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Mathematics
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Advances in Geostatistics and Spatial Statistics: Methods and Applications
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Advances in Geostatistics and Spatial Statistics: Methods and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 4000

Special Issue Editor

Dr. Tholang Mokhele

E-Mail Website
Guest Editor
Geospatial Analytics, eResearch Knowledge Centre, Human Sciences Research Council, Pretoria, South Africa
Interests: GIS; spatial statistics; spatial analysis; geostatistics; spatio-temporal modelling; accessibility modelling; data analysis; demographics; small areas estimation; automated zone design and spatial sampling techniques

Special Issue Information

Dear Colleagues,

Geostatistics and spatial statistics have been increasingly applied in social, health, physical, agricultural and environmental sciences in both developing and developed countries. Geostatistics and spatial statistics techniques involve statistical concepts and applications to geospatial data, which allow solutions of location-based complex problems and geotargeted interventions in various fields. This Special Issue focuses on the applications of current advances in geostatistics and spatial statistics. It is therefore anticipated that this Special Issue will provide a conducive environment for scientists, researchers, academics and other professionals to showcase their latest methods and applications in the fields of geostatistics and spatial statistics. This will help to promote future research in these growing fields of geostatistics and spatial statistics. Therefore, contributions of original and high-quality research papers are cordially invited.

Dr. Tholang Mokhele
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • spatial statistics
  • geostatistics
  • spatio-temporal modelling
  • geographically weighted regression
  • small area estimation
  • spatial dependence
  • automated zone design
  • accessibility modelling
  • spatial uncertainty
  • stochastic modelling
  • spatial autocorrelation

Published Papers (4 papers)

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Research

17 pages, 10883 KiB  
Article
A Multi-Point Geostatistical Modeling Method Based on 2D Training Image Partition Simulation
by Yifei Zhao, Jianhong Chen, Shan Yang, Kang He, Hideki Shimada and Takashi Sasaoka
Mathematics 2023, 11(24), 4900; https://doi.org/10.3390/math11244900 - 7 Dec 2023
Cited by 1 | Viewed by 808
Abstract
In this paper, a multi-point geostatistical (MPS) method based on variational function partition simulation is proposed to solve the key problem of MPS 3D modeling using 2D training images. The new method uses the FILTERSIM algorithm framework, and the variational function is used [...] Read more.
In this paper, a multi-point geostatistical (MPS) method based on variational function partition simulation is proposed to solve the key problem of MPS 3D modeling using 2D training images. The new method uses the FILTERSIM algorithm framework, and the variational function is used to construct simulation partitions and training image sequences, and only a small number of training images close to the unknown nodes are used in the partition simulation to participate in the MPS simulation. To enhance the reliability, a new covariance filter is also designed to capture the diverse features of the training patterns and allow the filter to downsize the training patterns from any direction; in addition, an information entropy method is used to reconstruct the whole 3D space by selecting the global optimal solution from several locally similar training patterns. The stability and applicability of the new method in complex geological modeling are demonstrated by analyzing the parameter sensitivity and algorithm performance. A geological model of a uranium deposit is simulated to test the pumping of five reserved drill holes, and the results show that the accuracy of the simulation results of the new method is improved by 11.36% compared with the traditional MPS method. Full article
(This article belongs to the Special Issue Advances in Geostatistics and Spatial Statistics: Methods and Applications)
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11 pages, 1225 KiB  
Article
Modulations of Stochastic Modeling in the Structural and Energy Aspects of the Kundu–Mukherjee–Naskar System
by Emad K. El-Shewy, Noura F. Abdo and Mahmoud A. E. Abdelrahman
Mathematics 2023, 11(24), 4881; https://doi.org/10.3390/math11244881 - 6 Dec 2023
Viewed by 804
Abstract
By using stochastic modeling, the investigation of the energy and wave characteristics of novel structures that develop in the sea and ocean currents becomes one of the most important advancements in the generation of sustainable and renewable energy. Theoretical examinations of random nonlinear [...] Read more.
By using stochastic modeling, the investigation of the energy and wave characteristics of novel structures that develop in the sea and ocean currents becomes one of the most important advancements in the generation of sustainable and renewable energy. Theoretical examinations of random nonlinear Kundu–Mukherjee–Naskar (RNKMN) structures have become recommended in a random mode. The two-dimensional RNKMN equation permits exact and solved solutions that give rise to solitonic structures with adaptable properties. The obtained stochastic waves, under the influence of random water currents, represent a dynamically controlled system. It has been demonstrated that the stochastic parameter modulates wave forcing and produces energy wave collapse accompanied by medium turbulence. The fundamental wave characteristics establish an exact pattern for describing sea and ocean waves. Full article
(This article belongs to the Special Issue Advances in Geostatistics and Spatial Statistics: Methods and Applications)
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17 pages, 570 KiB  
Article
A Hyperbolic Secant-Squared Distribution via the Nonlinear Evolution Equation and Its Application
by Amira F. Daghistani, Ahmed M. T. Abd El-Bar, Ahmed M. Gemeay, Mahmoud A. E. Abdelrahman and Samia Z. Hassan
Mathematics 2023, 11(20), 4270; https://doi.org/10.3390/math11204270 - 13 Oct 2023
Viewed by 838
Abstract
In this article, we present a hyperbolic secant-squared distribution via the nonlinear evolution equation. Namely, for this equation, the probability density function of the hyperbolic secant-squared (HSS) distribution has been determined. The density of our model has a variety of shapes, including symmetric, [...] Read more.
In this article, we present a hyperbolic secant-squared distribution via the nonlinear evolution equation. Namely, for this equation, the probability density function of the hyperbolic secant-squared (HSS) distribution has been determined. The density of our model has a variety of shapes, including symmetric, left-skewed, and right-skewed. Eight distinct frequent list estimation methods have been proposed for estimating the parameters of our models. Additionally, these estimation techniques have been used to examine the behavior of the HSS model parameters using data sets that were generated randomly. To demonstrate how the findings may be used to model real data using the HSS distribution, we also use real data. Finally, the proposed justification can be applied to a variety of other complex physical models. Full article
(This article belongs to the Special Issue Advances in Geostatistics and Spatial Statistics: Methods and Applications)
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15 pages, 783 KiB  
Article
Spatially Dependent Bayesian Modeling of Geostatistics Data and Its Application for Tuberculosis (TB) in China
by Zongyuan Xia, Bo Tang, Long Qin, Huiguo Zhang and Xijian Hu
Mathematics 2023, 11(19), 4193; https://doi.org/10.3390/math11194193 - 7 Oct 2023
Cited by 1 | Viewed by 911
Abstract
Geostatistics data in regions always have highly spatial heterogeneous, yet the regional features of the data itself cannot be ignored. In this paper, a novel latent Bayesian spatial model is proposed, which incorporates the spatial dependence of different subjects and the spatial random [...] Read more.
Geostatistics data in regions always have highly spatial heterogeneous, yet the regional features of the data itself cannot be ignored. In this paper, a novel latent Bayesian spatial model is proposed, which incorporates the spatial dependence of different subjects and the spatial random effects to further analysis the influence of spatial effect. The model is verified to be compatible with the integrated nested Laplace approximation (INLA) framework and is fitted using INLA and stochastic partial differential equation (SPDE). The posterior marginal distribution of parameters is estimated with high precision. Additionally, a practical application of the model is presented using tuberculosis (TB) incidence data in China from 2015 to 2019. The results show that the fitting accuracy of our model is higher than the general Bayesian spatial model using INLA-SPDE. Full article
(This article belongs to the Special Issue Advances in Geostatistics and Spatial Statistics: Methods and Applications)
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