geospatial data analysis using python

geospatial data analysis involves working with data that has a geographic component, such as latitude and longitude coordinates. python provides powerful libraries for handling and analyzing geospatial data, such as geopandas, shapely, folium, and rasterio. in this tutorial, we will cover the basics of geospatial data analysis using python with code examples.
before starting, make sure you have the following python libraries installed:
you can install them using pip:
first, let's start by loading some geospatial data. we will use a shapefile for this example. shapefiles are a common geospatial vector data format. we'll load a shapefile representing world countries.
one of the fundamental tasks in geospatial analysis is visualization. we can plot the loaded shapefile using the plot() method.
the shapely library provides functionalities for geometric operations on geometric objects. let's calculate the centroid of each country and add it as a new column to the dataframe.
folium is a powerful library for creating interactive maps. we can visualize our geospatial data on an interactive map using folium.

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