Geo-Spatial Big Data Analysis: An Overview

Advance increasing interest in large-scale, highresolution, real-time geographic information system (GIS) applications and spatial big data processing, traditional GIS are not efficient enough to handle due to limited computational capabilities. Geospatial analytics in big data needed new approaches that are flexible, non-parametric and should be able for dynamic modeling with non-linear processes. Compared to general big data, the special thing of geographical big data is Spatiotemporal Association Analysis (SAA) for scrutinizing the geographical big data. This analysis wraps of some vital elements of geometrical relations, statistical correlations, and semantics relations for effective decisive and predictive measurements based solutions. The gist and aim of this paper is to study and review the Spatiotemporal Association Analysis (SAA) in three aspects such as measurement (observation) adjustment of geometrical quantities, human spatial behavior analysis with trajectories, data assimilation of physical models and various observations.