Map Feature Density Adjustment via Quad Tree Spatial Index
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Solution Overview
Problem
Mobile mapping applications face challenges in managing map feature density, leading to suboptimal performance and user experience due to predefined feature classes that fail to adapt to non-uniform feature density across urban and rural areas, affecting map display, street routing, and nearest search functions.
Innovation Solution
A method that automatically adjusts map feature density in real-time by estimating the density of a query region using a quad tree spatial index, allowing for dynamic adjustment of visible features and search radii based on location and zoom level, optimizing map display, street routing, and nearest search performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a predefined feature class is used for the entire map, then map display consistency is maintained, but map feature density becomes too dense or too sparse for some regions
Solution Approach 1:
The patent applies local quality by allowing different feature classes to be displayed in different regions of the map based on local feature density characteristics. Urban areas display different feature classes compared to rural areas, enabling each region to have optimized feature density while maintaining overall system consistency through the unified quad tree structure.
2Adaptability or versatility
If a user interface is provided to change feature display level, then feature density can be adjusted, but user operation becomes cumbersome especially when hands are occupied
Solution Approach 1:
The system performs self-service by automatically detecting the current location, determining the appropriate feature class based on pre-stored feature density information in the quad tree structure, and displaying the suitable map features without requiring any user input. This eliminates the need for manual UI interaction while maintaining adaptability to different regions.
3Ease of operation
If predefined search radius is used for nearest search, then search operation is simple, but search results may be insufficient or excessive and take too much time
Solution Approach 1:
The patent implements dynamics by making the search radius a dynamic parameter that automatically adjusts based on the current location's feature density. The system queries the quad tree structure to determine the appropriate search radius for the current region, enabling simple user operation while optimizing search efficiency and result quality for both urban and rural areas.
4Device complexity
If constant routing criteria are used everywhere, then routing algorithm is simple to implement, but optimal route quality and search time cannot be achieved in both urban and rural areas
Solution Approach 1:
The routing algorithm applies local quality by using different routing criteria for different regions. The system determines the current region's feature class through the quad tree structure and applies location-specific routing parameters, enabling the algorithm to maintain simplicity in implementation while achieving optimal route quality and search time in both urban and rural areas.
Data Source
AI summary
A method of determining density of a map is described along with an apparatus and computer-readable medium comprising instructions therefore. The method comprises determining one or more nodes of a quad tree applied to a map with which a predetermined query region intersects, calculating a cumulative data size of the query region based on a data size of the one or more intersecting nodes, and determining an average density of the query region based on the query region area and the cumulative data size.