Cross-Temporal Location-Based Predictive Analysis with Locality Cohorts
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Solution Overview
Problem
Existing location-based predictive data analysis solutions face efficiency challenges due to the large number of location data objects that need to be processed, leading to high computational and storage resource demands.
Innovation Solution
Utilizing locality cohorts and machine learning models to generate inferred cross-temporal growth predictions, reducing the number of operations required for cross-temporal predictive data analysis by generating locality cohorts and processing ground-truth data to improve computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing location-based predictive data analysis solutions process all location data objects individually, then prediction accuracy is maintained, but computational resource demands and storage requirements increase significantly
Solution Approach 1:
The patent merges multiple individual location data objects into aggregated locality cohorts. Instead of processing each location data object separately, the system combines them into cohort structures that represent groups of locations with similar characteristics and policy responses. This aggregation reduces the number of individual processing units while maintaining predictive accuracy through cohort-level analysis of disease spread patterns.
Solution Approach 2:
The patent creates synthetic cohort representations that copy the essential characteristics of multiple location data objects into a single aggregated structure. These cohort copies preserve the statistical properties and policy response patterns of the original locations while reducing computational complexity. The cohort-based approach allows the system to work with simplified representations rather than raw individual location data.
2Reliability
If existing solutions process each location data object through cross-temporal analysis, then prediction reliability is improved, but storage resources and processing time increase
Solution Approach 1:
The patent merges multiple location data objects into locality cohorts that are processed together through cross-temporal analysis. Instead of storing and processing separate time-series data for each location, the system aggregates them into cohort structures that capture temporal patterns at the group level. This reduces the total volume of data requiring storage while maintaining the ability to perform reliable cross-temporal predictions.
Solution Approach 2:
The patent performs preliminary aggregation of location data objects into cohorts before conducting cross-temporal analysis. By pre-grouping the data and pre-computing cohort-level statistics and patterns, the system reduces the amount of data that needs to be stored and processed during the actual predictive analysis. This preliminary organization of data structure optimizes both storage requirements and processing efficiency.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing temporally dynamic location-based predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform temporally dynamic location-based predictive data analysis by using at least one of cohort generation machine learning models and cohort-based growth forecast machine learning models.