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  3. Working with Geospatial Data in Python
  • 1

    Introduction to geospatial vector data

    Free

    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.

  • 2

    Spatial relationships

    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.

  • 3

    Projecting and transforming geometries

    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!

  • 4

    Putting it all together - Artisanal mining sites case study

    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.

Initializing

Exercise

Plotting multiple layers

Another typical pandas functionality is filtering a dataframe: taking a subset of the rows based on a condition (which generates a boolean mask).

In this exercise, we will take the subset of all African restaurants, and then make a multi-layered plot. In such a plot, we combine the visualization of several GeoDataFrames on a single figure. To add one layer, we can use the ax keyword of the plot() method of a GeoDataFrame to pass it a matplotlib axes object.

The restaurants data is already loaded as the restaurants GeoDataFrame. GeoPandas is imported as geopandas and matplotlib.pyplot as plt.

Instructions

100 XP
  • Select a subset of all rows where the type is 'African restaurant'. Call this subset african_restaurants.
  • Make a plot of all restaurants and use a uniform grey color. Remember to pass a matplotlib axes object to the plot() method.
  • Add a second layer of only the African restaurants in red. For the typical colors, you can use English names such as 'red' and 'grey'.
  • Remove the box using the set_axis_off() method on the matplotlib axes object.