Generation and Classification of Activity Sequences for Spatiotemporal Modeling of Human Populations

TL;DR

This paper introduces an unsupervised method to classify and generate human activity sequences from survey data, enabling better modeling of individual exposure patterns in spatiotemporal contexts.

Contribution

It presents a novel unsupervised classification and sequence generation approach for human activity data, improving modeling of activity and mobility patterns.

Findings

The method produces coherent, stochastic activity sequences.

It enables group-specific exposure modeling.

The approach leverages existing machine learning algorithms.

Abstract

Human activity encompasses a series of complex spatiotemporal processes that are difficult to model, but represents an essential component of human exposure assessment. A significant empirical data source like the American Time Use Survey (ATUS) can be leveraged to model human activity, but tractable models require a better stratification of activity data to inform about different, but classifiable groups of individuals that exhibit similar activities and mobility patterns. We have developed a simple unsupervised classification and sequence generation method from existing machine learning algorithms that is capable of generating coherent and stochastic sequences of activity from the data in the ATUS. This classification, when combined with any spatiotemporal exposure profile, allows the development of stochastic models of exposure patterns for groups of individuals exhibiting similar activity behaviors.