Image Semantic Brand Penetration Mapping by Population Sub-Region
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Solution Overview
Problem
Existing image content analysis systems face challenges in efficiently determining brand penetration across geographic regions due to high computational costs associated with storing large volumes of data, particularly when population densities are low, leading to statistically irrelevant data.
Innovation Solution
A method and system that partitions geographic areas into sub-regions based on population thresholds, using image content analysis to determine brand detections, generating weighted brand penetration indices, and storing only statistically meaningful data to minimize memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If image content analysis is performed across entire geographic regions, then brand penetration data coverage is improved, but computational cost and storage requirements increase significantly
Solution Approach 1:
The geographic region is divided into multiple sub-regions based on population density thresholds. This segmentation allows the system to process and store brand penetration data only for sub-regions that meet minimum population criteria, reducing overall storage requirements while maintaining data coverage in meaningful areas.
Solution Approach 2:
Different processing and storage strategies are applied to different sub-regions based on their population characteristics. Sub-regions above the population threshold receive full brand penetration analysis and data storage, while areas below the threshold are excluded or processed with reduced detail, optimizing resource allocation.
2Area of stationary object
If brand penetration analysis is performed in low population density areas, then geographic coverage is improved, but data statistical relevance deteriorates
Solution Approach 1:
The system applies a population density threshold parameter to filter sub-regions before performing brand penetration analysis. By changing the parameter threshold, the system ensures that only sub-regions with sufficient population statistics are included in the analysis, maintaining measurement precision while still achieving comprehensive coverage of populated areas.
3Loss of information
If all detected brand data is stored for every sub-region, then data completeness is improved, but memory usage and computational burden increase
Solution Approach 1:
The system extracts and stores only the essential brand penetration indices for sub-regions that meet population thresholds, rather than storing all raw detection data. This extraction approach maintains data completeness for analytical purposes while significantly reducing memory usage and computational burden.
Solution Approach 2:
The system performs partial processing by focusing computational resources only on sub-regions above the population threshold. Rather than processing all areas equally, it applies analysis selectively to where it produces statistically meaningful results, improving computational efficiency without sacrificing data completeness in relevant areas.
Data Source
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AI summary
Example embodiments of the disclosed technology implement a brand penetration determination system using image semantic content. A geographic sub-region determination system is configured to partition a geographic area into two or more sub-regions. An image content analysis engine is configured to determine, from images captured at one or more sites within each sub-region, a number of detections of a brand within each respective sub-region. A brand penetration index generation system is configured to generate a brand penetration index for each sub-region based on the number of detections of the brand in the respective sub-region weighted by one or more factors (e.g., population factor, category factor, etc.), which is stored in memory with an indicator of each respective sub-region. In splitting the geographic area into two or more sub-regions, the number and/or boundaries of sub-regions are determined so as to ensure that the population within each sub-region is above a threshold.