Geographical Region Division Using Geohash Clustering
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Solution Overview
Problem
Conventional region division methods rely on manual data collection, leading to inefficiencies, inconsistent criteria, and difficulty in real-time updates, making it challenging to accurately and efficiently divide regions.
Innovation Solution
A method and apparatus that encode latitudes and longitudes into region numbers using GEOHASH, cluster these numbers to determine central points, and divide geographical regions based on central point quantities and distances, enabling automatic and efficient region division.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual investigation methods are used to collect store information and transaction data, then region division can be performed, but the process is time-consuming and inefficient
Solution Approach 1:
The patent uses GPS location data from mobile devices as a copy of physical store locations and customer visits. Instead of manual investigation, the system automatically collects and processes digital copies of location information to perform region division, dramatically improving efficiency while reducing time loss.
Solution Approach 2:
The patent replaces the mechanical manual investigation process with an automated electronic system that collects GPS data, processes it through algorithms, and generates region divisions automatically. This substitution eliminates the need for physical field surveys and manual data processing.
2Reliability
If manual investigation methods are used for region division, then region boundaries can be established, but the criteria are inconsistent and difficult to update in real-time
Solution Approach 1:
The patent implements dynamic region division that automatically adapts to changing conditions. The system continuously processes new GPS data and updates region boundaries in real-time based on current store locations and transaction patterns, making the division both consistent and adaptable simultaneously.
Solution Approach 2:
The system incorporates feedback loops where GPS location data and transaction information are continuously collected, processed, and used to refine region divisions. This feedback mechanism ensures consistent criteria application while enabling real-time updates when conditions change.
3Measurement precision
If detailed store information and transaction data are collected manually, then accurate region division can be achieved, but the computational effort and complexity increase
Solution Approach 1:
The patent extracts only the essential GPS location coordinates from the available data, separating the critical location information from other unnecessary details. This extraction approach maintains division accuracy while significantly reducing computational complexity by focusing only on what matters for region boundaries.
Solution Approach 2:
The patent transforms detailed store and transaction data into simplified geographic parameters (latitude and longitude coordinates). This parameter transformation reduces data complexity while preserving the essential spatial information needed for accurate region division.
Data Source
AI summary
This application discloses a method and for dividing geographical regions performed by a computing device. The method includes: obtaining N groups of the place names, their corresponding latitudes and longitudes; converting the N groups of the place names, their corresponding latitudes and the longitudes into a first dataset; encoding latitudes and longitudes in the first dataset into a second dataset, each pair of latitude and longitude corresponding to a respective geographical region associated with a region number in the second dataset; converting the second dataset into a third dataset, the third dataset including M groups of the region numbers, each group of region numbers representing central point latitudes and central point longitudes in the geographical regions represented by the group of region numbers; and dividing the geographical regions represented by the M groups of region numbers into P regions according to the third dataset.