Density-Aware Map Data Sampling for Rendering Performance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large geographic datasets are computationally expensive to display efficiently, leading to overcrowding on maps that obscure visual trends, necessitating a method to sample and reduce data points while maintaining performance and visual representation.
Innovation Solution
A process that divides maps into subregions, determines regional density, and samples data points based on this density to select a subset for rendering, using a sampling rate that considers neighboring subregions to maintain proportional representation across the map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the entire geographic dataset is displayed, then complete visual information is provided, but computational cost increases and data points crowd map regions
Solution Approach 1:
The map is divided into multiple subregions, and data points are sampled independently in each subregion based on its specific density characteristics. This segmentation allows the system to render only the necessary subset of data points for each region while maintaining overall visual accuracy, thereby improving rendering performance without losing critical visual information.
Solution Approach 2:
Different sampling rates are applied to different subregions based on their local data density. High-density regions use lower sampling rates to reduce clutter, while low-density regions use higher sampling rates to maintain representativeness. This local quality approach optimizes the balance between visual information preservation and rendering performance for each specific area.
2Productivity
If data points are sampled to reduce rendering load, then performance improves, but visual representation accuracy may deteriorate
Solution Approach 1:
The sampling rate is dynamically adjusted as a parameter based on the density characteristics of each subregion. By changing the sampling rate parameter according to local conditions, the system maintains visual representation accuracy in high-density areas while achieving better performance in low-density areas, thus resolving the contradiction between performance and accuracy.
Solution Approach 2:
The patent replaces simple uniform random sampling with a density-aware sampling mechanism that uses regional density information to guide the sampling process. This substitution of the sampling mechanism allows the system to maintain visual accuracy while reducing the number of rendered points, improving overall performance.
3Ease of manufacture
If uniform sampling is applied across the entire dataset, then implementation is simple, but regions with high data density become overcrowded
Solution Approach 1:
The system transitions from uniform sampling to non-uniform sampling where each subregion is sampled according to its local density characteristics. This local quality approach prevents data point crowding in high-density regions while maintaining implementation simplicity through automated density-based sampling rate adjustment, thereby eliminating the harmful effect of overcrowding.
Solution Approach 2:
The sampling rate is made dynamic rather than static, adapting automatically to the density distribution of data points in different subregions. This dynamic sampling mechanism prevents crowding in high-density areas while maintaining ease of implementation through automated adjustment, resolving the contradiction between simplicity and the avoidance of harmful crowding effects.
Data Source
AI summary
Sampling data is disclosed. A region of a map to be rendered is determined. It is determined that a first subregion is included in the region. Data points associated with subregions in a neighborhood of the first subregion are analyzed to determine a regional density of points. Data points associated with the first subregion are sampled based on the regional density of points.