Geospatial grid management: A comprehensive framework and systematic review of subdivision, encoding, indexing and storage

Abstract

With the rapid advancement of the Internet of Things (IoT), sensor technologies, and remote sensing, spatiotemporal data has emerged as a crucial data source across diverse industries, extensively utilized in environmental monitoring, intelligent transportation, socio-economic analysis, and other domains. Spatiotemporal data encompasses the locations, states, and interrelationships of objects within specific temporal and spatial contexts. It is characterized by dynamic properties, high dimensionality, and large data volumes, which pose significant challenges for storage, querying, and analysis. To address the challenges associated with managing large-scale spatiotemporal data, geospatial grid subdivision, grid encoding, grid indexing, and grid storage technologies offer essential support and have demonstrated remarkable effectiveness. In the past, existing reviews have typically focused on a single aspect, such as the fundamental methods of grid subdivision, the implementation details of encoding and indexing techniques, or solely on spatiotemporal databases. Although these reviews provide in-depth discussions of specific technologies, they lack a systematic analysis of the interrelationships among multiple technical modules, resulting in an inability to fully reveal the collaborative potential between modules. Additionally, current research provides limited comprehensive discussions on grid indexing technologies. This paper aims to address this gap by providing a systematic review of the development status of grid indexing technologies. Furthermore, it reviews and summarizes the key technologies, interrelationships, research advancements, and future directions of grid subdivision, grid encoding, grid indexing, and grid storage, thereby providing references for enhancing the storage and querying efficiency of spatiotemporal data.