Exploring Advanced Algorithms in Multi-Dimensional Spatial Data Modeling for Enhanced Geospatial Analysis

This study examines map analysis with the goal of improving current methods by looking at cutting-edge algorithms for modeling multidimensional spatial data. Tensor Decomposition (TD), Convolutional Neural Networks (CNNs), and Spatiotemporal Regression (STR) are all parts of the suggested method that work together to solve the complex problems that come up when representing spatial data. The abstract provides a concise summary of the primary objectives and methodologies of the study, while also elucidating the rationale behind its execution. In our work, we use new developments in deep learning and machine learning to go beyond common ways of looking at space, such as graph-based spatial analysis, geostatistical methods, and space partitioning strategies. When you combine convolutional neural networks with tensor decomposition, you can get hierarchical features out of the data and figure out what the complex spatial patterns mean. Using spatial-temporal regression also helps us understand spatial events better by adding temporal changes to the analysis framework. Our study's goal is to push the boundaries of geographic research by combining the newest techniques. This will make it possible to create more precise and all-encompassing methods that can correctly understand the complex nature of location data in many dimensions.