Geolocation Tracking Region Partitioning via Centroid Truncation
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Solution Overview
Problem
Conventional geolocation tracking systems face inefficiencies in determining discrete location memberships within regions, as they often require searching through multiple regions, limiting the use of parallel processing and increasing resource utilization.
Innovation Solution
The system employs a location engine that determines the centroid of each region, truncates it to a one-dimensional location, and assigns it as a partition key, allowing for efficient region identification by matching truncated merchant locations to corresponding regions within a defined threshold, enabling parallel processing across multiple computing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional geolocation tracking systems search through multiple regions to determine location memberships, then they can ensure complete coverage of all possible regions, but they increase resource utilization and limit parallel processing capabilities
Solution Approach 1:
The system segments the geolocation search problem by dividing regions into different granularity levels (e.g., country-level regions and state-level regions). This allows the system to first perform a coarse search at the country level, then refine results at the state level, enabling parallel processing while maintaining complete coverage.
Solution Approach 2:
The system introduces a hierarchical dimension to the region search process. Instead of searching all regions at a single granularity level simultaneously, it organizes regions in a hierarchy (country → state → local), allowing parallel searches at each level while reducing the total search space for subsequent levels.
2Measurement precision
If conventional systems search through multiple regions to determine location memberships, then they can ensure accurate region identification, but they increase processing time and resource consumption
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing region metadata (such as centroid coordinates, boundary definitions, and hierarchical relationships) before actual geolocation queries. This allows the system to quickly filter and identify candidate regions without performing complex calculations during query processing, thereby maintaining accuracy while reducing processing time.
Solution Approach 2:
The system segments the region identification process into multiple stages: first identifying candidate regions using coarse criteria (e.g., country-level matching), then refining the identification using more precise criteria (e.g., state-level boundary checks). This multi-stage approach maintains high accuracy while significantly reducing the time required compared to exhaustive searching.
3Stability of the object's composition
If systems process region membership determinations on a single computing device, then they can maintain data consistency, but they cannot utilize parallel processing to improve efficiency
Solution Approach 1:
The system segments the data processing workload across multiple computing devices by dividing the region dataset into partitions. Each device processes a specific partition independently, maintaining data consistency within its partition. The segmented approach allows parallel processing while ensuring that each device operates on a consistent subset of the overall data.
Solution Approach 2:
The system introduces an intermediary layer (such as a coordinate indexing service or region metadata store) that mediates between multiple computing devices and the region database. This intermediary maintains the global view of data consistency while allowing individual devices to process their assigned partitions in parallel, resolving the conflict between consistency and parallel processing efficiency.
Data Source
AI summary
Disclosed are exemplary embodiments of systems and methods for use in geolocation analysis. In one exemplary method, a computing device accesses boundary definitions associated with regions. For each region, the computing device determines a centroid of the region, having a location including a latitude and a longitude, based on an associated boundary definition, truncates the centroid location by deleting the latitude or longitude, and stores the truncated location as a partition key for the region. The computing device also accesses a transaction record, including a merchant point location having a latitude and longitude. The computing device truncates the point location by deleting the latitude or longitude, and identifies regions associated with the partition keys based on a comparison of the truncated point location to the partition keys. The computing device then determines whether the point location is included in one of the identified regions.