How to Perform Exploratory Data Analysis on Geospatial Data

Exploratory Data Analysis (EDA) is a critical first step in any data science or analytics workflow, and this holds true for geospatial data as well. Geospatial data adds complexity with its spatial component, but it also unlocks powerful insights through spatial relationships and patterns. Performing EDA on geospatial data involves a blend of statistical analysis, visualization, and spatial reasoning. Here’s a comprehensive guide on how to conduct EDA on geospatial data effectively.

Understanding Geospatial Data

Before diving into EDA, it’s important to understand the nature of geospatial data. It comes in two primary formats:

• Vector Data: Represents discrete features such as points (e.g., cities), lines (e.g., roads), and polygons (e.g., boundaries).

• Raster Data: Represents continuous data like elevation, temperature, or satellite imagery using a grid of cells or pixels.

Key components of geospatial data include:

• Coordinates: Latitude and longitude or other reference systems.

• Attribute Data: Information associated with each spatial feature.

• Coordinate Reference System (CRS): Defines how the two-dimensional, projected map in your GIS relates to real places on the earth.

Step 1: Data Collection and Loading

The first step involves collecting and loading geospatial datasets. Common formats include:

• Shapefiles (.shp)

• GeoJSON

• KML

• CSV files with latitude and longitude fields

• Raster files like GeoTIFF

Use Python libraries such as geopandas, shapely, and rasterio to load geospatial data:

python
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import geopandas as gpd
data = gpd.read_file("data_file.shp")

For raster data:

python
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import rasterio
raster = rasterio.open("raster_file.tif")

Step 2: Inspecting the Data Structure

Begin by checking the structure of the data:

• Head of the dataset: Understand the attributes and geometry column.

• Coordinate Reference System (CRS): Ensure consistency in spatial referencing.

• Null values: Check for missing data that might affect analysis.

python
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print(data.head())
print(data.crs)
print(data.isnull().sum())

Step 3: Summary Statistics

Just like with non-spatial data, summary statistics help understand distributions and central tendencies:

• Descriptive statistics of attributes

• Count of unique geometries

• Distribution of feature types (points, lines, polygons)

python
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print(data.describe())
print(data['geometry'].geom_type.value_counts())

For raster data, compute:

• Minimum, maximum, mean, standard deviation

• Histograms of pixel values

python
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import numpy as np
stats = {
    'min': np.min(raster.read(1)),
    'max': np.max(raster.read(1)),
    'mean': np.mean(raster.read(1)),
    'std': np.std(raster.read(1))
}

Step 4: Spatial Visualization

Spatial visualization is a powerful EDA technique in geospatial analytics:

• Plot raw geometries to identify spatial distribution

• Color-code features by attribute values

• Overlay multiple layers to identify relationships

python
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data.plot(column='attribute_name', legend=True, cmap='viridis')

Interactive maps using folium or plotly add more depth:

python
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import folium
map = folium.Map(location=[latitude, longitude], zoom_start=10)
folium.GeoJson(data).add_to(map)
map

Step 5: Spatial Relationships and Patterns

Understanding spatial patterns is key:

• Spatial Clustering: Use tools like DBSCAN to find clusters.

• Spatial Autocorrelation: Use Moran’s I or Geary’s C.

• Distance calculations: Evaluate proximity between features.

python
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from sklearn.cluster import DBSCAN
from shapely.geometry import Point
from geopandas.tools import sjoin

# Calculate distances or perform clustering

Step 6: Spatial Joins and Attribute Enrichment

You may need to join datasets based on location:

• Join points to polygons (e.g., customers to regions)

• Merge external data such as demographic or environmental data

python
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joined_data = gpd.sjoin(points_gdf, polygons_gdf, how='inner', op='within')

Enriching datasets with additional context can reveal more actionable insights.

Step 7: Temporal Analysis (if applicable)

If your data has a time component (e.g., GPS logs, satellite imagery), conduct temporal EDA:

• Time series plots of spatial attributes

• Animation of spatial changes over time

• Change detection in raster values

python
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data['timestamp'] = pd.to_datetime(data['timestamp'])
data.set_index('timestamp').resample('D').size().plot()

For satellite imagery or raster time-series:

• Calculate differences across time

• Analyze land cover change or NDVI progression

Step 8: Heatmaps and Density Analysis

Heatmaps reveal hotspots and intensity:

• Use kernel density estimation (KDE) for point data

• Rasterize features to create density layers

python
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import seaborn as sns
sns.kdeplot(data['longitude'], data['latitude'], shade=True)

Tools like QGIS or ArcGIS also provide GUI-based heatmap generation for non-programmatic EDA.

Step 9: Dimensionality Reduction and Feature Engineering

Create new features from spatial data:

• Area, perimeter, length

• Nearest neighbor distances

• Zonal statistics from raster within polygons

Apply PCA or t-SNE on attributes if dimensionality is high.

python
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data['area'] = data.geometry.area
data['centroid'] = data.geometry.centroid

Step 10: Documenting and Reporting Findings

Summarize the key insights found during EDA:

• What patterns emerged spatially and temporally?

• Which variables showed strong spatial correlation?

• Were there any anomalies or outliers?

Visuals such as maps, charts, and histograms should be saved and organized to support further modeling or decision-making.

Tools and Libraries for Geospatial EDA

Here’s a list of useful Python libraries:

• GeoPandas: Spatial operations on vector data

• Shapely: Geometry manipulation

• Folium / Plotly: Interactive mapping

• Matplotlib / Seaborn: Static plots

• PySAL: Spatial econometrics and statistics

• Rasterio / xarray: Raster data handling

• scikit-learn: Clustering and machine learning

• Kepler.gl / Deck.gl: WebGL-based advanced visualizations

Conclusion

Exploratory Data Analysis on geospatial data requires a balanced approach involving both traditional statistical techniques and spatial reasoning. By understanding the structure, visualizing the distributions, assessing spatial relationships, and enriching data with context, you can uncover critical patterns that might be hidden in purely tabular analysis. As geospatial data continues to grow in availability and importance, mastering EDA in this context is essential for unlocking its full analytical potential.