RESEARCH ON EFFICIENT INDEXING OF LARGE-SCALE GEOSPATIAL DATA BASED ON MULTI-LEVEL GEOGRAPHIC GRID

With the implementation of unified natural resource management in China, national geographic conditions monitoring data have been identified as fundamental data for natural resource survey and monitoring. The efficiency of information extraction from massive spatio-temporal data to support natural resource management has emerged as a critical indicator for maximizing the value of geographic conditions monitoring data and enhancing data-driven decision management. Traditional spatial indices are computationally intensive, and when confronted with immense data volume or uneven data scale, issues such as extensive index computations and poor scale adaptability arise, impeding the efficient retrieval of complex geospatial data. In response to the need for efficient indexing of massive geospatial monitoring data at a scale of 100 million, a multi-level geographic spatial index framework based on geographic grids is proposed. Within the geographic conditions spatio-temporal database, a three-level spatial index of "zone-grid-space" is constructed, utilizing massive land cover data for analysis and testing. The results demonstrate that the multi-level spatial index method exhibits excellent scale adaptability, and grid coding dimensionality reduction and numerical operations effectively reduce the computational load of spatial retrievals of complex vector patches. This method significantly improves the retrieval efficiency of large-scale national geographic conditions data, providing an efficient technique for lightweight information extraction of large-scale monitoring geospatial data within spatial computing systems. The method holds reference value for on-demand retrieval, analysis, and decision-making of natural resource spatio-temporal big data.