urban-mapper

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!

https://github.com/vida-nyu/urbanmapper

Science Score: 52.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
○
DOI references
○
Academic publication links
○
Committers with academic emails
✓
Institutional organization owner
Organization vida-nyu has institutional domain (vida.engineering.nyu.edu)
○
JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (13.0%) to scientific vocabulary

Keywords

spatial-data urban-computing urban-pipeline urban-planning urbanmappers

Last synced: 6 months ago · JSON representation ·

Repository

Basic Info

Host: GitHub
Owner: VIDA-NYU
License: mit
Language: Python
Default Branch: main
Homepage: https://urbanmapper.readthedocs.io/en/latest/
Size: 27.9 MB

Statistics

Stars: 55
Watchers: 7
Forks: 4
Open Issues: 10
Releases: 1

Topics

spatial-data urban-computing urban-pipeline urban-planning urbanmappers

Created about 1 year ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Citation Security

README.md

UrbanMapper

Enrich Urban Layers Given Urban Datasets

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

UrbanMapper Cover

[!IMPORTANT] - 📹 UrbanMapper Introductory Video 👉 https://www.youtube.com/watch?v=QUmfvda_z2U 👈 - 🤝 We support JupyterGIS following one of your Urban Pipeline's analysis for collaborative in real-time exploration on Jupyter 🏂 Shout-out to @mfisher87 and JGIS team 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):

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 instructions—including how to install within a virtual environment using uv, conda or from source in the UrbanMapper Installation Guide.

Urban Layers Currently Supported

UrbanMapper currently supports loading the following urban layers:

Street networks (roads and intersections) from OpenStreetMap via OSMNx
Pedestrian infrastructure (sidewalks and crosswalks) via Tile2Net ( automated mapping from aerial imagery)
City features (buildings, parks, bike lanes, etc.) from OpenStreetMap via OSMNx
Administrative boundaries (neighborhoods, cities, states, countries) from OpenStreetMap via OSMNx

More layers, like subway/tube networks, will be added in the future. If you have any suggestions, please feel free to open an issue or a pull request!

🚀 Getting Started with UrbanMapper

Are you ready to dive into urban data analysis in a couple of lines of code? The simplest approach to get started with UrbanMapper is to look through the two getting-started examples available in the documentation then walk through the hands-on examples in the examples/ directory. Documentation is available at UrbanMapper Documentation.

Licence

UrbanMapper is released under the MIT Licence.

Acknowledgments

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

Owner

Name: VIDA-NYU
Login: VIDA-NYU
Kind: organization
Location: New York, NY

Website: https://vida.engineering.nyu.edu/
Twitter: nyuvida
Repositories: 92
Profile: https://github.com/VIDA-NYU

Visualization, Imaging, and Data Analysis Center at New York University

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this library, please cite it as below 👇"
authors:
- family-names: "Provost"
  given-names: "Simon"
  orcid: "https://orcid.org/0000-0001-8402-5464"
- family-names: "Rulff"
  given-names: "Joao"
  orcid: "https://orcid.org/0000-0003-3341-7059"
- family-names: "Castelo Quispe"
  given-names: "Sonia"
  orcid: "https://orcid.org/0000-0001-6881-3006"
- family-names: "Felix Dias"
  given-names: "Fabio"
  orcid: "https://orcid.org/0000-0003-2081-1233"
- family-names: "Freire"
  given-names: "Juliana"
  orcid: "https://orcid.org/0000-0003-3915-7075"
- family-names: "T. Silva"
  given-names: "Claudio"
  orcid: "https://orcid.org/0000-0003-2452-2295"
title: "🗺️ 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!"
version: 0.0.1
date-released: 2025-04-27
url: "https://github.com/VIDA-NYU/UrbanMapper"

GitHub Events

Total

Create event: 41
Commit comment event: 2
Issues event: 36
Watch event: 45
Delete event: 39
Member event: 1
Issue comment event: 86
Push event: 166
Pull request review event: 97
Pull request review comment event: 94
Pull request event: 59
Fork event: 3

Last Year

Create event: 41
Commit comment event: 2
Issues event: 36
Watch event: 45
Delete event: 39
Member event: 1
Issue comment event: 86
Push event: 166
Pull request review event: 97
Pull request review comment event: 94
Pull request event: 59
Fork event: 3

Committers

Last synced: 8 months ago

All Time

Total Commits: 141
Total Committers: 2
Avg Commits per committer: 70.5
Development Distribution Score (DDS): 0.064

Past Year

Commits: 141
Committers: 2
Avg Commits per committer: 70.5
Development Distribution Score (DDS): 0.064

Top Committers

Name	Email	Commits
Provost Simon	s**t@e**u	132
sonia	s**2@g**m	9

Committer Domains (Top 20 + Academic)

epitech.eu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 51
Total pull requests: 75
Average time to close issues: about 1 month
Average time to close pull requests: 1 day
Total issue authors: 3
Total pull request authors: 4
Average comments per issue: 2.31
Average comments per pull request: 0.56
Merged pull requests: 71
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 51
Pull requests: 75
Average time to close issues: about 1 month
Average time to close pull requests: 1 day
Issue authors: 3
Pull request authors: 4
Average comments per issue: 2.31
Average comments per pull request: 0.56
Merged pull requests: 71
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

simonprovost (23)
soniacq (4)
fabiofelix (1)

Pull Request Authors

simonprovost (56)
soniacq (7)
ctsilva (4)
fabiofelix (2)

Top Labels

Issue Labels

enhancement (21) loader (8) help wanted (6) enricher (6) preprocessing (6) documentation (5) urban_layer (4) pipeline (3) visualiser (2) examples (1) bug (1)

Pull Request Labels

documentation (37) enhancement (35) bug (10) pipeline (10) loader (6) urban_layer (5) enricher (5) good first issue (4) fix (3) examples (2) study (2) network (2) visualiser (2) not planned (1) preprocessing (1)

Packages

Total packages: 3
Total downloads:
- pypi 133 last-month

Total dependent packages: 0
(may contain duplicates)
Total dependent repositories: 0
(may contain duplicates)
Total versions: 15
Total maintainers: 1

proxy.golang.org: github.com/vida-nyu/urbanmapper

Documentation: https://pkg.go.dev/github.com/vida-nyu/urbanmapper#section-documentation
License: mit
Latest release: v0.1.4
published 6 months ago

Versions: 5
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.4%

Dependent repos count: 5.7%

Stargazers count: 6.9%

Average: 7.5%

Forks count: 12.2%

Last synced: 6 months ago

proxy.golang.org: github.com/VIDA-NYU/UrbanMapper

Documentation: https://pkg.go.dev/github.com/VIDA-NYU/UrbanMapper#section-documentation
License: mit
Latest release: v0.1.4
published 6 months ago

Versions: 5
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.4%

Dependent repos count: 5.7%

Stargazers count: 6.9%

Average: 7.5%

Forks count: 12.2%

Last synced: 6 months ago

pypi.org: urban-mapper

🗺️ 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!

Homepage: https://github.com/VIDA-NYU/UrbanMapper
Documentation: https://urbanmapper.readthedocs.io/en/latest/
License: MIT
Latest release: 0.1.4
published 6 months ago

Versions: 5
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 133 Last month

Rankings

Dependent packages count: 9.1%

Stargazers count: 12.4%

Forks count: 24.3%

Average: 24.3%

Dependent repos count: 51.3%

Maintainers (1)

SimonProvost

Last synced: 6 months ago

Dependencies

pyproject.toml pypi

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