UrbanMapper
Spatial Join & Enrich any urban layer given any external urban dataset of interest, streamline your urban analysis with Scikit-Learn-Like pipelines, and share your insights with the urban research community!
Install / Use
/learn @VIDA-NYU/UrbanMapperREADME
[!IMPORTANT]
- 📹
UrbanMappergot its first Model Context Protocol (MCP) 👉https://www.youtube.com/watch?v=6gLkmKevj8Y 👈- 🤝 We support JupyterGIS following one of your
Urban Pipeline's analysis for collaborative in real-time exploration on Jupyter 🏂 Shout-out to @mfisher87 andJGISteam for their tremendous help.
UrbanMapper, In a Nutshell
UrbanMapper lets you link your data to spatial features—matching, for example, traffic events to streets—to enrich
each location with meaningful, location-based information. Formally, it defines a spatial enrichment
function $f(X, Y) = X \bowtie Y$, where $X$ represents urban layers (e.g., Streets, Sidewalks, Intersections and
more)
and $Y$ is a user-provided dataset (e.g., traffic events, sensor data). The operator $\bowtie$ performs a spatial
join, enriching each feature in $X$ with relevant attributes from $Y$.
In short, UrbanMapper is a Python toolkit that enriches typically plain urban layers with datasets in a reproducible,
shareable, and easily updatable way using minimal code. For example, given traffic accident data and a streets layer
from OpenStreetMap, you can compute accidents per street with
a Scikit-Learn–style pipeline called the Urban Pipeline—in under 15 lines of code.
As your data evolves or team members want new analyses, you can share and update the Urban Pipeline like a trained
model, enabling others to run or extend the same workflow without rewriting code.
There are more to UrbanMapper, explore!
Installation
Install UrbanMapper via pip (works in any environment):
pip install urban-mapper
Then launch Jupyter Lab to explore UrbanMapper:
jupyter lab
[!TIP] We recommend installing
UrbanMapperin a virtual environment to keep things tidy and avoid dependency conflicts. You can find detailed instructions—including how to install within a virtual environment using uv, conda or from source in the UrbanMapper Installation Guide.
Getting Started with UrbanMapper
We highly recommend exploring the UrbanMapper Documentation, starting with the homepage general information and then the Getting Started section.
Once you have grasped the basics, we recommend exploring the Interactive Examples
or running yourself the notebooks through the examples/ directory.
Licence
UrbanMapper is released under the MIT Licence.
Acknowledgments
This work is supported by the NSF and is part of the OSCUR initiative.
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