Micro-geographic aggregation system for ZIP9 code rollup
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Solution Overview
Problem
Existing methods for geographic data aggregation, such as using ZIP9 codes, face challenges in maintaining privacy and accuracy while ensuring a minimum number of households, often resulting in loss of granularity and variance, which is crucial for targeted marketing and advertising efforts.
Innovation Solution
A micro-geographic aggregation system that automatically rolls up ZIP9 codes by identifying and combining geographic units based on proximity, similarity in data attributes, and demographic characteristics to meet a minimum household threshold, preserving data variance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ZIP9 codes are used for geographic aggregation, then data granularity and variance are improved, but privacy protection and compliance with regulations deteriorate due to small number of households
Solution Approach 1:
The patent combines multiple fine-grained ZIP9 codes into a rollup group that meets the minimum household threshold. The system identifies ZIP9 codes within a geographic radius, evaluates their similarity based on data attributes, and merges them into a compliant aggregation that preserves granularity while ensuring privacy protection through sufficient household representation.
2Reliability
If ZIP9 codes with small number of households are suppressed or removed, then privacy protection is improved, but data accuracy and granularity are lost
Solution Approach 1:
Instead of suppressing individual ZIP9 codes with small household counts, the system merges them into rollup groups with multiple ZIP9 codes. This approach maintains the privacy protection benefits of aggregation while preserving data accuracy by retaining the individual ZIP9-level attributes and variances within the rollup group, avoiding the need to fall back to coarser ZIP5 or ZIP7 levels.
3Reliability
If default values are applied to non-compliant ZIP9s, then compliance with privacy regulations is improved, but data variance and specificity are lost
Solution Approach 1:
The system replaces default value application with a merging approach that creates rollup groups from actual ZIP9 codes. This preserves real data variance and specificity by combining multiple actual ZIP9 datasets rather than substituting synthetic default values, maintaining compliance through the minimum household threshold while retaining authentic demographic, socio-economic, and behavioral data variations.
4Reliability
If data is aggregated at ZIP5 or ZIP7 level, then privacy protection and compliance are improved, but granularity and targeted marketing capability deteriorate
Solution Approach 1:
The system merges fine-grained ZIP9 codes into rollup groups that maintain ZIP9-level granularity rather than degrading to ZIP5 or ZIP7 levels. By combining multiple ZIP9 codes horizontally rather than moving to a coarser hierarchical level vertically, the system achieves compliance through sufficient household representation while preserving the detailed granularity needed for targeted marketing and advertising applications.
Data Source
AI summary
The present disclosure describes systems and methods for automatically rolling-up data associated with one or more geographic units, such as ZIP+4 codes, such that the rollup comprises a minimum number of households to protect anonymity and ensure compliance with privacy regulations, while preserving variance of the underlying data associated with the geographic regions. Data attributes may include demographic data, socio-economic data, lifestyle segmentation, psychographic data, behavioral data, credit data, and other data. The rollup process may involve identifying one or more geographic units with a number of households below a minimum or threshold amount, applying filters to find candidate geographic units for rollup, scoring candidate geographic units to select best pairings for rollup, and repeating until the rollup group has at least the minimum number of households. The process may make trades off between granularity (e.g., number of households), proximity, and similarity of data attributes associated with each geographic unit.