{
  "abstract": "Regular-grid spatial functional datasets arise naturally in gridded environmental, oceanographic, climate, and remote-sensing applications, where each spatial location is associated with an entire curve. Existing spectral spatial functional principal component analysis methods provide an important frequency-domain approach for such data, but they do not directly target finite-neighbourhood least-squares reconstruction from an estimated latent spatial component field. To address this, we propose Spatial Functional Generalized Dynamic Principal Components (SFGDPC), a reconstruction-based dimension-reduction method for regular-grid spatial functional data. Each function is first represented by basis coefficients, and each coefficient vector is reconstructed from a scalar latent spatial field and its Chebyshev neighbourhood on the rectangular grid. The spatial neighbourhood radius is selected using a Bayesian information criterion (BIC)-type conditional reconstruction criterion. In the local-neighbourhood transfer simulation, SFGDPC reduced mean cumulative normalized mean squared error (NMSE) relative to spatial functional principal component analysis (SFPCA) by approximately 38-53% across the reported component counts and covariance conditions. In the Indian Ocean sea surface temperature (SST) application, one SFGDPC component produced lower whole-grid reconstruction error than both the boundary-safe and high-cap SFPCA benchmarks across all 33 annual fields. The results support SFGDPC as a local reconstruction-based complement to spectral SFPCA for regular-grid spatial functional data.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.14986",
  "author_total": 4,
  "authors": [
    {
      "name": "Dharini Pathmanathan"
    },
    {
      "name": "Teck Xiang Seow"
    },
    {
      "name": "Philipp Otto"
    },
    {
      "name": "Sophie Dabo-Niang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6jb98bbay90wr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.14986",
  "fulltext_url": null,
  "id": "6jb98bbay90wr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.14986",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Advanced Statistical Methods and Models"
    ]
  ],
  "openalex": "W7213264670",
  "pdf_url": "https://arxiv.org/pdf/2609.14986",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6jb98bbay90wr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:36Z",
      "native_id": "2609.14986",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.14986"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Neighbourhood-Based Generalized Dynamic Principal Components for Spatial Functional Data",
  "title_text": "Neighbourhood-Based Generalized Dynamic Principal Components for Spatial Functional Data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:36Z",
  "url": "https://research.draconitic.com/paper/6jb98bbay90wr",
  "venue": "arXiv",
  "versions": []
}