Human Mobility Quantification via Static-Dynamic Data Correlation
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Solution Overview
Problem
Current methods for measuring human mobility within geographic regions are limited by static census data that does not account for dynamic movement patterns, providing only periodic and inaccurate population information, which hinders urban planning and location-based services.
Innovation Solution
An apparatus and method that combines static and dynamic data sources to identify sub-regions, determine correlations between static and dynamic information elements, and generate a mobility score, representing the uncertainty of mobility patterns, to provide actionable insights for clients such as marketing and urban planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static census data is used for population measurement, then data collection simplicity is maintained, but measurement precision and accuracy of mobility patterns deteriorates
Solution Approach 1:
The patent segments the geographic region into multiple sub-regions (e.g., census tracts, blocks, or zones) to enable more precise spatial analysis of mobility patterns. This segmentation allows the system to capture local variations in population movement that would be lost in aggregate census data, thereby improving measurement precision without requiring complete redesign of the entire data collection infrastructure.
Solution Approach 2:
The patent merges static census data with dynamic mobility data sources (such as mobile device location data, transit records, or survey data) to create a comprehensive mobility measurement system. By combining these data types, the system achieves high measurement precision for population movement while leveraging the simplicity of existing census collection methods.
2Loss of time
If periodic census data is used, then data collection resource consumption is reduced, but availability of timely mobility information deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-processing and storing mobility data from multiple sources in a structured format that can be quickly queried and analyzed. This preliminary preparation allows the system to provide timely mobility information without requiring complete re-collection of data for each new analysis request, thus reducing time lag while maintaining data collection efficiency.
Solution Approach 2:
The patent implements a dynamic data framework that allows the system to update and reanalyze mobility patterns as new data becomes available, rather than relying on fixed periodic census cycles. This dynamic approach enables timely information availability for current planning needs while maintaining efficient use of collection resources through selective data updates.
3Measurement precision
If residential address data is used for population counting, then data collection simplicity is maintained, but accuracy of actual location information deteriorates
Solution Approach 1:
The patent introduces an intermediary processing layer that translates various data sources (census addresses, mobile device locations, transit records) into a unified spatial framework based on sub-regions. This intermediary layer reconciles the simplicity of address-based data collection with the need for accurate actual location information by mapping different data types to common geographic references without requiring complete redesign of the data processing pipeline.
4Measurement precision
If detailed sub-region analysis is implemented, then measurement precision of mobility patterns is improved, but computational resource requirements worsen
Solution Approach 1:
The patent applies segmentation by dividing the study area into manageable sub-regions that can be processed independently, allowing detailed mobility analysis at appropriate granularities while controlling computational resource requirements. The segmentation enables the system to focus computational energy on areas where detailed analysis is most needed rather than uniformly processing the entire region at high resolution.
Data Source
AI summary
Provided herein is a method for quantifying and measuring human mobility within defined geographic regions and sub-regions. Methods may include: identifying sub-regions within a region; identifying static information associated with the sub-regions from one or more static information sources; obtaining dynamic information associated with the sub-regions from one or more dynamic information sources; determining correlations between elements of the static information associated with a respective sub-region and elements of the dynamic information associated with the respective sub-regions; generating a mobility score for the respective sub-region based, at least in part, on the correlations between the elements of the static information and the elements of the dynamic information associated with the respective sub-region; and providing the mobility score to one or more clients for guiding an action relative to the mobility score.