A Novel Spatiotemporal Data Model for River Water Quality Visualization and Analysis

Abstract

River water quality (RWQ) data has obvious characteristics of spatial and temporal distribution, and tables are conventionally exploited for storage of multi-period monitoring data of RWQ; however, neither effective visualization nor accurate analysis of the obtained data can be realized due to its dispersion character. In this paper, a novel spatiotemporal data model is proposed for RWQ data to realize conveniently data representation and spatiotemporal analysis. In this model, a spatial point, containing both location and dynamic water quality information, is considered as the basic element of river spaces, and methods of expanding a point to a line segment, a flat surface and a cube are designed respectively so as to make this model be applicable to different generalizations of river spaces. Moreover, a temporal data storage structure is designed so that efficient inquiry and advanced analysis of RWQ data can be guaranteed and the occupied memory space can be reduced. Finally, case studies are conducted by performing 3D visualization, trend analysis and anomaly identification on RWQ data, the result of which showing that tridimensional representation of RWQ data can be realized efficiently, the computational complexity is reduced significantly and the occupied memory space of monitoring data is effectively economized. Accordingly, the proposed spatiotemporal data model can contribute to the efficient visualization and advanced analysis of RWQ data.

Keywords

• River space
• spatiotemporal data model
• water quality prediction