Predictability of Human Movements across Industry Sectors using Multilayer Networks
Abstract
Predicting the patterns of human movement is important to applications from urban design to disease control and emergency response. In this work, we leverage machine learning (ML) to study the predictability of movements to different types of locations (e.g., movements to grocery stores, to schools, or to hospitals). Our approach relies on developing multilayer mobility networks encoding movements between census tracts with network layers associated with different industry sectors. We study the predictability of node degrees across layers, studying which industries are easier/harder to predict. Rather than treat prediction as an end in itself, we propose ML predictability as an exploratory tool to understand multilayer network organization. Our results suggest that node's out-degrees are easy to predict, since they are driven by census tract populations; whereas in-degrees are harder to predict, since persons can be drawn to diverse type of locations in a census tract. We also find that movements to certain industry types (e.g., restaurants) are easier to predict than others, and the different industrie's predictability are positively correlated with the number of movements to those types of locations. These findings provide a practical step toward using ML for multilayer mobility modeling and the many downstream application.
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BibTeX
@misc{sejunti2026predictability,
title = {{Predictability of Human Movements across Industry Sectors using Multilayer Networks}},
author = {Maisha Islam Sejunti and Melissa Butler and Yingjie Hu and Dane Taylor},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2607.28494},
eprint = {2607.28494},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.28494},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Predictability of Human Movements across Industry Sectors using Multilayer Networks
AU - Maisha Islam Sejunti
AU - Melissa Butler
AU - Yingjie Hu
AU - Dane Taylor
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2607.28494
UR - https://arxiv.org/abs/2607.28494
AB - Predicting the patterns of human movement is important to applications from urban design to disease control and emergency response. In this work, we leverage machine learning (ML) to study the predictability of movements to different types of locations (e.g., movements to grocery stores, to schools, or to hospitals). Our approach relies on developing multilayer mobility networks encoding movements between census tracts with network layers associated with different industry sectors. We study the predictability of node degrees across layers, studying which industries are easier/harder to predict. Rather than treat prediction as an end in itself, we propose ML predictability as an exploratory tool to understand multilayer network organization. Our results suggest that node's out-degrees are easy to predict, since they are driven by census tract populations; whereas in-degrees are harder to predict, since persons can be drawn to diverse type of locations in a census tract. We also find that movements to certain industry types (e.g., restaurants) are easier to predict than others, and the different industrie's predictability are positively correlated with the number of movements to those types of locations. These findings provide a practical step toward using ML for multilayer mobility modeling and the many downstream application.
ER -