Geographical Recommendation via Spatial Grid Vector Similarity
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Solution Overview
Problem
Traditional information retrieval systems fail to effectively recommend geographical regions with similar content information to a user-specified region, particularly in terms of representative categories and spatial distributions, and do not identify potential high-risk areas for disease outbreaks.
Innovation Solution
The system employs a spatial vector space model and a quadtree-based heuristic region search approach to analyze and recommend geographical regions by partitioning the spatial map into grids, evaluating similarity through cosine similarity of feature vectors and using mutual and reference distance vectors to capture spatial distributions, while also incorporating a prune-and-refine process for efficient search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional information retrieval is used to search for locations by keywords, then text documents can be retrieved, but the system cannot effectively recommend geographical regions with similar content information or identify high-risk areas for disease outbreaks
Solution Approach 1:
The patent segments the geographical space into discrete grid cells, where each grid represents a specific region with associated points of interest and disease risk data. This segmentation enables the system to analyze and compare geographical regions systematically by evaluating the composition and spatial distribution of representative categories within each grid, thereby improving geographical recommendation accuracy while maintaining service versatility
Solution Approach 2:
The patent transitions from traditional one-dimensional keyword-based text retrieval to a multi-dimensional geographical information analysis framework. This framework incorporates spatial coordinates, category compositions, and distribution patterns across multiple dimensions, enabling the system to identify regions with similar content information and high-risk disease areas that cannot be detected through conventional keyword search alone
2Measurement precision
If the system analyzes spatial distributions and representative categories to recommend similar geographical regions, then recommendation accuracy improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing geographical data into structured grid cells with categorized points of interest and pre-calculated spatial distribution features. This pre-organization of data allows the system to efficiently compute region similarity by comparing pre-extracted features rather than processing raw geographical data from scratch, thereby improving measurement precision while managing computational complexity
Solution Approach 2:
The patent creates simplified representations (copies) of geographical regions through grid-based abstraction, where each grid cell contains aggregated information about representative categories and their spatial distributions. These copied representations enable efficient similarity comparisons between regions without requiring complex analysis of the full geographical detail, thus improving measurement precision while reducing computational burden
Data Source
AI summary
Techniques for searching and providing geographical regions are described. The process searches and recommends points of interests based on a user-specified region. Points of interests include spatial objects (e.g., buildings, landmarks, rivers, parks) and their distributions in a geographical region. The process searches and recommends points of interests by partitioning a spatial map into grids to identify representative categories located in each of the grids. In response to the user-specified region, a set of geographical candidates containing the representative categories is retrieved. The process determines whether the user-specified region and the set of geographical candidates include similar or common representative categories and similar or common spatial distributions of the representative categories. Then the process provides the top ranked set of geographical candidates that have similar content information.