Understanding Housing and Homelessness System Access by Linking Administrative Data

TL;DR

This study demonstrates that privacy-preserving data linkage methods effectively connect housing and homelessness records, enabling better understanding of individual interactions across multiple agencies, with implications for improving service delivery.

Contribution

The paper evaluates machine learning-based privacy-preserving linkage techniques using both traditional and domain-specific metrics in the context of housing and homelessness data.

Findings

Privacy-preserving linkage is effective for understanding individual agency interactions.

Performance varies across linkage methods, especially when evaluated with domain-specific metrics.

Linked data reveals detailed patterns of service use and homelessness trajectories.

Abstract

This paper uses privacy preserving methods to link over 235,000 records in the housing and homelessness system of care (HHSC) of a major North American city. Several machine learning pairwise linkage and two clustering algorithms are evaluated for merging the profiles for latent individuals in the data. Importantly, these methods are evaluated using both traditional machine learning metrics and HHSC system use metrics generated using the linked data. The results demonstrate that privacy preserving linkage methods are an effective and practical method for understanding how a single person interacts with multiple agencies across an HHSC. They also show that performance differences between linkage techniques are amplified when evaluated using HHSC domain specific metrics like number of emergency homeless shelter stays, length of time interacting with an HHSC and number of emergency shelters visited per person.