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Geospatial Analysis

Analyze data that has geographic or spatial components to identify patterns and relationships.

Geospatial analysis definition:

Geospatial analysis is the process of analyzing spatial data to extract meaningful insights and patterns. Geospatial data can be in the form of geographical coordinates, satellite images, and other related data. In the context of modern data pipelines, geospatial analysis can help in understanding the relationship between different geographic locations and other data points, such as demographics or climate data.

Geospatial data analysis is used to model and represent how people, objects, and phenomena interact within space, as well as to make predictions based on trends in the relationships between places.

Python has several libraries that can be used for geospatial analysis, including Geopandas, Shapely, Fiona, and PySAL. Here is a practical example using Geopandas and Matplotlib:

Geospatial data analysis in Python

  • Matplotlib installation instructions are found here but basically just involves the command python -m pip install -U matplotlib .
import matplotlib.pyplot as plt
import geopandas as gpd

# Load the data
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))

# Plot the world map
fig, ax = plt.subplots(figsize=(10, 6))
world.plot(ax=ax, color='white', edgecolor='black')

# Plot the cities
cities.plot(ax=ax, markersize=5, color='red')

# Set the title and axis labels
ax.set_title('Cities of the world')

# Show the plot

This code loads two datasets from the geopandas library: world and cities. The world dataset contains the shapes of all countries in the world, while the cities dataset contains the locations of major cities in the world.

The code then creates a plot of the world map using matplotlib. It sets the color of the countries to white and the color of the borders to black. It then plots the cities on top of the map as red dots.

Finally, the code sets the title and axis labels of the plot and displays it using

This is just a simple example, but geopandas and matplotlib offer a wide range of geospatial analysis tools that can be used for more advanced applications.

Other data engineering terms related to
Data Aggregation and Summarization: