Geo-enrichment Using Centroid-Based Reference Points
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Solution Overview
Problem
Current computing systems face challenges in effectively geo-enriching location data by associating it with geographical shapes, particularly in generating accurate spatial data tables and visualizations that incorporate reference points within complex geographical regions.
Innovation Solution
A system and method for geo-enriching data by accessing shape data associated with geographical regions, determining reference points within these shapes, and generating spatial data tables and visualizations that include location data attributes and their corresponding shapes, using centroid calculations and point identification within polygons to accurately represent geographical regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centroid calculation is used to determine reference points for all shapes, then the reference point selection is systematic and consistent, but the computational complexity increases for complex geometries where the centroid may fall outside the shape
Solution Approach 1:
The patent performs preliminary centroid calculation for all shapes, then checks whether the centroid lies within the shape boundaries. This preliminary action allows the system to systematically handle both simple and complex geometries by preparing reference points in advance, while only requiring additional boundary checks for cases where the centroid falls outside the shape.
Solution Approach 2:
The system uses the shape's own geometric properties (centroid) to determine its reference point, rather than requiring external intervention or complex algorithms. The centroid calculation is self-contained and automatically adapts to different shape complexities, with the boundary check serving as a self-correcting mechanism when the centroid falls outside the shape.
2Reliability
If the system stores complete shape data for all geographical regions, then spatial queries can be answered accurately, but the data storage requirements and processing time increase significantly
Solution Approach 1:
The patent extracts only the essential reference point information (centroid or alternative point) from complete shape data and stores this extracted information in the spatial data table. This extraction approach maintains query accuracy by preserving the key geometric reference while eliminating the need to store and process complete shape geometries for every location data record.
Solution Approach 2:
The system creates a simplified copy of the shape information by storing reference points that represent the essential geometric properties of each shape. These reference point copies are sufficient for spatial association operations, eliminating the need to work with complete shape data while maintaining the necessary spatial relationships.
3Ease of operation
If the system generates separate views for different location attributes, then data accessibility and usability improve, but the system complexity and maintenance burden increase
Solution Approach 1:
The patent segments the spatial data system into distinct views based on location attributes (e.g., state view, county view). Each view is tailored to specific spatial association needs, allowing users to access and manipulate data according to their specific requirements. This segmentation improves ease of operation by providing specialized interfaces for different spatial contexts.
Solution Approach 2:
The underlying spatial data table structure is designed to be universal and support multiple location attribute types through a common reference point association mechanism. This multi-functional design allows the same core data structure to serve multiple views and purposes, reducing maintenance burden while providing specialized access paths through the different views.
Data Source
AI summary
Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a request to geo-enrich data comprising a set of location data. The program further accesses shape data comprising a plurality of shapes associated with a plurality of geographical regions. The program also associates, for each location data in the set of location data, a shape in the plurality of shape with the location data.