GeoSPM

GeoSPM performs spatial analysis of geographic point data by integrating differential geometry, random field theory, and SPM-like methodologies to enable topological inference of spatially structured effects relevant to health and disease.

Key Features:

• Spatial data analysis: Analyzes diverse geographic point data to model spatially distributed effects.
• Theoretical foundation: Integrates principles from differential geometry and random field theory and employs methodologies akin to statistical parametric mapping (SPM).
• Topological inference: Facilitates topological inference about spatially structured effects with well-behaved spatial dependencies.
• Validation: Validated through extensive synthetic simulations encompassing various spatial configurations, sampling strategies, and noise levels.
• Large-scale data: Demonstrated on large-scale datasets, including analyses of UK Biobank data.
• Statistical criteria: Provides principled criteria for determining statistical significance in spatial analyses.
• Interpretability: Produces interpretable results for spatial patterns and effects.
• Computational performance: Computationally efficient and scalable to large volumes of spatial data.

Scientific Applications:

• Spatial epidemiology and health geography: Characterizes the spatial organization inherent in health and disease characteristics.
• Population-scale spatial analysis: Applies to large cohort datasets such as the UK Biobank for population-level spatial inference.
• Method validation and benchmarking: Uses synthetic simulations to assess robustness across spatial configurations, sampling strategies, and noise levels.

Methodology:

Combines differential geometry, random field theory and methodologies akin to statistical parametric mapping (SPM) to perform topological inference of spatially structured effects and is validated via synthetic simulations covering various spatial configurations, sampling strategies, and noise levels.