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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!

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Installation

Install UrbanMapper via pip (works in any environment):

pip install urban-mapper

Then launch Jupyter Lab to explore UrbanMapper:

jupyter lab

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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.

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Licence

UrbanMapper is released under the MIT Licence.

Acknowledgments

This work is supported by the NSF and is part of the OSCUR initiative.