datasets. Geospatial EDA incorporates mapping techniques, spatial statistics, and visual analytics to help decision-makers interpret and utilize location-based data more effectively. Here’s a comprehensive guide on how to analyze geospatial data using EDA for better insights.

Understanding Geospatial Data

Geospatial data is information that describes objects, events, or features with a location on or near the surface of the Earth. It includes:

• Vector Data: Points (e.g., city locations), lines (e.g., roads), and polygons (e.g., country boundaries).

• Raster Data: Gridded data, such as satellite imagery or elevation maps.

• Attributes: Additional information tied to spatial features, such as population, income levels, or land use.

Step-by-Step EDA for Geospatial Data

1. Data Collection and Cleaning

Before analysis begins, ensure the data is accurate, relevant, and clean.

• Sources: Common sources include government databases (e.g., USGS, NASA), open data platforms (e.g., OpenStreetMap), and APIs (e.g., Google Maps).

• Formats: Geospatial data often comes in formats like GeoJSON, Shapefile (.shp), KML, and CSV with latitude/longitude.

• Cleaning: Handle missing coordinates, inconsistent projections, duplicate geometries, and invalid polygon shapes.

2. Coordinate Reference Systems (CRS)

CRS standardizes how the two-dimensional, projected map corresponds to real places on Earth.

• Use a consistent CRS across all datasets.

• For global analysis, WGS 84 (EPSG:4326) is common.

• For local analysis, use a projected CRS that minimizes distortion.

3. Basic Descriptive Statistics

Initial exploration includes summarizing attribute data and spatial features:

• Count the number of features per region (e.g., number of stores in each city).

• Calculate summary statistics: mean, median, standard deviation of variables like elevation, population density, etc.

• Detect outliers that could indicate data entry errors or unique events.

4. Visualization Techniques

Visualization is a cornerstone of geospatial EDA.

a. Static Maps

• Choropleth Maps: Show attribute variation by color (e.g., unemployment rate by region).

• Dot Maps: Represent points, useful for population or incident location.

• Heatmaps: Visualize density of points (e.g., crime hotspots).

• Proportional Symbol Maps: Size of symbols varies based on data value.

b. Interactive Maps

Using tools like Folium or Plotly, interactive maps allow zooming, tooltips, and filtering for deeper exploration.

c. 3D Maps

For elevation or urban development data, 3D visualizations (e.g., using Kepler.gl or Cesium) can provide added dimensionality.

5. Spatial Relationships and Patterns

EDA also includes uncovering spatial correlations and trends.

a. Spatial Clustering

• K-Means and DBSCAN: Identify natural groupings of points.

• Getis-Ord Gi*: Detect hotspots or cold spots.

• Moran’s I: Measures spatial autocorrelation – the degree to which similar values cluster in space.

b. Buffer Analysis

Create buffer zones around features (e.g., 500m radius around schools) to study influence or coverage.

c. Overlay Analysis

Combine multiple layers (e.g., zoning, land use, and flood zones) to assess interactions and overlaps.

6. Temporal-Spatial Analysis

Many geospatial datasets have a time component. Analyzing how spatial distributions change over time can yield insights.

• Time series maps: Visualize change (e.g., deforestation or urban growth).

• Animation tools: Use tools like Carto or TimeSlider in Leaflet to animate spatial change.

7. Statistical Correlation with Geography

Link spatial data with socio-economic, environmental, or infrastructure datasets to uncover meaningful patterns.

• Compare average income to proximity to healthcare facilities.

• Analyze housing prices relative to distance from green spaces.

• Evaluate pollution levels near industrial zones.

8. Dimensionality Reduction and Feature Engineering

Incorporate spatial features into machine learning pipelines:

• Convert geometry into numeric features: distance to city center, number of nearby amenities, land use type.

• Use dimensionality reduction (e.g., PCA, t-SNE) to simplify high-dimensional spatial datasets for visualization and clustering.

9. Tools and Libraries

Several powerful tools and libraries support geospatial EDA:

a. Python Libraries

• GeoPandas: Combines pandas with spatial operations.

• Shapely: Manipulate and analyze planar geometric objects.

• Folium / Plotly: Create interactive maps.

• Rasterio: Read and write raster data.

• Scikit-mobility: Analyze human mobility patterns.

b. R Packages

• sf: Handle simple features.

• tmap: Create thematic maps.

• leaflet: Build interactive maps.

• spdep: Spatial dependence analysis.

c. GIS Software

• QGIS: Open-source GIS software for mapping and spatial analysis.

• ArcGIS: Comprehensive GIS platform with robust analysis tools.

• Google Earth Engine: Cloud platform for large-scale geospatial analysis.

10. Case Studies and Applications

a. Urban Planning

EDA helps planners understand land use, traffic patterns, and population distribution for zoning and infrastructure development.

b. Environmental Monitoring

Track deforestation, air quality, and water levels using satellite data and EDA techniques to identify critical changes.

c. Public Health

Study spatial distribution of diseases, healthcare access, and environmental hazards to improve public health strategies.

d. Retail Site Selection

Retailers use spatial EDA to find optimal locations based on foot traffic, demographic characteristics, and competitor locations.

e. Disaster Management

Visualize vulnerable zones, evacuation routes, and real-time sensor data to plan and respond to natural disasters efficiently.

11. Limitations and Considerations

• Modifiable Areal Unit Problem (MAUP): Statistical results may vary depending on the spatial unit (e.g., zip code vs county).

• Spatial Data Quality: Accuracy of coordinates, attribute data, and temporal resolution impacts insights.

• Computational Cost: High-resolution data and complex operations may demand significant resources.

12. Best Practices

• Always validate assumptions with domain knowledge.

• Combine visual and statistical methods for robust analysis.

• Use reproducible workflows with code-based tools.

• Incorporate both global and local spatial statistics.

• Maintain metadata for traceability and understanding of source data.

Conclusion

Analyzing geospatial data with EDA provides invaluable insights into patterns and relationships tied to location. By systematically exploring spatial datasets through cleaning, visualization, statistical analysis, and spatial modeling, professionals across industries can make informed, data-driven decisions. As geospatial data becomes more accessible and tools become more powerful, the role of spatial EDA in analytics and decision-making continues to grow.