___
> [!IMPORTANT]
> - đš `UrbanMapper` got its first Model Context Protocol (MCP) đhttps://www.youtube.com/watch?v=6gLkmKevj8Y đ
> - đ¤ We support [JupyterGIS](https://github.com/geojupyter/jupytergis) following one of your `Urban Pipeline`'s
analysis for collaborative in real-time exploration on Jupyter đ Shout-out
to [@mfisher87](https://github.com/mfisher87) and `JGIS` team for their tremendous help.
## UrbanMapper, In a Nutshell
`UrbanMapper` lets you link your data to spatial featuresâmatching, for example, traffic events to streetsâto 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](https://www.openstreetmap.org), you can compute accidents per street with
a [Scikit-Learn](https://scikit-learn.org/stable/)âstyle pipeline called the `Urban Pipeline`âin 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):
```bash
pip install urban-mapper
```
Then launch Jupyter Lab to explore `UrbanMapper`:
```bash
jupyter lab
```
> [!TIP]
> We recommend installing `UrbanMapper` in a virtual environment to keep things tidy and avoid dependency conflicts. You
> can find detailed instructionsâincluding how to install within a virtual environment
> using [uv](https://docs.astral.sh/uv/getting-started/installation/), [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html)
> or from source in the [UrbanMapper Installation Guide](https://urbanmapper.readthedocs.io/en/latest/getting-started/installation/).
---
# Getting Started with UrbanMapper
We highly recommend exploring the [UrbanMapper Documentation](https://urbanmapper.readthedocs.io/en/latest/), starting
with the homepage general information and then the [Getting Started](https://urbanmapper.readthedocs.io/en/latest/getting-started/)
section.
Once you have grasped the basics, we recommend exploring the [Interactive Examples](https://urbanmapper.readthedocs.io/en/latest/examples/)
or running yourself the notebooks through the `examples/` directory.
---
## Licence
`UrbanMapper` is released under the [MIT Licence](./LICENCE).
## Acknowledgments
This work is supported by the NSF and is part of the [OSCUR initiative]([url](https://oscur.org/)).
