Annals of GIS

Geographical Principles in the New Era of Spatial Analysis and Modeling

Background on the special issue:

Approaches to spatial data analysis and spatial process modeling have continuously evolved from classic spatial statistics to the emerging domain of geospatial artificial intelligence (GeoAI). This evolution has been largely driven by the increasing availability of diverse forms of geographic data (e.g., volunteered geographic information, remote sensing, trajectory, human mobility, Internet of Things (IoT) sensor data, street view images, and many other types), advances in analytical and computational techniques (e.g., deep neural networks and high-performance computing), and, most importantly, new ways of understanding and conceptualizing geographic processes (e.g., The Third Law of Geography). Despite these advances, the fundamental geographical principles, such as location, place, region, movement, spatial interaction, spatial organization, temporal dynamics, and scale, remain essential. Rather than becoming obsolete, spatial is still special, and these geospatial principles continue to serve as enduring foundations that inform and shape emerging approaches in spatial analysis and modeling, alongside new ways of approaching geographic processes.

This special issue invites contributions that look at methodological developments grounded in core geographical principles, spanning from classic frameworks to innovative approaches that leverages spatial analysis and modeling. Submissions may include theoretical, methodological, or applied studies that demonstrate how geographical principles continue to inform contemporary GIScience and GeoAI practices.

Topics include, but are not limited to, the following:

• Spatial analysis and modeling methods grounded in core geographical principles such as location, place, region, movement, spatial interaction, spatial organization, temporal dynamics, and scale
• Theoretical and conceptual examinations of geographical principles in spatial analysis, spatial statistics, and geospatial modeling
• Methodological innovations that integrate classic geographic thinking with emerging approaches such as GeoAI, machine learning, and deep learning
• Spatial interpolation, prediction, and simulation frameworks informed by geographic laws and related principles
• Models that are explicitly concerned with spatial networks and complex geospatial structures in the human-environment geographic systems
• Geography-grounded modeling and analysis using new forms of geographic data, including volunteered geographic information, remote sensing, trajectory data, and other geospatial big data
• Approaches addressing uncertainty, explainability, and interpretability in spatial analysis and modeling, with explicit grounding in geographical principles
• Comparative and critical evaluations of how geographical principles shape the design, interpretation, and performance of spatial analytical methods
• Applied studies demonstrating the continued relevance of geographical principles in real-world spatial analysis and modeling across geographic, environmental, social, urban, and GIScience domains