Map Label Density Sampling for Overcrowding
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Solution Overview
Problem
Existing map rendering technologies face challenges in selecting the optimal subset of labels to display on a map region, leading to overcrowding and obscuration, as the number of labels varies by region and zoom level, making it difficult to pre-select labels effectively.
Innovation Solution
A process involving regional density calculation and sampling of map labels, where labels are retrieved based on subregion density, with a sampling rate determined by the total area and number of labels in neighboring subregions, ensuring a balanced and relevant display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the number of map labels to be displayed is increased to provide more information, then the information completeness is improved, but the labels will overlap and crowd map regions to obscure the labels and underlying map
Solution Approach 1:
The patent applies local quality by calculating label density specifically for each subregion and using this local density information to determine sampling rates. Different subregions with different label densities receive different sampling rates, allowing the system to display appropriate information density in each local area without causing overlapping or obscuration in high-density regions while maintaining information completeness in low-density regions.
Solution Approach 2:
The patent changes the parameter of label density calculation from a global metric to a local subregion metric. By computing density within specific subregions and using this to dynamically adjust sampling rates, the system adapts the number of displayed labels to local conditions, preventing crowding while maintaining information availability.
2Device complexity
If labels are preselected based on zoom level, then the selection process is simplified, but problems associated with over or under including labels arise as density varies by region
Solution Approach 1:
The patent segments the map into multiple subregions and calculates label density for each subregion independently. This segmentation allows the system to make localized label selection decisions based on actual density distributions rather than using a single global zoom level criterion, improving label inclusion accuracy while maintaining manageable complexity through automated density calculation.
Solution Approach 2:
The patent replaces the mechanical/manual process of preselecting labels based on fixed zoom levels with an automated computational approach that calculates label density and determines sampling rates algorithmically. This substitution eliminates the need for manual label selection while adapting to regional density variations, improving both accuracy and efficiency.
3Productivity
If a fixed sampling rate is used for all regions, then the processing is simplified, but the visual representations of labels will overlap and crowd map regions in high-density areas
Solution Approach 1:
The patent introduces dynamics by making the sampling rate variable rather than fixed. The sampling rate is dynamically adjusted based on the calculated label density for each subregion, allowing the system to automatically reduce sampling rates in high-density regions to prevent label overlapping while maintaining higher sampling rates in low-density regions to ensure adequate information display.
Solution Approach 2:
The patent implements feedback by using the calculated label density information to determine the appropriate sampling rate for each subregion. The density calculation provides feedback about the actual label distribution, which then feeds back into the sampling decision, creating a closed-loop system that adapts to real conditions and prevents overlapping while maintaining processing efficiency.
Data Source
AI summary
Sampling map labels is disclosed. A region of a map to be rendered is determined. It is determined that a first subregion is included in the region. Map labels associated with subregions in a neighborhood of the first subregion are analyzed to determine a regional density of map labels. Map labels associated with the first subregion are sampled based on the regional density of map labels.