17  *Lab: Maps

Notebook Setup

  1. In the Taylor server, start a new JupyterLab session or access an active one.

  2. In the directory navigation bar, open the eds-220-sections directory you created in the last lab.

  3. In the terminal, navigate into the eds-220-sections directory and verify eds-220-sections is your current working directory.

  4. Create a new Python Notebook in eds-220-sections.

  5. Update the notebook’s name to something useful like ‘map-earthquake-data.ipynb’.

  6. Use the terminal to stage, commit, and push this file to the remote repository.

  7. If you are prompted for your credentials and need to set up a new Personal Access Token (PAT) follow steps 13-18 in this guide to set it up.

General directions
  • Add comments in each one of your code cells
  • Include markdown cells in between your code cells to add titles/information to each exercise
  • You won’t need to upload any data.
About the data

For this task we are going to use two datasets.

First dataset

Simplified data from the USGS Earthquakes Database. This is the same dataset we used for homework 1, task 4. It is in tabular format and has the following columns:

  • time = date and time of event (all events from 2014)
  • latitude = decimal degrees [-90,90]
  • longitude = decimal degrees [-360,360]
  • depth = depth of the event (km)
  • mag = magnitude of event
  • id = event identifier
  • place = where the event took place
  • type = type of event

Further information about the dataset can be accessed in the ANSS Comprehensive Earthquake Catalog (ComCat) Documentation.

Second dataset

Natural Earth’s low resolution cultural boundaries data. These low-resolution polygons are available directly through geopandas and are useful to make maps of the whole world, although might not be suitable for mapping individual nations or finer geospatial analysis.

17.1 Earthquake data

17.1.1 Import data

  1. Import the earthquake data directly from the following URL (it is a csv file). Store it in a variable raw_eqk.

  2. Do some simple preliminary exploration of the raw_eqk dataframe.

17.1.2 Data selection

  1. Check which are the unique values for the type of earthquakes.
  2. Select only data with with type equal to ‘eartquake’ and magnitude greater than or equal to 6.5.
  3. Store this as a new dataframe called eqk.

17.1.3 pd.DataFrame to gpd.GeoDataFrame

  1. Overwrite eqk dataframe as a geopandas.GeoDataFrame. HINT: find the CRS of the dataset by looking at the metadata.
  2. Make a simple plot of the eqk data.

17.2 Import low-resolution countries polygons

Run the following line to import Natural Earth’s low-resolution countries polygons using geopandas:

worldmap = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
worldmap

17.3 Map

Create a map of the earthquales of magnitude greater equal to 6.5 across the world in 2014. Color the points by the magnitude. This is an example of how it could look like: