The Residence History Inference Problem

TL;DR

This paper formalizes the residence history inference problem from online traces, providing an exact algorithmic solution to determine individuals' residence sequences, which advances computational modeling of human mobility.

Contribution

It introduces a formal model and an exact algorithm for residence inference, addressing a gap where prior work relied on heuristics.

Findings

Provides an exact solution for residence history inference.

Establishes the algorithmic complexity of the problem.

Demonstrates the tractability of the proposed method.

Abstract

The use of online user traces for studies of human mobility has received significant attention in recent years. This growing body of work, and the more general importance of human migration patterns to government and industry, motivates the need for a formalized approach to the computational modeling of human mobility - in particular how and when individuals change their place of residence - from online traces. Prior work on this topic has skirted the underlying computational modeling of residence inference, focusing on migration patterns themselves. As a result, to our knowledge, all prior work has employed heuristics to compute something like residence histories. Here, we formalize the residence assignment problem, which seeks, under constraints associated with the minimum length-of-stay at a residence, the most parsimonious sequence of residence periods and places that explains the movement history of an individual. Here we provide an exact solution for this problem and establish its algorithmic complexity. Because the calculation of optimal residence histories (under the assumptions of the model) is tractable, we believe that this method will be a valuable tool for future work on this topic.