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Mathematics
Special Issues
Spatial Statistics Methods and Modeling
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Spatial Statistics Methods and Modeling

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: closed (20 March 2025) | Viewed by 788

Special Issue Editor

Dr. Frank Badu Osei

E-Mail Website
Guest Editor
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands
Interests: spatial (bio) statistics; spatial epidemiology; air quality mapping; spatial biostatistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advent of Global Positioning System (GPS) technology and Geographic Information Science (GIS), spatial data are increasingly becoming easy to collect, store, and analyze. Spatial data may be observations of the locations of event occurrences (point processes), observations of aggregated quantities over a set of contiguous areas (lattice processes), or observations of quantities over a set of locations (geostatistical processes). Today, spatial data have rapidly become abundant in precision public health and disease modeling, environmental science monitoring, precision agriculture, and diverse other domains. The modeling of these spatial data processes presents unique perspectives about how inferences about the phenomenon under study can be drawn. This Special Issue aims to bring to studies that apply, innovate, or extend existing spatial modeling methods in public health, environmental science, agriculture, etc. We shall consider manuscripts that present innovative solutions to application and methodological challenges to solve real societal problems.

Dr. Frank Badu Osei
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
  • spatial modeling
  • geostatistics
  • Markov random field
  • Gaussian Markov random field
  • conditional autoregressive
  • point processes
  • log Gaussian Cox process

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Published Papers (1 paper)

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Research

13 pages, 264 KiB  
Article
Parameter Estimation of Geographically and Temporally Weighted Elastic Net Ordinal Logistic Regression
by Margaretha Ohyver, Purhadi and Achmad Choiruddin
Mathematics 2025, 13(8), 1345; https://doi.org/10.3390/math13081345 - 19 Apr 2025
Viewed by 157
Abstract
Geographically and Temporally Weighted Elastic Net Ordinal Logistic Regression is a parsimonious ordinal logistic regression with consideration of the existence of spatial and temporal effects. This model has been developed with the following three considerations: the spatial effect, the temporal effect, and predictor [...] Read more.
Geographically and Temporally Weighted Elastic Net Ordinal Logistic Regression is a parsimonious ordinal logistic regression with consideration of the existence of spatial and temporal effects. This model has been developed with the following three considerations: the spatial effect, the temporal effect, and predictor selection. The last point prompted the use of Elastic Net regularization in choosing predictors while handling multicollinearity, which often arises when there are many predictors involved. The Elastic Net penalty combines ridge and LASSO penalties, leading to the determination of the appropriate λEN and αEN. Therefore, the objective of this study is to determine the parameter estimator using Maximum Likelihood Estimation. The estimation process comprises defining the likelihood function, determining the natural logarithm of the likelihood function, and maximizing the function using Berndt–Hall–Hall–Hausman. These steps continue until the estimator converges on the values that maximize the likelihood function. This study contributes by developing an estimation framework that integrates spatial and temporal effects with Elastic Net regularization, allowing for improved model interpretation and stability. The findings provide an advanced methodological approach for ordinal logistic regression models that incorporate spatial and temporal dependencies. This framework is particularly useful for applications in fields such as economic forecasting, epidemiology, and environmental studies, where ordinal responses exhibit spatial and temporal patterns. Full article
(This article belongs to the Special Issue Spatial Statistics Methods and Modeling)
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