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.

The process of regionalization involves clustering a set of spatial areas into spatially contiguous regions. Given the NP-hard nature of regionalization problems, all existing algorithms yield approximate solutions. To ascertain the quality of these approximations, it is crucial for domain experts to obtain statistically significant evidence on optimizing the objective function, in comparison to a random reference distribution derived from all potential sample solutions. In this paper, we propose a novel spatial regionalization problem, denoted as SISR (Statistical Inference for Spatial Regionalization), which generates random sample solutions with a predetermined region cardinality. The driving motivation behind SISR is to conduct statistical inference on any given regionalization scheme. To address SISR, we present a parallel technique named PRRP (P-Regionalization through Recursive Partitioning). PRRP operates over three phases: the region growing phase constructs initial regions with a predefined cardinality, while the region merging and region splitting phases ensure the spatial contiguity of unassigned areas, allowing for the growth of subsequent regions with predefined cardinalites. An extensive evaluation shows the effectiveness of PRRP using various real datasets.

Spatial regionalization is the process of combining a collection of spatial polygons into contiguous regions that satisfy user-defined criteria and objectives. Numerous techniques for spatial regionalization have been proposed in the literature, which employ varying methods for region growing, seeding, optimization and enforce different user-defined constraints and objectives. This paper introduces a scalable unified system for addressing seeding spatial regionalization queries efficiently. The proposed system provides a usable and scalable framework that employs a wide-range of existing spatial regionalization techniques and allows users to submit novel combinations of queries that have not been previously explored. This represents a significant step forward in the field of spatial regionalization as it provides a robust platform for addressing different regionalization queries. The system is mainly composed of three components: query parser, query planner, and query executor. Preliminary evaluations of the system demonstrate its efficacy in efficiently addressing various regionalization queries.

MP-regions is an NP-hard problem that groups spatial areas to produce a maximum number of regions by enforcing a user-defined constraint at the regional level. Existing approximate algorithms for MP-regions do not scale for large datasets due to their high computational cost. This paper introduces SMP; a scalable technique to support MP-regions on large datasets. SMP works on two stages. The first stage finds an initial solution through randomized search, and the second stage improves this solution through efficient heuristic search. SMP optimizes the region building efficiency and quality by tuning the randomized area selection to trade-off runtime with region homogeneity. The experimental evaluation shows the superiority of our technique to support an order of magnitude larger datasets efficiently compared to the state-of-the-art techniques while producing high-quality solutions.

Mobility data is being produced in massive amounts way larger than any time in history due to the widespread of new mobile data sources such as mobile smart devices and Internet of Things (IoT) devices. This adds new challenges to analyze data in both space and time. Spatial regionalization is a process of grouping spatial areas into a set of regions to analyze and draw conclusions about spatial phenomena at different points of time. Almost all the existing regionalization techniques develop approximate solutions that are limited to small-sized data. However, the massive amount of data being produced nowadays calls for scalable and efficient regionalization techniques to handle the contagiously growing and changing data. Moreover, using approximation techniques requires having some kind of measure to assess the quality of those techniques. This Ph. D research mainly addresses the scalability issue of spatial regionalization techniques to explore new frontiers and applications. First, we introduce parallel scalable techniques to support regionalization techniques and measure their quality on large datasets. Then, we build upon our scalable techniques to support high-level spatial inference applications that are not possible with the existing limitations. We conduct extensive experiments to evaluate the performance of our proposed techniques in terms of runtime and solution quality.