Declustering Point-of-Interest Icons Using Mean Out Vectors
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Solution Overview
Problem
The display of multiple point-of-interest (POI) icons on maps often results in overlapping or occlusion, making it difficult for users to visually distinguish and identify various points of interest effectively.
Innovation Solution
The creation of super-clusters and mini-clusters based on the proximity of POI locations, with POI icons being placed using a mean out positioning vector that extends from the center of these clusters, attempting to place icons as groups or individually to minimize overlap and maximize visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple POI icons are displayed on the same map, then the quantity of information displayed is improved, but the icons overlap or occlude one another making them difficult to distinguish
Solution Approach 1:
The patent divides densely packed POI icons into separate clusters based on spatial proximity. Each cluster is processed independently to determine icon placement, preventing overlap between icons from the same cluster while maintaining comprehensive display of all POIs across the map view.
Solution Approach 2:
The patent introduces angular positioning as an additional dimension beyond simple radial distance from cluster center. By varying both distance and angle parameters, icons are distributed in a two-dimensional placement space, maximizing separation and visibility while maintaining cluster coherence.
2Loss of information
If POI icons are placed individually at each location, then complete information is provided, but visual clutter and overlap increase
Solution Approach 1:
The patent merges nearby POI locations into spatial clusters when they fall within a threshold distance. Icons representing POIs within the same cluster are positioned relative to a common cluster center rather than their exact individual locations, reducing visual clutter while preserving the ability to display all POI information.
Solution Approach 2:
The patent applies different placement strategies to different spatial regions. Dense clusters of POIs receive specialized cluster-based positioning with angular distribution, while sparsely distributed POIs are placed at their individual locations. This localized approach optimizes visibility in high-density areas without affecting low-density regions.
Data Source
AI summary
Super-clusters of point-of-interest locations are created based on how close the point-of-interest locations are to one another. Additionally, one or more mini-clusters are created in each of the super-clusters based on how close the point-of-interest locations within each super-cluster are to one another. For each of one or more mini-clusters, some point-of-interest icons corresponding to the point-of-interest locations are placed based at least in part on a mean out positioning vector that is based at least in part on a center of the mini-cluster and a center of the super-cluster that includes the mini-cluster. Additionally, some point-of-interest icons are attempted to be placed as a group, while others are attempted to be placed individually.