Spatial Data Simplification via View Coordinate Transformation
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Solution Overview
Problem
Existing methods for simplifying vector data, such as the Douglas-Peucker method, face challenges including reliance on experiential distance thresholds, neglect of spatial relations, inability for lossless display adaptation, and inefficient calculation, making real-time simplification of high-resolution spatial data difficult.
Innovation Solution
A method and device that transform original spatial data into view coordinates within a view window, analyze pixels to determine simplification conditions, and simplify data based on these conditions, ensuring lossless display and correct spatial relation representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Douglas-Peucker method is used for simplification, then data transmission efficiency is improved, but manufacturing precision (display accuracy) deteriorates due to lossless display inability
Solution Approach 1:
The patent transforms spatial data from original coordinate system to view coordinate system, changing the reference frame parameters. This allows simplification decisions to be based on view-specific parameters (pixel mapping) rather than fixed distance thresholds, enabling lossless display adaptation across different zoom levels and view configurations
Solution Approach 2:
The patent introduces dynamic simplification conditions that adapt to the specific view window and display configuration. Instead of static distance thresholds, the simplification criteria dynamically adjust based on pixel mapping relationships, allowing the same spatial data to be optimally simplified for different display scenarios while maintaining display accuracy
2Loss of time
If Douglas-Peucker method is used for simplification, then calculation speed is improved, but measurement precision (spatial relation accuracy) deteriorates
Solution Approach 1:
The patent extracts and prioritizes the processing of key spatial elements (such as endpoints and inflection points) while simplifying or eliminating redundant intermediate points. By focusing computational resources on critical points that define spatial relationships, the method maintains measurement precision while reducing overall calculation time
Solution Approach 2:
The patent segments the spatial data processing into distinct phases: coordinate transformation, pixel mapping analysis, and conditional simplification. This segmentation allows each phase to be optimized independently, with spatial relation accuracy preserved in the transformation and analysis phases while efficiency is improved in the simplification phase
3Manufacturing precision
If high resolution spatial data is used, then manufacturing precision (display quality) is improved, but quantity of substance (data volume) increases
Solution Approach 1:
The patent applies different levels of simplification to different regions of the spatial data based on their importance and visibility in the view window. Critical spatial features maintain high precision while less important areas undergo greater simplification, achieving overall data reduction while preserving display quality where it matters most
Solution Approach 2:
The patent introduces a new dimension of analysis by mapping spatial coordinates to pixel coordinates. This dimensional transformation enables the system to evaluate and simplify data based on display-relevant criteria rather than purely geometric criteria, reducing data volume while maintaining perceived display quality
4Ease of operation
If experiential distance threshold is used in Douglas-Peucker method, then ease of operation is improved, but adaptability deteriorates
Solution Approach 1:
The patent creates a universal simplification framework based on pixel mapping that works across different view configurations, zoom levels, and display resolutions. The same core algorithm adapts to various scenarios by using the view window and pixel coordinates as universal reference frames, eliminating the need for multiple threshold settings
Data Source
AI summary
A method and a device for simplifying space data are provided, and the method includes: an original coordinate point of original space data is transformed into a view coordinate point of a view window according to predetermined view control parameters; the view coordinate point is analyzed that whether it accords with a simplification condition; the original coordinate point corresponding to the view coordinate point that accords with the simplification is simplified according to an analysis result. The method for simplifying space data transforms the original coordinate point of original space data into the view coordinate point of the view window and performs analysis processing, which can ensure that not only the space relation of each simplified space data of random complex itself is displayed correctly, but also the space relations between all the simplified space data are displayed correctly.