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Course

Visualizing Geospatial Data in Python

СреднийУровень мастерства

Обновлено 06.2025

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

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PythonData Visualization4 ч14 videos51 Exercise4,250 XP22,747Свидетельство о достижениях

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Описание курса

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!

Предварительные требования

Introduction to Data Visualization with Matplotlib Data Manipulation with pandas

1

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.

Introduction

Plotting a scatterplot from longitude and latitude

Styling a scatterplot

Extracting longitude and latitude

Plotting chicken locations

Geometries and shapefiles

Creating a GeoDataFrame & examining the geometry

Plotting shapefile polygons

Scatterplots over polygons

Plotting points over polygons - part 1

Plotting points over polygons - part 2

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2

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.

GeoJSON and plotting with geopandas

Working with GeoJSON

Map Nashville neighborhoods

Projections and coordinate reference systems

Changing coordinate reference systems

Construct a GeoDataFrame from a DataFrame

Spatial joins

Spatial join practice

Finding the neighborhood with the most public art

Aggregating points within polygons

Plotting the Urban Residents neighborhood and art

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3

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.

GeoSeries attributes and methods I

Find the area of the Urban Residents neighborhood

GeoSeries attributes and methods II

The center of the Urban Residents neighborhood

Prepare to calculate distances

Art distances from neighborhood center

Street maps with folium

Create a folium location from the urban centroid

Create a folium map of downtown Nashville

Folium street map of the downtown neighborhood

Creating markers and popups in folium

Adding markers for the public art

Troubleshooting data issues

A map of downtown art

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4

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.

What is a choropleth?

Finding counts from a spatial join

Council district areas and permit counts

Calculating a normalized metric

Choropleths with geopandas

Geopandas choropleths

Area in km squared, geometry in decimal degrees

Spatially joining and getting counts

Building a polished Geopandas choropleth

Choropleths with folium

Folium choropleth

Folium choropleth with markers and popups

Closing thoughts

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Visualizing Geospatial Data in Python

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