Geographically Weighted Canonical Correlation Analysis: Local Spatial Associations Between Two Sets of Variables

• URL: http://arxiv.org/abs/2602.10241v1
• Date: Tue, 10 Feb 2026 19:36:49 GMT
• Title: Geographically Weighted Canonical Correlation Analysis: Local Spatial Associations Between Two Sets of Variables
• Authors: Zhenzhi Jiao, Angela Yao, Ran Tao, Jean-Claude Thill,
• Abstract summary: This article critically assesses the utility of the classical statistical technique of Canonical Correlation Analysis (CCA) for studying spatial associations.<n>We propose Geographically Weighted Canonical Correlation Analysis (GWCCA) as a new technique for exploring local spatial associations between two sets of variables.<n>The results indicate that GWCCA has broad potential applications in spatial data-intensive fields such as urban planning, environmental science, public health, and transportation.
• Score: 47.652697094546994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article critically assesses the utility of the classical statistical technique of Canonical Correlation Analysis (CCA) for studying spatial associations and proposes a new approach to enhance it. Unlike bivariate correlation analysis, which focuses on the relationship between two individual variables, CCA investigates associations between two sets of variables by identifying pairs of linear combinations that are maximally correlated. CCA has strong potential for uncovering complex multivariate relationships that vary across geographic space. We propose Geographically Weighted Canonical Correlation Analysis (GWCCA) as a new technique for exploring local spatial associations between two sets of variables. GWCCA localizes standard CCA by weighting each observation according to its spatial distance from a target location, thereby estimating location-specific canonical correlations. The effectiveness of GWCCA in recovering spatial structure and capturing spatial effects is evaluated using synthetic data. A case study of US county-level health outcomes and social determinants of health further demonstrates the empirical capabilities of the proposed method. The results indicate that GWCCA has broad potential applications in spatial data-intensive fields such as urban planning, environmental science, public health, and transportation, where understanding local multivariate spatial associations is critical.

Related papers

• An Interpretable Implicit-Based Approach for Modeling Local Spatial Effects: A Case Study of Global Gross Primary Productivity [9.352810748734157]
  In Earth sciences, unobserved factors exhibit non-stationary distributions, causing the relationships between features and targets to display spatial heterogeneity.<n>In geographic machine learning tasks, conventional statistical learning methods often struggle to capture spatial heterogeneity.<n>We propose a novel perspective - that is, simultaneously modeling common features across different locations alongside spatial differences using deep neural networks.
  arXiv  Detail & Related papers  (2025-02-10T05:44:54Z)
• Combining Observational Data and Language for Species Range Estimation [63.65684199946094]
  We propose a novel approach combining millions of citizen science species observations with textual descriptions from Wikipedia.<n>Our framework maps locations, species, and text descriptions into a common space, enabling zero-shot range estimation from textual descriptions.<n>Our approach also acts as a strong prior when combined with observational data, resulting in more accurate range estimation with less data.
  arXiv  Detail & Related papers  (2024-10-14T17:22:55Z)
• Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning [60.058083574671834]
  This paper presents a novel FCCL+, federated correlation and similarity learning with non-target distillation. For heterogeneous issue, we leverage irrelevant unlabeled public data for communication. For catastrophic forgetting in local updating stage, FCCL+ introduces Federated Non Target Distillation.
  arXiv  Detail & Related papers  (2023-09-28T09:32:27Z)
• DCID: Deep Canonical Information Decomposition [84.59396326810085]
  We consider the problem of identifying the signal shared between two one-dimensional target variables. We propose ICM, an evaluation metric which can be used in the presence of ground-truth labels. We also propose Deep Canonical Information Decomposition (DCID) - a simple, yet effective approach for learning the shared variables.
  arXiv  Detail & Related papers  (2023-06-27T16:59:06Z)
• Multi-Temporal Relationship Inference in Urban Areas [75.86026742632528]
  Finding temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. We propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation learning and further handle the relationship sparsity.
  arXiv  Detail & Related papers  (2023-06-15T07:48:32Z)
• Parallel Reasoning Network for Human-Object Interaction Detection [53.422076419484945]
  We propose a new transformer-based method named Parallel Reasoning Network(PR-Net) PR-Net constructs two independent predictors for instance-level localization and relation-level understanding. Our PR-Net has achieved competitive results on HICO-DET and V-COCO benchmarks.
  arXiv  Detail & Related papers  (2023-01-09T17:00:34Z)
• STICC: A multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity [7.376428009531946]
  An ideal spatial clustering should consider both spatial contiguity and aspatial attributes. Existing spatial clustering approaches may face challenges for discovering repeated geographic patterns with spatial contiguity maintained. We propose a Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both attributes and spatial relationships of geographic objects.
  arXiv  Detail & Related papers  (2022-03-17T20:58:06Z)
• Multi-modality fusion using canonical correlation analysis methods: Application in breast cancer survival prediction from histology and genomics [16.537929113715432]
  We study the use of canonical correlation analysis (CCA) and penalized variants of CCA for the fusion of two modalities. We analytically show that, with known model parameters, posterior mean estimators that jointly use both modalities outperform arbitrary linear mixing of single modality posterior estimators in latent variable prediction.
  arXiv  Detail & Related papers  (2021-11-27T21:18:01Z)
• Conditional canonical correlation estimation based on covariates with random forests [0.0]
  We propose a new method called Random Forest with Canonical Correlation Analysis (RFCCA) to estimate the conditional canonical correlations between two sets of variables. The proposed method and the global significance test is evaluated through simulation studies that show it provides accurate canonical correlation estimations and well-controlled Type-1 error.
  arXiv  Detail & Related papers  (2020-11-23T17:09:46Z)
• Probabilistic Canonical Correlation Analysis for Sparse Count Data [3.1753001245931323]
  Canonical correlation analysis is an important technique for exploring the relationship between two sets of continuous variables. We propose a model-based probabilistic approach for correlation and canonical correlation estimation for two sparse count data sets.
  arXiv  Detail & Related papers  (2020-05-11T02:19:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.