Post #9: Time Series Analysis for Geospatial Data in Python

 

Title: Analyzing Temporal Changes in Geospatial Data with Python

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📍Introduction

When working with geospatial data, time is often a critical factor. Whether it's tracking changes in land cover, urbanization, or environmental conditions, time series analysis helps us understand trends and predict future outcomes.

In this post, we’ll walk through:

• Time series analysis on geospatial data.

• How to analyze temporal changes in your GIS datasets.

• Using pandas, GeoPandas, and Matplotlib for time series visualization.

By the end of this post, you’ll be able to track changes in your spatial data and gain meaningful insights from temporal trends.

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🧰 Step 1: Install Required Libraries

To get started, you'll need a few libraries:

bash
pip install geopandas pandas matplotlib

• pandas: For time series data manipulation.

• GeoPandas: For geospatial data handling.

• Matplotlib: For plotting time series data.

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🗺️ Step 2: Load Your Geospatial Data with Time Information

For time series analysis, we need spatial data with an associated time attribute (such as year, month, or day). Let's say we have a land use dataset with different land use types over several years.

python
import geopandas as gpd
import pandas as pd

# Load the geospatial data (assuming data has a 'year' column)
land_use = gpd.read_file("data/land_use.shp")

# Check the first few rows of the data to ensure there's a time column
land_use.head()

Make sure your dataset has a time column (e.g., year, date, etc.). If it’s in a string format, you’ll need to convert it to a datetime format using pandas.

python
# If the 'year' column is not in datetime format
land_use['year'] = pd.to_datetime(land_use['year'], format='%Y')

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🔍 Step 3: Time Series Aggregation

If you want to analyze changes over time (e.g., land use changes over years), you’ll likely need to aggregate the data based on time. For example, you can calculate the area of each land use type for each year.

python
# Group by year and land use type, then calculate the area (or another metric)
land_use['area'] = land_use.geometry.area

# Aggregate by year and land use type
land_use_agg = land_use.groupby(['year', 'land_use_type'])['area'].sum().reset_index()

# View the aggregated data
print(land_use_agg.head())

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🧠 What Just Happened?

• Geometry Area: We calculated the area of each spatial feature (polygon) using geometry.area.

• Aggregation: The data was grouped by year and land use type to track how each land use category changes over time.

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📍 Step 4: Visualize the Time Series Data

Once you have the aggregated data, you can visualize the time series for each land use type. We'll plot the area of each land use type over the years.

python
import matplotlib.pyplot as plt

# Create a pivot table for better plotting
land_use_pivot = land_use_agg.pivot(index='year', columns='land_use_type', values='area')

# Plot time series for each land use type
land_use_pivot.plot(figsize=(10, 6))
plt.title("Land Use Changes Over Time")
plt.xlabel("Year")
plt.ylabel("Area (Square Units)")
plt.legend(title="Land Use Type")
plt.grid(True)
plt.tight_layout()
plt.show()

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🧠 What Just Happened?

• Pivot Table: We reshaped the data into a pivot table where each column represents a land use type, and each row represents a year. This allows easy plotting of each land use type over time.

• Plotting: We used Matplotlib to plot the area of each land use type across the years.

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📍 Step 5: Trend Analysis and Forecasting

Now that we have our time series data visualized, we can move on to trend analysis or even forecasting. For example, you can use linear regression to understand the trend over time.

python
from sklearn.linear_model import LinearRegression

# Prepare the data (example: 'Residential' land use)
residential_data = land_use_agg[land_use_agg['land_use_type'] == 'Residential']

# Reshape the data for regression
X = residential_data['year'].dt.year.values.reshape(-1, 1)  # Years as features
y = residential_data['area'].values  # Area as target

# Train a linear regression model
model = LinearRegression()
model.fit(X, y)

# Predict future values
future_years = pd.DataFrame({'year': range(2025, 2031)})
future_predictions = model.predict(future_years)

# Plot the trend
plt.plot(residential_data['year'], residential_data['area'], label="Actual Data")
plt.plot(future_years['year'], future_predictions, label="Forecast", linestyle='--')
plt.title("Land Use Area Prediction (Residential)")
plt.xlabel("Year")
plt.ylabel("Area (Square Units)")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

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🧠 What Just Happened?

• Linear Regression: We applied linear regression to analyze the trend of residential area over time and predict future changes.

• Prediction: We used the model to forecast future land use values (e.g., predicting the residential area for 2025–2030).

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📍 Step 6: Interpreting the Results

By examining the regression line and forecast values, you can see the trend of land use changes. For instance, you might discover:

• Increasing urbanization: Residential areas expanding over time.

• Land conservation: Some land use types may be decreasing in area.

Understanding these trends can help in making policy decisions, planning future land developments, or monitoring environmental impacts.

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🧠 Why Use Time Series for Geospatial Data?

• Change Detection: Time series allows you to identify and understand changes in spatial features over time (e.g., urban growth, deforestation).

• Prediction: You can forecast future trends, helping with decision-making and planning (e.g., predicting land expansion).

• Monitoring: Track dynamic changes in the landscape, whether they’re environmental or human-induced.

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🎯 Conclusion

Time series analysis in geospatial data gives you valuable insights into how the world changes over time. By combining pandas, GeoPandas, and Matplotlib, you can track these changes and even forecast future trends. This is especially useful for environmental monitoring, urban planning, and resource management.

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📌 Next Up:

➡️ Post 10: Advanced Geospatial Analysis with Remote Sensing Data