Spatial Statistics
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 114
Special Issue Editors
Interests: Bayesian methods; statistical computing; statistical modeling; spatial statistics; high-dimensional data
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Spatial statistics is a field involving the analysis of data with spatial context, which involves probability, stochastic processes, statistical inference, and computation. Its origins can be traced to mid-20th century when advances in stochastic process theory and geostatistics provided a principled way to quantify spatial dependence and to predict unobserved values from partial measurements. This began with the work of the South African mining engineer D. G. Krige, who developed statistical methods for estimating ore grades from sparse samples. The early applications of spatial statistics included mineral exploration, agriculture, forestry, and meteorology. These areas motivated core ideas such as variograms, kriging, and the modeling of random fields. Spatial point process theory was developed to describe the arrangement of events in space, from ecology to epidemiology. Modern applications of the theory include its use in neuroimaging, where methods for the analysis of spatial point patterns are used for the meta-analysis of brain imaging studies, and in spatial biology where interest lies in the spatial arrangement and interaction of cells in tumor tissue.
Contemporary spatial statistics is central to data science across the environmental, health, biological, social, and engineering sciences, and the field is constantly evolving as it is faced with the practical demands of complex, heterogeneous spatial data. Hierarchical formulations have been integrated with mathematical and biophysical models, but lead to challenges in statistical computation. Recent decades have seen unprecedented increases in the complexity and size of datasets available, which has led to augmented computational demand. Scalability has become a major concern when developing tools for spatial data analysis, and as such breakthroughs that make the analysis of large spatial datasets tractable are increasing.
Contributions to this Special Issue may include interesting reviews, applied works and studies of theoretical properties. The adopted approach for inference could be frequentist, Bayesian or any interesting variants of the aforementioned approaches. Furthermore, using data science and machine learning for spatial analyses are particularly welcomed.
Dr. Farouk Nathoo
Prof. Dr. Dani Gamerman
Guest Editors
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Keywords
- geostatistics
- areal data
- point pattern
- stationarity
- spatiotemporal models
- spatial and temporal prediction
- machine learning
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