RegionContext

RegionContext is a Python-based project designed to analyze and cluster Points of Interest (POIs) based on spatial and contextual similarities. Utilizing advanced machine learning techniques, including SpaBERT and KMeans clustering, the project aims to provide insights into regional patterns and relationships among POIs.

Features

• SpaBERT Prediction: Leverages the SpaBERT model to predict contextual embeddings for POIs based on their descriptions and categories.
• Autoencoder Dimensionality Reduction: Applies an autoencoder to reduce the dimensionality of the contextual embeddings, facilitating more efficient clustering.
• KMeans Clustering: Implements KMeans clustering to group POIs into clusters based on their reduced-dimensional embeddings, allowing for the analysis of regional similarities and differences.
• Flexible Data Handling: Supports input and output in various formats, including CSV and JSON, for easy integration with other tools and workflows.

Getting Started

   conda create --name=regioncontext python=3.10
   conda activate regioncontext

   git clone https://github.com/knowledge-computing/regioncontext.git
   cd regioncontext
   pip install -r requirements.txt

   export PYTHONPATH=$PYTHONPATH:./src

Paper & BibTeX Citation

For more details, see the paper: Region Context from Unifying Points, Lines, and Polygons

If you find this work useful, please cite it using the following BibTeX entry:

   @inproceedings{10.1145/3764926.3771941,
   author = {Kim, Jina and Chiang, Yao-Yi},
   title = {Region Context from Unifying Points, Lines, and Polygons},
   year = {2025},
   isbn = {9798400721892},
   publisher = {Association for Computing Machinery},
   address = {New York, NY, USA},
   url = {https://doi.org/10.1145/3764926.3771941},
   doi = {10.1145/3764926.3771941},
   booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Urban-AI},
   pages = {94–95},
   numpages = {2},
   keywords = {urban foundation models, region contextualization, spatial semantics},
   location = {Minneapolis, MN, USA},
   series = {UrbanAI '25}
   }