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The objective of spatial analysis techniques is to describe the patterns existing in spatial data and to establish, preferably quantitatively, the relationships between different geographic variables. The notion of spatial analysis in a Geographic Information Systems (GIS) environment encompasses the idea of integrating spatial data and alphanumeric attributes and translating it into a series of functions related to selection and data search, on the one hand, and with modeling, on the other. There have been substantial advances in spatial analysis techniques in GIS, mainly in the form of more...

Time Series Analysis - Frontiers in Research and Practice offers a concise overview of cutting-edge methods for understanding, forecasting, and applying models to temporal data. The volume combines Markov Chain Monte Carlo, Bayesian ARIMA, and Posterior Predictive Distribution to enable uncertainty-aware time series forecasting. It reinterprets temporal data through co-evolving time series, causal dependencies, shift/visibility graph representations, graph representation learning, multiplex networks, and temporal metagraphs, tools suited for dependency regime forecasting and critical fields...