Visualizing Geospatial Data in Python
Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.
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Course Description
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!
- 1
Building 2-layer maps : combining polygons and scatterplots
FreeIn 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.
Introduction50 xpPlotting a scatterplot from longitude and latitude50 xpStyling a scatterplot100 xpExtracting longitude and latitude100 xpPlotting chicken locations100 xpGeometries and shapefiles50 xpCreating a GeoDataFrame & examining the geometry100 xpPlotting shapefile polygons100 xpScatterplots over polygons50 xpGeometry50 xpPlotting points over polygons - part 1100 xpPlotting points over polygons - part 2100 xp - 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 geopandas50 xpWorking with GeoJSON50 xpColormaps100 xpMap Nashville neighborhoods100 xpProjections and coordinate reference systems50 xpChanging coordinate reference systems100 xpConstruct a GeoDataFrame from a DataFrame100 xpSpatial joins50 xpSpatial join practice100 xpFinding the neighborhood with the most public art100 xpAggregating points within polygons100 xpPlotting the Urban Residents neighborhood and art100 xp - 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 I50 xpFind the area of the Urban Residents neighborhood100 xpGeoSeries attributes and methods II50 xpThe center of the Urban Residents neighborhood100 xpPrepare to calculate distances100 xpArt distances from neighborhood center100 xpStreet maps with folium50 xpCreate a folium location from the urban centroid100 xpCreate a folium map of downtown Nashville100 xpFolium street map of the downtown neighborhood100 xpCreating markers and popups in folium50 xpAdding markers for the public art100 xpTroubleshooting data issues100 xpA map of downtown art100 xp - 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?50 xpFinding counts from a spatial join100 xpCouncil district areas and permit counts100 xpCalculating a normalized metric100 xpChoropleths with geopandas50 xpGeopandas choropleths100 xpArea in km squared, geometry in decimal degrees100 xpSpatially joining and getting counts100 xpBuilding a polished Geopandas choropleth100 xpChoropleths with folium50 xpFolium choropleth100 xpFolium choropleth with markers and popups100 xpClosing thoughts50 xp
In the following tracks
Data Visualization
Greg Wilson
Adrián Soto
Mary van Valkenburg
Data Science Program Manager at Nashville Software School
Mary van Valkenburg led the development of the Analytics and Data Science Programs at Nashville Software School (NSS), a non-profit that helps adults develop skills and launch careers in software development and data science. Currently she supports NSS in using organizational data to guide decision-making as the Director of Data Operations.
What do other learners have to say?
I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.
Devon Edwards Joseph
Lloyds Banking Group
DataCamp is the top resource I recommend for learning data science.
Louis Maiden
Harvard Business School
DataCamp is by far my favorite website to learn from.
Ronald Bowers
Decision Science Analytics, USAA
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