Predicting spatial spread of rabies in skunk populations using surveillance data reported by the public. by Kim M Pepin, Amy J Davis, Daniel G Streicker, Justin W Fischer, Kurt C VerCauteren, Amy T Gilbert

“…We developed a dynamic patch-occupancy model which predicts spatio-temporal spreading while accounting for heterogeneous sampling. …”
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Transformer based spatially resolved prediction of mechanical properties in wire arc additive manufacturing by Mohammad Keshmiri, Shirin Dehgahi, Abdullah Mohiuddin, Ahmed J. Qureshi

“…The results demonstrate that the framework achieves good prediction capabilities using a small dataset. It provides a state-of-the-art methodology for predicting the spatial and temporal evolution of mechanical properties leveraging the transformer architecture. …”
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Data Fusion and Dimensionality Reduction for Pest Management in Pitahaya Cultivation by Wilson Chango, Mónica Mazón-Fierro, Juan Erazo, Guido Mazón-Fierro, Santiago Logroño, Pedro Peñafiel, Jaime Sayago

“…First, early fusion better captures cross-domain interactions before dimensionality reduction, improving prediction robustness. Second, KPCA-poly offers an effective non-linear mapping suitable for tropical agroecosystem complexity. …”
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Modeling spatial distributions of Amah Mutsun priority cultural plants to support Indigenous cultural revitalization by Annalise Taylor, Alexii Sigona, Maggi Kelly

“…We utilized community science datasets with an ensemble modeling approach that combines the results of five machine learning models to predict not only the distribution of each species, but also the relative certainty of those predictions spatially. …”
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Predicting the effect of landscape structure on epidemic invasion using an analytical estimate for infection rate by Yevhen F. Suprunenko, Stephen J. Cornell, Christopher A. Gilligan

“…We explore the potential of using an analytical approximation for the rate, [Formula: see text], at which susceptible crop fields become infected at the start of an epidemic to predict the effect that the spatial structure of a host landscape will have on an epidemic. …”
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Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach by Rongshang Chen, Zhiyong Chen

“…We apply a Bayesian quantile regression (BQR) of the partially linear varying coefficient spatial autoregressive (PLVCSAR) model for spatial data to improve the prediction of performance. …”
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Spatial Modeling of Unemployment Rate in Counties of Iran Based on Population and Housing Census Data by Hamed Seifi, Abdollah Jalilian, Azad Khanzadi

“…In these data, the economically active population and the number of unemployed, aged 15 years old or above are categorized by gender and different levels of education in Iran counties. The aim pursued during this research is the spatial modeling of the number of unemployed in counties of Iran, based on gender and education as covariates. …”
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Profiling user segments and spatial clusters of EV uptake through multi-method modeling by Bailing Zhang, Jing Kang

“…However, most existing approaches treat EV adoption as a homogeneous process or rely on fixed-rule models that overlook spatial clustering. This study implements a multi-method approach that combines probabilistic modeling and geospatial analysis to classify and profile EV and conventional vehicle (CV) users. …”
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Disease prediction models and operational readiness. by Courtney D Corley, Laura L Pullum, David M Hartley, Corey Benedum, Christine Noonan, Peter M Rabinowitz, Mary J Lancaster

“…As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). …”
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Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRK by Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Noel Cressie

“…FRK is an R package for spatial and spatio-temporal modeling and prediction with very large data sets that, to date, has only supported linear process models and Gaussian data models. …”
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E-scooter's Availability and Social Equity in Minneapolis, MN: A Spatial Modeling Approach by Ahmad Ilderim Tokey

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LEPROSY CASE MODELING IN EAST JAVA USING SPATIAL REGRESSION WITH QUEEN CONTIGUITY WEIGHTING by Toha Saifudin, Marisa Rifada, Karina Rubita Makhbubah, Devira Thania Ramadhanty

“…Among the regression models tested, the spatial error regression model proved most effective, showing an R-Square value of 67.14% and an AIC of 213.023. …”
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Metabolism-associated marker gene-based predictive model for prognosis, targeted therapy, and immune landscape in ovarian cancer: an integrative analysis of single-cell and bulk RNA sequencing with spatial transcriptomics by Lele Ling, Bingrong Li, Boliang Ke, Yinjie Hu, Kaiyong Zhang, Siwen Li, Te Liu, Peng Liu, Bimeng Zhang

“…The MRG-based prognostic model was further utilized for functional analysis of the model gene set, pan-cancer analysis of genomic variations, spatial transcriptomics analysis, as well as GO and KEGG enrichment analysis. …”
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Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China by Haizhi Luo, Yiwen Zhang, Xinyu Gao, Zhengguang Liu, Xiangzhao Meng, Xiaohu Yang

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Performance Prediction and Evaluation of Biogas-Fed Solid Oxide Fuel Cell Plants Based on the Spatial and Temporal Fusion Model under Fluctuation Fuel Types and Processing Options by Shuai Ma, Run Jia, Yanan Li, Changyan Liu, Hao Guo

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A data-driven supervised machine learning approach to estimating global ambient air pollution concentrations with associated prediction intervals by Liam Jordan Berrisford, Hugo Barbosa, Ronaldo Menezes

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Bayesian Adaptive Lasso for the Partial Functional Linear Spatial Autoregressive Model by Dengke Xu, Ruiqin Tian, Ying Lu

“…This study introduces a partial functional linear spatial autoregressive model which can explore the relationship between a scalar spatially dependent response variable and predictive variables containing both multiple scalar covariates and a functional covariate. …”
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Mathematical Modelling of the Spatial Epidemiology of COVID-19 with Different Diffusion Coefficients by Benedict Barnes, Ishmael Takyi, Bright Emmanuel Owusu, Francis Ohene Boateng, Augustine Saahene, Emmanuel Saarah Baidoo, Jennifer Aduko Adombire

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Forecasting Lattice and Point Spatial Data: Comparison of Unilateral and Multilateral SAR Models by Carlo Grillenzoni

“…Spatial auto-regressive (SAR) models are widely used in geosciences for data analysis; their main feature is the presence of weight (W) matrices, which define the neighboring relationships between the spatial units. …”
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The role of host plants, land cover and bioclimate in predicting the invasiveness of Aromia bungii on a global scale by Enrico Ruzzier, Seunghyun Lee, Pietro Tirozzi, Valerio Orioli, Andrea Di Giulio, Olivia Dondina, Luciano Bani

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