Spatial Clustering
Last updated on 2025-12-05 | Edit this page
Estimated time: 101 minutes
Overview
Questions
- What is spatial clustering and why do we use it?
- How can we perform basic clustering on geographic point data?
- How do algorithms like K-Means, Hierarchical Clustering, and DBSCAN differ?
Objectives
- Understand the concept of spatial clustering
- Learn how to prepare point data for clustering
- Apply K-Means, Hierarchical Clustering, and DBSCAN in Python
- Visualize clustering results on simple scatterplots and maps
Why is Spatial Clustering Important?
Spatial clustering is a core method in geospatial analysis for identifying how points, people, places, or events are distributed across space. Instead of treating data as isolated observations, clustering helps us detect patterns, revealing where concentrations or groupings occur — and just as importantly, where they do not.
Clustering allows us to transform large sets of point data into meaningful spatial insights that can guide research, decision-making, and planning.
What Spatial Clustering Helps Us Understand
- Where events or features form geographic hotspots
- How points group based on proximity or similarity
- Regions of high vs. low density
- Patterns of distribution — clustered, dispersed, or random?
- Spatial relationships in social, environmental, or urban data
- Location-based trends that maps alone may not reveal
Why Analysts Use Spatial Clustering
- Reduces complex spatial datasets into interpretable
groups
- Helps detect clusters in public health (disease outbreaks), crime,
ecology, and more
- Identifies emerging hotspots for management or
intervention
- Useful for urban planning, environmental monitoring, and
archaeology
- Works well as a first step for further spatial statistics (PySAL,
regression, AI)
- Enables classification, prediction, and pattern recognition in large datasets
Clustering at a Glance
| Method | Strength | Best For |
|---|---|---|
| K-Means | Simple, fast | Well-separated, circular clusters |
| Hierarchical | Dendrogram visualization | Multi-scale grouping, unknown k values |
| DBSCAN | Finds irregular shapes + noise | Spatial hotspots and natural patterns |
Spatial clustering is often the first analytical step when exploring point distribution. It moves the analysis beyond visual mapping — showing not only where points are located, but how spatial processes shape them.
Introduction
Spatial clustering is a core method used in geography, archaeology, ecology, and urban studies. It helps identify patterns in the spatial distribution of points—such as hotspots of crime, clusters of archaeological artifacts, or regions with similar environmental characteristics.
This beginner tutorial walks you through the fundamentals of spatial clustering using a simple dataset of geographic coordinates. The workflow is entirely in Python, following the structure used in your uploaded notebook.
We will cover:
- Loading and exploring point data
- Preparing coordinates for clustering
- Running three clustering algorithms
- Visualizing the results
All examples use standard Python libraries:pandas, geopandas, matplotlib,
sklearn, and scipy.
Learners may need extra time understanding the differences between clustering algorithms. Consider pausing after each method and showing multiple visualizations.
1. Loading Spatial Point Data
Spatial clustering typically starts with a set of point locations. A minimal example:
Visualize the raw points:
PYTHON
import matplotlib.pyplot as plt
plt.scatter(df.lon, df.lat, s=10)
plt.title("Raw Spatial Points")
plt.xlabel("Longitude")
plt.ylabel("Latitude")
plt.show()This simple scatterplot helps identify whether your data already looks clustered.
2. K-Means Clustering
K-Means is the simplest clustering algorithm. It works best when:
You know the number of clusters you want
Clusters are roughly circular
Points are evenly distributed
PYTHON
from sklearn.cluster import KMeans
coords = df[['lon', 'lat']]
kmeans = KMeans(n_clusters=4, random_state=42)
df['kmeans_label'] = kmeans.fit_predict(coords)Visualize results:
3. Hierarchical Clustering
Hierarchical clustering builds clusters step-by-step. It is useful when:
You want a dendrogram
You don’t know the number of clusters beforehand
Clusters may have irregular shapes
Example:
PYTHON
from sklearn.cluster import AgglomerativeClustering
agg = AgglomerativeClustering(n_clusters=4)
df['hier_label'] = agg.fit_predict(coords)Plot:
4. DBSCAN: Density-Based Clustering
DBSCAN is ideal for spatial datasets because:
It finds clusters of any shape
It identifies noise points
It does not require the number of clusters in advance
Example:
PYTHON
from sklearn.cluster import DBSCAN
import numpy as np
epsilon = 0.01 # distance threshold
db = DBSCAN(eps=epsilon, min_samples=5).fit(coords)
df['dbscan_label'] = db.labels_Points labeled -1 are noise (outliers).
Plot:
PYTHON
plt.scatter(df.lon, df.lat, c=df.dbscan_label, cmap='Accent')
plt.title("DBSCAN Spatial Clusters")
plt.show()Challenge
Challenge 1: Exploring Your Own Dataset
Using the examples above:
Load your own set of spatial coordinates
Apply K-Means and DBSCAN
Compare the results
Which method performs better, and why?
Example Interpretation
K-Means finds evenly divided clusters
DBSCAN finds natural geographic groups and labels outliers
For irregular spatial patterns, DBSCAN usually performs better
Larger eps creates larger clusters. Smaller
eps creates more clusters and more noise points.
Math
DBSCAN uses density to define clusters. Its key idea:
A point belongs to a cluster if it has at least
min_samples neighbors within a distance ε:
$\text{density} = \frac{\text{neighbors}}{\pi \varepsilon^2}$
Higher density regions form clusters; low density points become noise.
Spatial clustering groups geographic points into meaningful patterns
K-Means is simple but assumes circular clusters
Hierarchical clustering builds clusters step-wise
DBSCAN is best for irregular shapes and detecting noise
Always visualize your clusters to interpret them correctly