Learning Individual Movement Shifts After Urban Disruptions with Social Infrastructure Reliance

TL;DR

This paper introduces a deep learning model that incorporates social infrastructure resilience to predict individual movement shifts after urban disruptions, addressing data limitations and complex spatial interactions.

Contribution

It presents a novel approach integrating social infrastructure resilience into movement prediction models, improving accuracy in capturing individual responses to urban disruptions.

Findings

Incorporating SIR improves movement prediction accuracy.

Model captures divergent movement shifts among similar pre-event patterns.

Spatial context enhances understanding of individual movement changes.

Abstract

Shifts in individual movement patterns following disruptive events can reveal changing demands for community resources. However, predicting such shifts before disruptive events remains challenging for several reasons. First, measures are lacking for individuals' heterogeneous social infrastructure resilience (SIR), which directly influences their movement patterns, and commonly used features are often limited or unavailable at scale, e.g., sociodemographic characteristics. Second, the complex interactions between individual movement patterns and spatial contexts have not been sufficiently captured. Third, individual-level movement may be spatially sparse and not well-suited to traditional decision-making methods for movement predictions. This study incorporates individuals' SIR into a conditioned deep learning model to capture the complex relationships between individual movement patterns and local spatial context using large-scale, sparse individual-level data. Our experiments demonstrate that incorporating individuals' SIR and spatial context can enhance the model's ability to predict post-event individual movement patterns. The conditioned model can capture the divergent shifts in movement patterns among individuals who exhibit similar pre-event patterns but differ in SIR.