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Datacamp

Visualizing Geospatial Data in Python

via Datacamp

Overview

Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.

One of the most important tasks of a data scientist is to understand the relationships between their data's physical location and their geographical context. In this course you'll be learning to make attractive visualizations of geospatial data with the GeoPandas package. You will learn to spatially join datasets, linking data to context. Finally you will learn to overlay geospatial data to maps to add even more spatial cues to your work. You will use several datasets from the City of Nashville's open data portal to find out where the chickens are in Nashville, which neighborhood has the most public art, and more!

Syllabus

Building 2-layer maps : combining polygons and scatterplots
-In this chapter, you will learn how to create a two-layer map by first plotting regions from a shapefile and then plotting location points as a scatterplot.

Creating and joining GeoDataFrames
-You'll work with GeoJSON to create polygonal plots, learn about projections and coordinate reference systems, and get practice spatially joining data in this chapter.

GeoSeries and folium
-First you will learn to get information about the geometries in your data with three different GeoSeries attributes and methods. Then you will learn to create a street map layer using folium.

Creating a choropleth building permit density in Nashville
-In this chapter, you will learn about a special map called a choropleth. Then you will learn and practice building choropleths using two different packages: geopandas and folium.

Taught by

Mary van Valkenburg

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