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. …”
Get full text

Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland by Zeinab Bandpey, Soroush Piri, Mehdi Shokouhian

Subjects: Get full text

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

Subjects: Get full text

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

Subjects: Get full text

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

Get full text

A Spatial Transformation Based Next Frame Predictor by Saad Mokssit, Daniel Bonilla Licea, Bassma Guermah, Mounir Ghogho

“…In this work, we equip autonomous cars with an object-oriented next-frame predictor that leverages Transformer architecture to extract, for each moving object in the scene, a spatial transformation applied to the object to predict its configuration in the next frame. …”
Get full text

Machine learning-driven framework for realtime air quality assessment and predictive environmental health risk mapping by M. Rajesh, R. Ganesh Babu, Usha Moorthy, Sathishkumar Veerappampalayam Easwaramoorthy

Get full text

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. …”
Get full text

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. …”
Get full text

Spatial-temporal analysis of the international trade network by Donghai Liu, Ziwen Yang, Kun Qin, Kai Li

“…With the support of spatial-temporal data analysis technologies and network science, the International Trade Network (ITN) research has made significant progress, demonstrating broad application prospects in mining market evolution and predicting trade dynamics. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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

Get full text

Spatial modelling of vector-borne diseases: Where? When? How many? by Mr Cedric Marsboom

“…Avia-GIS R&D team has an extensive expertise in the spatial modeling of vector-borne diseases (VBDs) to address critical concerns regarding the epidemiology and control of VBDs. …”
Get full text

A model of spatially restricted transcription in opposing gradients of activators and repressors by Michael A White, Davis S Parker, Scott Barolo, Barak A Cohen

“…We present a general thermodynamic model that explains how spatial patterns of gene expression are established within OARGs. …”
Get full text

Latent spectral-spatial diffusion model for single hyperspectral super-resolution by Yingsong Cheng, Yong Ma, Fan Fan, Jiayi Ma, Yuan Yao, Xiaoguang Mei

“…To address these issues, we propose a novel latent spectral-spatial diffusion model (LSDiff) for single hyperspectral SR. …”
Get full text

Numerical simulation for spray spatial distribution of swirl nozzle and its target dustfall area prediction by Cuicui Xu, Xinyu Jia, Jingjing Jing, Junpeng Wang, Qingyi Mi, Gang Zhou, Lirong Wu

Get full text

Predictive Modeling of Surface Subsidence Considering Different Environmental Risk Zones by Yunsong Li, Yongjun Qin, Liangfu Xie, Yangchun Yuan, Jie Ran

“…Adopt four different noise reduction algorithms for data noise reduction on the raw data of the monitoring points at the intervals of different risk zones, and combine the time series prediction as well as the deep learning prediction method to get the prediction model for environmental risk zoning based on the environmental risk zoning. …”
Get full text

Modeling of spatial spread of COVID-19 pandemic waves in Russia using a kinetic-advection model by V. V. Aristov, A. V. Stroganov, A. D. Yastrebov

“…This paper studies the development of epidemic events in Russia, starting from the third and including the most recent fifth and sixth waves. Our twoparameter model is based on a kinetic equation. The investigated possibility of predicting the spatial spread of the virus according to the time lag of reaching the peak of infections in Russia as a whole as compared to Moscow is connected with geographical features: in Russia, as in some other countries, the main source of infection can be identified. …”
Get full text