Regionalization techniques group spatial areas into a set of homogeneous regions to analyze and draw conclusions about spatial phenomena. A recent regionalization problem, called MP-regions, groups spatial areas to produce a maximum number of regions by enforcing a user-defined constraint at the regional level. The MP-regions problem is NP-hard. Existing approximate algorithms for MP-regions do not scale for large datasets due to their high computational cost and inherently centralized approaches to process data. This article introduces a parallel scalable regionalization framework (
This content will become publicly available on November 13, 2024
- Award ID(s):
- 2237348
- NSF-PAR ID:
- 10494683
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- The 31st ACM International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL '23)
- ISBN:
- 9798400701689
- Page Range / eLocation ID:
- 1 to 12
- Subject(s) / Keyword(s):
- Statistical Inference, Regionalization, Spatial Clustering
- Format(s):
- Medium: X
- Location:
- Hamburg, Germany
- Sponsoring Org:
- National Science Foundation
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