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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 predetermined region cardinality, while the region merging and region-splitting phases ensure the spatial contiguity of unassigned areas, allowing for the growth of subsequent regions with predetermined cardinalities. An extensive evaluation shows the effectiveness of PRRP using various real datasets.
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This content will become publicly available on August 15, 2026
Formalizing Dependence of Web Infrastructure
Over the past decade, Internet centralization and its implications for privacy, resilience, and innovation have become a topic of active debate. While the networking community informally agrees on the definition of centralization, we lack a formal metric for quantifying it, which has limited in-depth analysis. In this work, we introduce a rigorous statistical metric for Internet centralization. In doing so, we also uncover how regionalization—geopolitical dependence on the Internet—fundamentally affects centralization. We argue that centralization and regionalization are intertwined forms of dependence that both affect the lived experiences of users and should be jointly studied. We develop a suite of statistical tools, which we use to better understand dependence across three layers of web infrastructure—hosting providers, DNS infrastructure, certificate authorities—in 150 countries. We hope that this statistical toolkit can serve as the foundation for future analysis of Internet behavior.
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- Award ID(s):
- 2319080
- PAR ID:
- 10656206
- Publisher / Repository:
- Proceedings of the ACM SIGCOMM 2025 Conference
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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