Pyneapple-R: Scalable and Expressive Spatial Regionalization

This paper demonstrates Pyneapple-R, an open-source library for scalable and expressive regionalization. Re-gionalization algorithms, also known as the ‘spatially-constrained clustering algorithms', have been widely adopted in spatial analysis tasks and now evolving towards a more large-scale and fine-scale direction. Through collaborations with social scientists and domain experts, we have identified emerging challenges in existing regionalization techniques, particularly regarding scalability and expressiveness. As data volumes continue to grow and regionalization algorithms become increasingly crucial to decision-making across various fields, enhancing these aspects can significantly impact the quality and effectiveness of re-search and applications. To address these challenges, Pyneapple-R provides novel algorithms for regionalization queries including the expressive p-regions algorithm, the scalable max-p regions algorithm, and the expressive max-p regions problem. To show-case Pyneapple-R, we have developed frontend web applications that enable users to interact with the algorithms by selecting constraints or simply engaging in conversation with the system to issue queries with the help of popular AI models. Interactive notebooks, designed to demonstrate the superiority and simplicity of Pyneapple-R, provide varying levels of detail to help social scientists and developers explore its full potential.