Learning Individual Movement Shifts After Urban Disruptions with Social Infrastructure Reliance

TL;DR

This work tackles predicting shifts in individual movement after urban disruptions, addressing heterogeneity in responses and limited sensitivity to traditional sociodemographic features. It introduces a conditional UNet (cUNet) that jointly conditions on Social Infrastructure Reliance (SIR) and local spatial context to model post-event movement patterns from pre-event data. Key contributions include integrating SIR and spatial context, capturing interactions with sparse data, and demonstrating divergent movement responses among individuals with similar pre-event patterns but different SIR. Experiments on the YJMob100K dataset show that conditioning improves predictive accuracy, with the full model achieving the highest overall accuracy and offering insight for behavior-aware urban resilience planning.

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.

Figures (2)

• Figure 1: Illustration of individual movement pattern shifts after urban disruptive events.
• Figure 2: Predicted post-event movements of (a) sample individuals, and (b) individuals with similar pre-event patterns but different SIR

Theorems & Definitions (3)

• Definition 1
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• Definition 3