Spatial Distribution Map Analysis for Textual Feature Identification
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Solution Overview
Problem
Current data analysis techniques for unstructured text data fail to effectively utilize spatial information beyond simple map visualization and frequent word identification, limiting the depth of insights derived from spatial features in multidimensional data.
Innovation Solution
A computer-implemented method that selects textual features from multidimensional data, extracts spatial information, projects them into spatial distribution maps, and determines similarity with predefined property distribution maps to identify notable textual features based on spatial correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If simple map visualization and frequent word identification methods are used, then the implementation is simple and quick, but the depth of insights derived from spatial features is limited
Solution Approach 1:
The patent transitions from simple 2D map visualization to a multi-dimensional analysis framework that combines spatial distribution maps with property distribution maps. By adding the dimension of property comparison (e.g., population density, income levels, climate zones) to the spatial analysis, the system derives deeper insights about regional characteristics and their relationship with textual features, resolving the contradiction between analysis depth and method complexity.
Solution Approach 2:
The patent introduces multiple parameters for comprehensive analysis: spatial distribution parameters (geographic coordinates, regional boundaries), property parameters (population, income, climate metrics), and similarity metrics (correlation coefficients, statistical distances). By changing from a single parameter approach to multi-parameter analysis, the system achieves deeper insights while managing complexity through systematic parameter selection and comparison.
2Adaptability or versatility
If conventional spatial analysis focusing on single-area concentration is used, then the analysis scope is narrow and focused, but the ability to identify meaningful spatial patterns across different regions is limited
Solution Approach 1:
The patent creates a universal analysis framework that can handle multiple types of spatial patterns and property comparisons simultaneously. The system is designed to analyze various textual features (keywords, phrases, categories) against multiple property dimensions (demographic, economic, geographic), making it adaptable to different analysis scenarios while maintaining a consistent methodological approach, thus enhancing versatility without proportionally increasing complexity.
Solution Approach 2:
The patent employs partial action by selectively analyzing specific property distributions that are most relevant to the research question rather than attempting to analyze all possible properties. The system calculates similarity metrics only for selected property pairs, avoiding the complexity of comprehensive analysis while still identifying meaningful spatial patterns through targeted comparisons.
Data Source
AI summary
A technique for use in analyzing multidimensional data is disclosed. In the technique, a subset of texts specified by a textual feature is selected from the multidimensional data. Each text of the subset is projected into a target image based on the corresponding spatial information to obtain a spatial distribution map for the textual feature. The similarity between the spatial distribution map for the textual feature and each property distribution map for each predefined property is determined. For the similarity exceeding a threshold, the textual feature is outputted as a notable textual feature.