Geo-analytics for prioritizing areas and outlets
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Solution Overview
Problem
Current geographic visualization methods consume significant network resources, processing power, and memory due to the need to process and display separate information for each subarea and handle duplicate points of interest, leading to inefficiencies in data processing and transmission.
Innovation Solution
The method processes raster files to estimate values for subareas and employs fuzzy search processes to eliminate duplicates, using a combination of master phrases, n-grams, and machine learning for uniform categorization and tagging of points of interest, thereby reducing data processing and transmission requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate information is processed and displayed for each subarea, then geographic visualization completeness is improved, but network resources and processing power are excessively consumed
Solution Approach 1:
The patent merges duplicate points of interest that appear across multiple subareas into single standardized entries. Instead of processing and transmitting separate information for each subarea, the system combines redundant data into a unified representation, thereby maintaining visualization completeness while significantly reducing network resource consumption and processing requirements.
2Measurement precision
If duplicate points of interest are handled separately for each subarea, then geographic visualization accuracy is improved, but data processing time and memory usage increase
Solution Approach 1:
The patent identifies and discards duplicate points of interest across subareas, keeping only standardized unique entries. By removing redundant data that does not add value to visualization accuracy, the system reduces data processing time and memory usage while preserving the essential geographic information needed for accurate representation.
3Manufacturing precision
If separate data processing is performed for each subarea, then local detail accuracy is improved, but overall system productivity decreases
Solution Approach 1:
The patent creates a universal standardized representation of points of interest that serves multiple subareas simultaneously. This multi-functional approach allows the same standardized data to be used across different geographic regions, maintaining local detail accuracy while significantly improving overall system productivity by eliminating redundant processing operations.
Data Source
AI summary
In some implementations, a data visualizer may receive a raster file associated with a geographic area and generate tabular data based on the raster file. The data visualizer may receive a list of possible outlets and update a list of outlets based on removing duplicate outlets from the list of possible outlets. The data visualizer may generate a set of categories corresponding to the list of outlets based on a combination of master phrases, n-grams, and machine learning. The data visualizer may generate area scores, associated with subareas of the geographic area, based on the tabular data and indicated factors. The data visualizer may further generate outlet scores, associated with outlets in the list of outlets, based on the tabular data, the set of categories, and the indicated factors. The data visualizer may display, based on user input, a visual representation of the area scores or the outlet scores.