What characterizes the spatio-temporal view?

Spatial and temporal data are data with both time and space dimensions, and more than 80% of the data in the real world is related to geographic location. Spatio-temporal big data includes three-dimensional information of time, space, and thematic attributes, and has the comprehensive characteristics of multiple sources, massive volume, and rapid updating. With the rapid development of science and technology, human exploration of their own living environment has not only been limited to the surrounding world, the outer edge of the exploration space has expanded dramatically, and has spread to all corners of the earth, all circles, and extended to the outer space. Therefore, how to express the objective world and activity characteristics of human activities has become a hot spot and focus of research institutions and personnel. Accompanied by the development of computer technology, how to use computer simulation and characterization of the objective world and human activities, undoubtedly also provides scholars with a broad research space.

Along with the process of people's exploration of space, the scope of access to a variety of information from the local ground, the global surface, the Earth's various circles to the expansion of the entire space inside and outside the Earth, from the original two-dimensional planar spatial benchmarks evolved gradually to the three-dimensional spatial benchmarks, and then evolved to reflect the spatio-temporal distribution of geospatial objects of the four-dimensional spatial benchmarks. Spatio-temporal data refers to spatial data with a time element and changes over time, and is a way of expression to describe the information of physical elements in the Earth's environment. These spatio-temporal data involve a variety of data, such as numbers, text, graphics and images of the number, shape, texture, spatial distribution characteristics, internal connections and laws of the earth's environmental features, etc., which not only have obvious spatial distribution characteristics, but also have the characteristics of huge data volume, non-linearity and time-varying. Data with both temporal and spatial dimensions, more than 80% of the data in the real world is related to geographic location. Spatio-temporal big data includes three-dimensional information of time, space, and thematic attributes, and has the comprehensive characteristics of multiple sources, massive volume, and rapid updating. Spatio-temporal data presents the complexity of multidimensional, semantic, and spatio-temporal dynamic associations due to the inherent characteristics of spatial entities and spatial phenomena in the space in which they are located in the three dimensions of time, space, and attributes, so it is necessary to study the formal expression of spatio-temporal big data multidimensional association descriptions, the dynamic modeling of association relationships, and the multiscale association analysis methods, and the collaborative spatio-temporal big data computation and reconstruction to provide a fast and accurate task-oriented The methodology is to provide fast and accurate task-oriented association constraints for spatial and temporal big data collaborative computing and reconstruction.