Exercises 1-4#

Objectives: These exercises help you to get a hands-on experience in using the open source spatial accessibility tools in Python and/or R. You should do the Exercise 1 first, but after that you can pick an exercise that interests you the most.

Exerise 1 - Run the tutorial#

Run the first tutorial that demonstrates how to calculate travel time matrices in Brighton by public transport and cycling. The tutorial will teach you the basics of using R5 with:

Exerise 2 - Inspect accessibility of locations of your own interest#

In the Exercise 1 (i.e. first tutorial), you familiarized yourself with calculating accessibility to schools in Brighton area. In this exercise, the idea is to apply these same techniques to e.g. services / or locations of your own interest (e.g. health-care, bars, restaurants, what-ever-you-like). To do this:

  • Create a new Notebook where you run these codes (alternatively add new cells under the tutorial)

  • Download the points of interest for yourself from OpenStreetMap using omsnx (Python) or osmdata (R). You can get inspiration from this short tutorial in the Extra materials

  • Repeat the travel time analyses for these locations and explore the patterns with maps and other graphics.

  • If you are using Binder: Download the Notebook to your own computer if you wish to store your work permanently

Exercise 3 - Shortest path analysis in Python#

Exerises 1 and 2 focused on computing travel time matrices that show regional patterns of access, but we did not conduct any individual shortest path analysis. If you are interested there is a separate tutorial that shows how to conduct individual shortest path analyses using Python.

Task:

  • Familiarized yourself with this tutorial to network analysis and shortest path analysis using networkx and pyrosm libraries.

Exercise 4 - Go adventurous and repeat the analyses in some other city!#

Advanced: If you feel that the previous exercises weren’t enough challenge for you, here, you should conduct accessibility analyses in some other city. You should find appropriate data for yourself (at least GTFS and OSM data), build the networks, and run the travel time analyses for the locations that interest you.