Computational and Machine Learning Methods in Spatial Statistical Modeling
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 21
Special Issue Editors
Interests: spatial statistics; data science; machine learning; probability theory
Special Issue Information
Dear Colleagues,
We are pleased to announce this Special Issue of the journal Mathematics entitled “Computational and Machine Learning Methods in Spatial Statistical Modeling.” This Issue is intended to provide the latest development of computational and machine learning approaches in the context of spatial statistical modeling. It focuses on recent progress in learning techniques for spatial and spatiotemporal data, advances in computational methods, and practical use across scientific fields where location-based data play an essential role. The issue encourages contributions that bring new ideas to problems in public health, environmental studies, agriculture, and related areas where spatial reasoning is key.
The main topics of this Issue include, but are not limited to, the following: data-driven methods for spatial analysis, Bayesian approaches for spatial modeling, Generalized Linear Models and Autoregressive Models, predictive models using deep learning, applications of Gaussian processes for spatial estimation, statistical analysis of spatial point patterns, cluster detection and spatial scan statistics, kriging and geostatistical interpolation techniques, combining information from diverse spatial sources, evaluation of uncertainty in spatial predictions, and applied research that shows how these tools are used in practice.
Dr. Mohammad Meysami
Dr. Ali Lotfi
Guest Editors
Manuscript Submission Information
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Keywords
- spatial statistical modeling
- machine learning for spatial data
- spatiotemporal analysis
- Bayesian spatial models
- deep learning for spatial prediction
- Gaussian processes
- kriging and geostatistical interpolation
- spatial point pattern analysis
- cluster detection
- autoregressive models
- data fusion from multiple spatial sources
- spatial uncertainty quantification
- applied geospatial data science
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