Quad-tree GIS Data Relationship Probability Analysis
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Solution Overview
Problem
Geographic Information Systems (GIS) lack the ability to identify potential relationships between different data layers, despite data items in these layers being related through location information, which can impact the effectiveness of location-based services.
Innovation Solution
Implementing a quad-tree data structure to analyze and determine the probability of relationships between GIS data layers by aggregating data into layers based on common properties and using a threshold to determine subnodes, allowing for the identification of coincidental or causal relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If GIS data is organized into layers with common properties, then data organization and management are improved, but the ability to identify relationships between different layers is lost
Solution Approach 1:
The patent segments GIS data into distinct layers based on common properties (e.g., transportation, land use, demographics) while maintaining the ability to analyze relationships between layers through statistical methods. Each layer is independently organized but can be correlated with others through location-based analysis.
Solution Approach 2:
The patent introduces location information as an intermediary that connects different data layers. By using spatial coordinates and geographic relationships as a mediator, the system can identify correlations between layers (e.g., relationship between transportation infrastructure and demographic patterns) without losing the organizational benefits of layered structure.
2Measurement precision
If manual analysis of relationships between GIS layers is performed, then relationship identification accuracy is improved, but time consumption and labor requirements increase
Solution Approach 1:
The patent implements automated statistical analysis methods that enable the GIS system to self-analyze relationships between data layers without requiring manual intervention. The system automatically calculates correlation coefficients, performs spatial统计分析, and identifies relationships between layers, eliminating the need for time-consuming manual analysis while maintaining analytical accuracy.
Solution Approach 2:
The patent replaces manual analytical methods with automated computational algorithms. Statistical analysis, spatial correlation calculations, and relationship identification are performed through computer-based processing rather than human analysis, dramatically reducing time requirements while maintaining or improving accuracy through consistent application of analytical methods.
3Difficulty of detecting and measuring
If comprehensive relationship analysis between all GIS layers is performed, then relationship detection capability is improved, but system complexity increases
Solution Approach 1:
The patent segments the relationship analysis process into manageable components: individual layer analysis, pairwise layer correlation, and multi-layer relationship identification. This segmented approach allows comprehensive relationship detection to be performed through a series of simpler, modular analytical steps rather than attempting to analyze all layers simultaneously.
Solution Approach 2:
The patent implements a tiered analysis approach where the system can perform partial relationship analysis between selected layers when full comprehensive analysis is not required. This allows users to balance analysis depth with system complexity by analyzing only the specific layer combinations relevant to their needs, rather than always performing exhaustive analysis of all possible layer relationships.
Data Source
AI summary
A device may receive first data items and second data items that may have been output by sensor devices. The device may add, to a node in a data structure, at least some of the first data items. The device may divide the node to create subnodes that correspond to subregions of a geographic region. The device may add, to at least one of the subnodes, at least some of the second data items. The device may generate a probability of a first relationship, between the first data items and the second data items, based on determining subnodes that include a first data item and determining subnodes that exhibit a predefined second relationship with a second data item. The device may send, to another device, the probability of the first relationship to support location-based services.