Projecting to Web Mercator for using web tiles

In the first chapter, we did an exercise on plotting the restaurant locations in Paris and adding a background map to it using the contextily package.

Currently, contextily assumes that your data is in the Web Mercator projection, the system used by most web tile services. And in that first exercise, we provided the data in the appropriate CRS so you didn't need to care about this aspect.

However, typically, your data will not come in Web Mercator (EPSG:3857) and you will have to align them with web tiles on your own.

GeoPandas, matplotlib and contextily are already imported.

Questo esercizio fa parte del corso

Working with Geospatial Data in Python

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Istruzioni dell'esercizio

Convert the restaurants dataset to the Web Mercator projection (EPSG:3857). Call the result restaurants_webmercator.
Make a plot of this projected dataset (specify the marker size to be 1) and add a background map using contextily.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Convert to the Web Mercator projection
restaurants_webmercator = restaurants.____

# Plot the restaurants with a background map
ax = ____
contextily.____
plt.show()

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Working with Geospatial Data in Python

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4.8+

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In this chapter, you will be introduced to the concepts of geospatial data, and more specifically of vector data. You will then learn how to represent such data in Python using the GeoPandas library, and the basics to read, explore and visualize such data. And you will exercise all this with some datasets about the city of Paris.

Exercise 1: Geospatial data Exercise 2: Restaurants in Paris Exercise 3: Adding a background map Exercise 4: Introduction to GeoPandas Exercise 5: Explore the Paris districts (I)Exercise 6: Explore the Paris districts (II)Exercise 7: The Paris restaurants as a GeoDataFrame Exercise 8: Exploring and visualizing spatial data Exercise 9: Visualizing the population density Exercise 10: Using pandas functionality: groupby Exercise 11: Plotting multiple layers

One of the key aspects of geospatial data is how they relate to each other in space. In this chapter, you will learn the different spatial relationships, and how to use them in Python to query the data or to perform spatial joins. Finally, you will also learn in more detail about choropleth visualizations.

Exercise 1: Shapely geometries and spatial relationships Exercise 2: Creating a Point geometry Exercise 3: Shapely's spatial methods Exercise 4: Spatial relationships with GeoPandas Exercise 5: In which district in the Eiffel Tower located?Exercise 6: How far is the closest restaurant?Exercise 7: The spatial join operation Exercise 8: Paris: spatial join of districts and bike stations Exercise 9: Map of tree density by district (1)Exercise 10: Map of tree density by district (2)Exercise 11: Choropleths Exercise 12: Equal interval choropleth Exercise 13: Quantiles choropleth Exercise 14: Compare classification algorithms

In this chapter, we will take a deeper look into how the coordinates of the geometries are expressed based on their Coordinate Reference System (CRS). You will learn the importance of those reference systems and how to handle it in practice with GeoPandas. Further, you will also learn how to create new geometries based on the spatial relationships, which will allow you to overlay spatial datasets. And you will further practice this all with Paris datasets!

Exercise 1: Coordinate Reference Systems Exercise 2: Geographic vs projected coordinates Exercise 3: Working with coordinate systems in GeoPandas Exercise 4: Projecting a GeoDataFrame Exercise 5: Projecting a Point Exercise 6: Calculating distance in a projected CRS Exercise 7: Projecting to Web Mercator for using web tiles

Esercizio in corso

Exercise 8: Spatial operations: creating new geometries Exercise 9: Exploring a Land Use dataset Exercise 10: Intersection of two polygons Exercise 11: Intersecting a GeoDataFrame with a Polygon Exercise 12: Overlaying spatial datasets Exercise 13: Overlay of two polygon layers Exercise 14: Inspecting the overlay result

In this final chapter, we leave the Paris data behind us, and apply everything we have learnt up to now on a brand new dataset about artisanal mining sites in Eastern Congo. Further, you will still learn some new spatial operations, how to apply custom spatial operations, and you will get a sneak preview into raster data.

Exercise 1: Introduction to the dataset Exercise 2: Import and explore the data Exercise 3: Convert to common CRS and save to a file Exercise 4: Styling a multi-layered plot Exercise 5: Additional spatial operations Exercise 6: Buffer around a point Exercise 7: Mining sites within national parks Exercise 8: Applying custom spatial operations Exercise 9: Finding the name of the closest National Park Exercise 10: Applying a custom operation to each geometry Exercise 11: Working with raster data Exercise 12: Import and plot raster data Exercise 13: Extract information from raster layer Exercise 14: The end