Adjustable Attribute Matching in Digital Similars of Populations

Kazi Ashik Islam, S. S. Ravi, Henning Mortveit, Samarth Swarup

Keywords: Digital similar, societal resilience, synthetic population, population digital twin

TL;DR: We present an integer linear programming approach for adjusting the matching of attributes in the integration of datasets into digital similars.

Abstract:

A digital similar (DS) of a population of a region is a common starting point for agent-agent based simulations. Here, an integer linear programming-based algorithm is presented that refines an existing, high-resolution methodology for constructing DSs. The extension consists of constructing a household-to-residence mapping that maximizes the correlation between household income of individual households and residence property values of individual residences.
The algorithm is applied to a coastal region of Virginia (US) where we demonstrate that new household-to-residence assignment generates significantly different outcomes than the existing approach which is random assignment at blockgroup level. Using the context of road inundation and measures such as ``time to evacuate'' and ``time to reach critical care'', it is demonstrated significant differences across household income segments with the new method, while no such difference is established with the prior method.

Submission Number: 16