Hexagonal Grid Aggregation for Vector Tile Rendering
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Solution Overview
Problem
Geospatial indexing grid systems, such as H3, face significant computing resource challenges due to the exponential increase in the number of cells with each resolution level, leading to memory exhaustion and slow data aggregation, which hinders real-time visualization of data in vector map environments.
Innovation Solution
A database-centric approach is implemented to aggregate and visualize large volume data points using hexagonal grid cells, reducing CPU time and memory usage by employing efficient filtering, intrinsic hexagonal-index hierarchy, and common-ancestor-based packing schemes, allowing for dynamic construction of hexagon features tailored for vector tiles during rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If higher resolution levels are used in geospatial indexing grid systems, then measurement precision is improved, but device complexity increases due to exponential increase in number of cells
Solution Approach 1:
The patent divides the geospatial data space into hierarchical resolution levels, where data is segmented into coarse and fine resolution grid cells. This segmentation allows the system to manage complexity by processing only relevant resolution levels for each query, rather than handling all cells at once.
Solution Approach 2:
The patent introduces a temporal dimension by implementing lazy resolution construction, where resolution levels are built on-demand rather than pre-computed. This transforms the problem from a static high-dimensional space to a dynamic multi-dimensional space where data is constructed in time based on query requirements.
2Measurement precision
If higher resolution levels are used in geospatial indexing grid systems, then measurement precision is improved, but loss of time increases due to slow data aggregation
Solution Approach 1:
The patent performs preliminary aggregation of data into coarse resolution cells first, then progressively refines to finer resolutions only when needed. This preliminary action at coarser levels reduces the overall computation time by avoiding immediate processing of all fine-resolution cells.
Solution Approach 2:
The patent implements dynamic resolution construction where the system adapts the resolution level and aggregation depth based on query requirements and available resources. This dynamic approach allows the system to balance between precision and time by constructing only the necessary resolution levels on-demand.
3Measurement precision
If higher resolution levels are used in geospatial indexing grid systems, then measurement precision is improved, but use of energy increases due to memory exhaustion
Solution Approach 1:
The patent applies local quality by storing data at varying resolution levels in different locations in the database hierarchy. Coarse resolution data is stored at higher levels while fine resolution data is stored at lower levels, allowing the system to access only the necessary resolution level for each query, thereby reducing overall memory usage.
Solution Approach 2:
The patent implements a nested hierarchical structure where fine resolution grid cells are nested within coarse resolution cells. This nesting allows the system to represent high-resolution data compactly by referencing parent cells when fine-grained detail is not required, significantly reducing memory consumption while preserving the ability to access high resolution data when needed.
Data Source
AI summary
Systems, methods, and other embodiments associated with generating aggregate data geospatial grid cells for encoding in vector tiles are described. In one embodiment, a method includes accepting an input to adjust a zoom level of a map. In response to the input to adjust the zoom level, the method automatically (i) identifies finest-resolution hexagons contained in a vector tile that appears in the map at the adjusted zoom level, (ii) selects a hexagon resolution level that allows for n hexagons to be placed along an axis of the vector tile, (iii) generates, from the finest resolution hexagons, new hexagons at the hexagon resolution level, wherein the new hexagons aggregate data values of the finest resolution hexagons, and (iv) encodes the new hexagons in the vector tile. The method then transmits the vector tile for display in the map with the new hexagons overlaid on the vector tile.