Post #8: Geospatial Data Analysis with Machine Learning in Python

 

Title: Analyze Geospatial Data with Machine Learning in Python

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📍Introduction

Machine learning and geospatial data are a powerful combination. When you apply machine learning techniques to spatial data, you can uncover patterns, predict future trends, and automate analysis.

In this post, we’ll cover:

• Clustering for identifying spatial patterns (using KMeans)

• Prediction for spatial features (using Random Forest)

• How to integrate geospatial data with scikit-learn and GeoPandas.

Ready to use machine learning to unlock new insights from your GIS data? Let’s go!

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🧰 Step 1: Install Necessary Libraries

You’ll need a few packages to get started:

bash
pip install scikit-learn geopandas matplotlib

• GeoPandas: For handling geospatial data.

• scikit-learn: For applying machine learning models.

• matplotlib: For visualizations.

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🗺️ Step 2: Load and Prepare Your Geospatial Data

Let’s start by loading a geospatial dataset (for example, city boundaries and some features like population, area, or elevation).

python
import geopandas as gpd
import pandas as pd

# Load a shapefile (e.g., cities or districts with additional features)
cities = gpd.read_file("data/cities.shp")

# Check the CRS and convert if necessary
cities = cities.to_crs("EPSG:4326")

# View data columns (you should have features like 'population', 'area', 'elevation')
print(cities.columns)

Ensure that your spatial data has some attributes you can use for analysis, like population, area, or distance.

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🔍 Step 3: Clustering Cities Using KMeans

Clustering is a great way to identify patterns in your spatial data. KMeans is one of the simplest and most commonly used clustering algorithms.

We'll use KMeans to group cities based on population and area.

python
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

# Select the features (columns) we want to use for clustering
features = cities[["population", "area"]]

# Normalize the data (optional, but often improves performance)
features_scaled = (features - features.mean()) / features.std()

# Apply KMeans clustering
kmeans = KMeans(n_clusters=3, random_state=42)
cities['cluster'] = kmeans.fit_predict(features_scaled)

# Plot the clusters on a map
fig, ax = plt.subplots(figsize=(10, 10))
cities.plot(ax=ax, column='cluster', cmap='viridis', legend=True)
ax.set_title("KMeans Clusters of Cities by Population & Area")
plt.show()

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🧠 What Just Happened?

• Feature selection: We selected "population" and "area" as features for clustering.

• KMeans: We used KMeans to divide cities into 3 clusters based on these features.

• Visualization: We colored the cities based on the cluster they belong to, providing insights into spatial patterns.

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📍 Step 4: Predicting a Spatial Feature Using Random Forest

Next, let’s predict a spatial feature (e.g., elevation) using other attributes like population and area. For this, we’ll use a Random Forest regressor.

python
from sklearn.ensemble import RandomForestRegressor

# Select features for prediction (exclude the target 'elevation')
X = cities[["population", "area"]]
y = cities["elevation"]

# Split the data into training and testing sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Predict on the test set
y_pred = model.predict(X_test)

# Evaluate model accuracy
from sklearn.metrics import mean_squared_error
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse}")

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🧠 What Just Happened?

• RandomForestRegressor: This algorithm makes predictions by building multiple decision trees and averaging their results.

• Training and Testing: We split the data into training and testing sets to evaluate model performance.

• Prediction: The model predicts elevation based on population and area.

• Model Evaluation: We use mean squared error (MSE) to assess how well the model performs.

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📍 Step 5: Visualize the Prediction Results

Now, let’s visualize the predicted values on the map:

python
# Add predictions back to the GeoDataFrame
cities['predicted_elevation'] = model.predict(X)

# Plot the predicted elevation
fig, ax = plt.subplots(figsize=(10, 10))
cities.plot(ax=ax, column='predicted_elevation', cmap='coolwarm', legend=True)
ax.set_title("Predicted Elevation Based on Population & Area")
plt.show()

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🧠 Why Use Machine Learning for Geospatial Data?

• Clustering helps you identify natural groupings in your data (e.g., finding regions with similar characteristics like population density).

• Prediction models (like Random Forest) allow you to estimate missing or unmeasured values, such as predicting elevation or land value based on available attributes.

• Automation: Machine learning automates the analysis of large geospatial datasets, saving you time and effort in the field.

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🎯 Conclusion

By combining scikit-learn with GeoPandas, you can analyze spatial patterns, predict values, and uncover insights in your GIS data. Whether it’s clustering regions with similar characteristics or predicting missing values based on other spatial features, machine learning has a lot to offer in geospatial analysis.

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📌 Next Up:

➡️ Post 9: Deep Dive into Time Series Analysis for Geospatial Data