Non-Euclidean data, Graph Neural Networks (GNNs), Spatio-temporal data, Geographic Information Systems (GIS)

Abbasi, Mohammad Taghi; Alesheikh, Ali Asghar

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Volume 14, Issue 4 (6-2025)

JGST 2025, 14(4): 91-107

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Non-Euclidean data, Graph Neural Networks (GNNs), Spatio-temporal data, Geographic Information Systems (GIS)

Mohammad Taghi Abbasi

, Ali Asghar Alesheikh ^*

Abstract: (76 Views)

The geometric structure of data plays a vital role in selecting analytical methods for processing and modeling. In Euclidean data such as one-dimensional signals and two-dimensional images, concepts such as distance, order, and adjacency are precisely defined. In contrast, in many real-world phenomena, data are often modeled on non-Euclidean domains such as graphs, where concepts like distance and direction are not precisely defined, and topological relationships and node connections form the basis of analysis. Convolutional Neural Networks (CNNs) are specifically designed for Euclidean data with regular, grid-like structures, and they have limitations in processing irregularly structured data such as graphs, where notions like distance and direction lack precise definitions. Therefore, Graph Neural Networks (GNNs) have been introduced as effective tools for modeling and analyzing non-Euclidean data. Spatio-temporal non-Euclidean data are typically modeled as graphs, where spatial and temporal variations are interpreted as representations of dynamic relationships among nodes. GNNs effectively extract spatially varying features from node and edge feature vectors and, by combining them with sequential learning models, also extract temporal features. These models can simultaneously analyze complex and nonlinear dependencies in spatial and temporal dimensions. In this study, the conceptual and mathematical transition from classical convolutional networks to Graph Convolutional Networks (GCNs) is examined, with a particular focus on the capabilities of these advanced models in processing spatio-temporal data in Geographic Information Systems (GIS). Additionally, a public repository has been created on GitHub, containing freely available datasets for implementing these applications, which will be continuously updated.

Article number: 7

Keywords: Graph Neural Networks (GNNs), Graph Data Analysis, Complex Dependencies, Geographic Information Systems (GIS)

Full-Text [PDF 1136 kb] (296 Downloads)

Type of Study: Research | Subject: GIS
Received: 2025/04/8

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Abbasi M T, Alesheikh A A. Non-Euclidean data, Graph Neural Networks (GNNs), Spatio-temporal data, Geographic Information Systems (GIS). JGST 2025; 14 (4) : 7
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