Releases · VIDA-NYU/UrbanMapper

Hi folks!

We are pleased to announce that UrbanMapper is now available in its first public release under the tag 0.1.2 🎉

Note

Why not 0.1.0 ? Bear with us! We had to do very slight patches from 0.1.0 such as documentation improvement, PyPI Readme adjustments, and stuff like this.

📽️ Urban Mapper is built on @openstreetmap through OSMNX (By @gboeing and team), @Tile2Net (By @Mary-h86 and team), features-enhanced by @geojupyter through @JupyterGIS (By @QuantStack maintained by @mfisher87 and teams @SchmidtDSE), by @huggingface through @datasets, by @skrub-data through @Skrub (By @probabl-ai and team), among many other such cool tools and features! But really, what does it do? 👇👇👇

💡 Urban Mapper presents an innovative GIS-based library for deterministic (as of now) urban analysis, aimed at streamlining the process of collecting urban layers from around the globe and facilitating the downloading of spatial datasets from the web and all open data from major cities worldwide. And what's next? The goal is to then allow, with ease of use, to spatially join these datasets, compute statistics, and apply them to your urban layer, resulting in a comprehensive urban analysis as a geo-dataframe. Sounds easy, right? This is thanks to the introduction of an urban pipeline, akin to a Scikit-Learn pipeline, enabling users to sequentially apply various urban analysis steps together in a compact format. From utilising transformers for missing geo-coordinates and filtering techniques to employing enrichers for statistical insights, the urban pipeline allows for the saving of urban analyses in a manner similar to saving a trained sklearn pipeline-based model. It promotes reproducibility, ease of loading, modification, and the ability to add or remove components from a given urban analysis at any time, without having to read tweaked-based and manual code.

There are numerous additional features that, while not central to Urban Mapper, warrant thoughtful consideration. For instance, after conducting an urban analysis with the urban pipeline introduced above, why not export and import it into JupyterGIS for a collaborative real-time session with urban planners in Jupyter? Another one is related to the big LLM trends; why not consider utilising LLM to generate your own analysis urban pipeline based on a specific urban layer and dataset of interest? This is all made possible by @Ell from @MadcowD. Last one? All right! Another one may allow you to avoid having to have locally available spatial datasets to play with and instead, for instance, simply request an urban layer of interest and a dataset available in the HuggingFace datasets hubs that we download for you. This is all made possible by HuggingFace datasets from @huggingface.

More is coming; explore our GitHub issue and read through our README and our documentation!

Please share your suggestions! Without external suggestions, how can we be sure we help some GIS workflows out there? 👀 So far, a few have already been made by external contributors to @VIDA-NYU, where this project originated. For example:

@hunarjain09 from @facebook suggested enhancing the presentation of examples in the Getting Started section by possibly creating a new tab for examples. This would allow users to easily navigate through all the example notebooks we have produced directly from the documentation (i.e. the notebooks would be already run through, allowing anyone to simply look at the output and navigate the various maps Urban Mapper produces). For further details, refer to #53. Very initial suggestion goes to @mfisher87.
@MohanadDiab from @POLIMI (Off. Website) & the GEO-GIS Lab has agreed to try to find one of their Master's students to assist in integrating Mohana's LangRS as a new urban layer's primitive. For more details, see #52.

From within the lab @VIDA-NYU, many things are being cooked on by @soniacq and others! Stay tuned!

🫵
https://pypi.org/project/urban-mapper/

[v0.1.2] - 2025 – May – 22nd - First Public Release

Added

HuggingFace Datasets integration with Urban Mapper, amazing work by @soniacq – #45, #49
LLM-generator of Urban Pipeline – #37
Custom Urban Layer addition from dataframe – #36
Copyright Update From @simonprovost to @nyu, requested by @ctsilva – #34
Pipeline Viz. enhancement with progress-bar for long-pipeline execution, kindly proposed by @fabiofelix – #33
Documentation & Readme – #29, #30, #31, #32, #39, #43 (Work by @ctsilva & @simonprovost), #46, #50, #57 (work by @soniacq)
Enricher enhancement with support for reporting indices used while augmenting datasets to urban layers, requested by @soniacq's work – #25
Pipeline Better Integration from Joblib to Dill, requested by @fabiofelix's work – #24
JupyterGIS integration & support, thanks to @mfisher87 for great support with such integr. – #22, #25
Enricher Module Enhancement with custom lambda function, requested by @fabiofelix's work – #21
Continuous Integration with compilation verification – #20
Introduction of numerous Examples & Study Cases – #14, #16, #19, #25, #50
Introduction of Chaining-Based API style – #12 #13
Integration of @VIDA-NYU Auctus By @soniacq @remram44 @julianafreire and team via Auctus Search – #2, #20
Introduction of Loader, Urban layer, Geo Imputer, Geo Filter, Pipeline & Visualiser internal modules from a long discussion over a whiteboard with @joaorulff where the library initiated – Commits.