Understanding Housing and Homelessness System Access by Linking Administrative Data
Journal:
arXiv
Published Date:
May 13, 2025
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.
