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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Impact of symmetry in local learning rules on predictive neural representations and generalization in spatial navigation. by Janis Keck, Caswell Barry, Christian F Doeller, Jürgen Jost

“…In spatial cognition, the Successor Representation (SR) from reinforcement learning provides a compelling candidate of how predictive representations are used to encode space. …”
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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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Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States by Hongxiang Yan, Ning Sun, Hisham Eldardiry, Travis Thurber, Patrick Reed, Daniel Kennedy, Sean Swenson, Jennie Rice

“…Abstract One of the major challenges in large‐domain hydrological modeling efforts lies in the estimation of spatially distributed hydrological parameters while simultaneously accounting for their associated uncertainties. …”
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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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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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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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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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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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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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Modified STARIMA model for space-time data by Laura Šaltytė

“… In this paper we propose spatial time series model. ARIMA model class is considered for each location. …”
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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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A Hybrid Deep Learning–Based Approach for Visual Field Test Forecasting by Ashkan Abbasi, PhD, Sowjanya Gowrisankaran, PhD, Wei-Chun Lin, MD, PhD, Xubo Song, PhD, Bhavna Josephine Antony, PhD, Gadi Wollstein, MD, Joel S. Schuman, MD, Hiroshi Ishikawa, MD

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A novel approach to skin disease segmentation using a visual selective state spatial model with integrated spatial constraints by Yu Bai, Hai Zhou, Hongjie Zhu, Shimin Wen, Binbin Hu, Haotian Li, Huazhang Wang, Daji Ergu, Fangyao Liu

“…Additionally, we introduce a spatially-constrained loss function that mitigates gradient stability issues by considering the distance between label and prediction boundaries. …”
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Penalized Composite Likelihood Estimation for Spatial Generalized Linear Mixed Models by Mohsen Mohammadzadeh, Leyla Salehi

“…When discussing non-Gaussian spatially correlated variables, generalized linear mixed models have enough flexibility for modeling various data types. …”
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