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UrbanMapper’s Examples Playground

New here? Start with the basics!

Before jumping into this playground, make sure you’ve walked through the Getting Started Step-By-Step and Getting Started W/ Pipeline guides. They’re explaining quite in depth UrbanMapper—trust us, it could be useful before delving into these examples, which are more straightforward and focused on specific tasks.

Welcome to the examples/ folder, where you will get hands-on experience with UrbanMapper! This set of Jupyter notebooks guides users through the process of loading, mapping, enhancing, and visualising urban data. These examples are suitable for both newcomers to urban analysis and experienced urban planners seeking to improve their process.

Explore the full potential of UrbanMapper with these real-world demos, including Brooklyn collisions, Paris trees, cab rides, to name a few.

Where’s the data at?

All the datasets powering these examples are public and ready for you to grab. Pick your channel:

  • Channel 1: Straight from the source
    • Check the links in each notebook, download from the official sites, and drop them into your data/ folder.
  • Channel 2: HuggingFace OSCUR datasets hub
    • If you prefer HuggingFace, you can find the datasets in the OSCUR datasets hub. Use the datasets library to load them directly into your code (integrated with UrbanMapper): 
      import urbanmapper as um
loader = um.UrbanMapper().loader.from_huggingface("oscur/pluto") # replace "pluto" with the dataset you want.
gdf = loader.load()
print(gdf.head())

Execution Status at a Glance

A quick heads-up on execution

Notebooks are either executed in the docs or kept as static previews. If you want the full interactive experience, run them locally from the examples/ folder in the repository.

2-End-to-End

These render as static previews. Run locally for full outputs.

1-Per-Module

2-End-to-End - Step by Step

3-Case-Studies

4-External-Libraries-Usages

Want the full interactive experience?

Clone the repo, pull the datasets, and run any notebook locally from examples/. Most notebooks contain links to their data sources or instructions in the first few cells.

Provost Simon