UrbanMapper Community

Enrich Urban Layers Given Urban Datasets

with ease-of-use API and Sklearn-alike Shareable & Reproducible Urban Pipeline

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UrbanMapper & Urban Mapper Community, 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.

About the community-fork: please scroll-down to the #Acknowledgments section below to learn more about the history of the project.

Installation

Install UrbanMapper-Community:

uv add urban-mapper-community
# pip install works too!

Then launch Jupyter Lab to explore UrbanMapper:

uv run jupyter lab

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 — Community-Led Continuation

We are grateful to New York University for supporting the early design and development of UrbanMapper, and for providing an encouraging research environment—especially through the OSCUR funding support (https://oscur.org).

UrbanMapper Community builds on those initial foundations and continues the work as a community-led effort, with a focus on transparent collaboration, reproducible workflows, and open participation as well as public roadmap.

This was unfortunately hardly the case through the first UM repository, questions were hardly answered, issues left, and contributions difficult to make through.