Map Icon Clustering via Delaunay Triangulation
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Solution Overview
Problem
Mapping applications face overcrowding issues due to dense concentrations of locational information items, leading to user confusion and difficulty in identifying specific locations on a map, especially when switching between various levels of detail (LODs).
Innovation Solution
The implementation of a clustering method using Delaunay Triangulation to group locational information items into clusters, with a cluster tree built based on specified levels of detail, ensuring that items closer than a certain distance are clustered together without exceeding a permissible area, and backtracking to adjust clustering at lower LODs to maintain optimal representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple locational information items are displayed simultaneously on a map, then comprehensive information coverage is improved, but map overcrowding increases making it difficult to identify specific locations
Solution Approach 1:
The patent segments locational information items into multiple hierarchical levels of detail (LODs). At higher LODs, items are grouped into clusters representing broader categories, while at lower LODs, individual items are displayed with full detail. This segmentation allows the system to display comprehensive information across different scales without overcrowding the map at any single level.
Solution Approach 2:
The patent adds a hierarchical dimension to the display by implementing multiple LODs. Instead of displaying all items at one level, the system organizes them in a hierarchy where cluster icons represent groups at higher LODs and individual location icons appear at lower LODs. This dimensional addition allows comprehensive information display while maintaining map clarity through hierarchical organization.
2Ease of operation
If locational information items are clustered together, then map overcrowding is reduced, but accuracy in representing individual locations may be compromised
Solution Approach 1:
The patent applies local quality by allowing different representation methods for different spatial contexts. In dense areas where multiple locations exist, cluster icons with categorical information are used. In sparser areas, individual location icons are displayed. This local adaptation ensures that clustering improves readability where needed while maintaining individual location accuracy where sufficient space exists.
Solution Approach 2:
The patent implements dynamic switching between clustered and individual representations based on LOD and spatial density. As users zoom in or out, the system dynamically adjusts the level of clustering, transitioning from aggregated cluster icons at higher LODs to individual location icons at lower LODs. This dynamic behavior maintains both map readability and location accuracy across different viewing conditions.
3Stability of the object's composition
If clustering is applied at all LODs, then consistent organization is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary clustering actions at higher LODs before displaying at lower LODs. By pre-organizing locational information items into clusters and establishing the cluster tree structure in advance, the system reduces the computational burden during actual map rendering. This preliminary organization enables consistent clustering across LODs while managing computational complexity through advance preparation.
Data Source
AI summary
A set of locational information items are provided, each of which includes a set of coordinate values. These locational information items can be represented on a map including various levels of detail. A mapping application is provided which is intended to display these locational information items on various levels of detail of the map. The locational information items are clustered on certain levels of detail of the map. To cluster the locational information items, a level of detail for each LOD can be specified; a Delaunay Triangulation can be created over the set of the locational information items provided; a list of the edges associated with the Delaunay Triangulation can be generated and sorted by length; a cluster tree can be built; and the locational information items are displayed in clusters at each LOD.