Spatial Pattern

Spatial pattern analysis focuses on identifying and understanding recurring structures in data distributed across space, aiming to extract meaningful insights and predictions. Current research heavily utilizes machine learning, employing diverse models like convolutional neural networks, autoencoders, and XGBoost, to analyze spatial patterns in diverse fields such as agriculture, hydrology, and climate modeling. These analyses improve the accuracy of predictions (e.g., water table depth, precipitation, and traffic flow) and enable better decision-making in various applications, from optimizing agricultural practices to enhancing resource management. The development of novel algorithms that effectively handle high-dimensional spatial data and incorporate spatial dependencies remains a key focus.