Geospatial Similarity Platform Using Jensen-Shannon Divergence
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining similarity between geographic regions are inadequate, particularly when considering socio-demographic characteristics, as they often rely on simple numeric attributes and fail to account for the nuanced relationships and semantic meanings of locations, making it difficult to identify and visualize similarities across multiple variables.
Innovation Solution
The implementation of a system that uses Jensen-Shannon Divergence (JSD) for calculating similarity between geographic regions, allowing users to weight attributes and visualize similarities through interactive maps, providing a data hub for easy incorporation of similarity measurements and offering tools for geospatial analysis that incorporate socio-economic and demographic variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods use simple numeric attributes to determine similarity between geographic regions, then the analysis process is simple and fast, but the measurement precision and ability to capture nuanced relationships is insufficient
Solution Approach 1:
The patent transforms the similarity measurement from simple numeric attribute comparison to a multi-dimensional parameter space including socio-demographic characteristics, economic indicators, and geographic features. This parameter transformation enables more precise similarity assessment by considering multiple dimensions simultaneously rather than relying on single numeric values.
Solution Approach 2:
The patent creates a composite similarity metric that integrates multiple types of data (demographic, economic, geographic) into a unified measurement framework. This composite approach combines different data sources and variable types to produce a more comprehensive and accurate similarity assessment between geographic regions.
2Quantity of substance
If researchers cherry-pick a few simple variables for demographic analysis, then the analysis is manageable and interpretable, but the quantity of information and comprehensiveness of the analysis is limited
Solution Approach 1:
The patent segments the large volume of socio-demographic data into distinct categories and dimensions (e.g., demographic characteristics, economic indicators, geographic features). This segmentation allows the system to manage and analyze comprehensive data sets by breaking them into manageable components that can be processed systematically.
Solution Approach 2:
The patent creates a universal analysis framework that can handle multiple types of variables and data sources simultaneously. This multi-functional system is designed to process diverse data types (numeric, categorical, spatial) through a unified methodology, making comprehensive data analysis more accessible and easier to operate.
3Loss of information
If traditional methods compare locations using single numeric attributes like population density or median income, then the analysis is straightforward and computationally efficient, but the ability to capture semantic meaning and contextual relationships is lost
Solution Approach 1:
The patent adds semantic and contextual dimensions to the traditional numeric attribute space by incorporating socio-demographic characteristics and their interrelationships. This dimensional expansion transforms the analysis from single-attribute comparison to multi-dimensional semantic space, preserving contextual information while maintaining computational feasibility through structured data organization.
Data Source
AI summary
A method provides visual analysis of datasets. The method is performed at a computer system. A user selects a data source. In response, the system presents a natural language interface for analysis of data in the data source and presents a map data visualization within the graphical user interface for selecting geospatial data points from the data source. In response to receiving a first user input to select a first set of one or more geographic regions, the system calculates the similarity between the first set of one or more geographic regions and a second set of one or more geographic regions, based on a range of socio-economic, demographic, and/or geographic data fields from the data source, using one or more statistical techniques (e.g., Jensen-Shannon Divergence (JSD)). The system then updates and displays the map data visualization according to the calculated similarity.