An Object-Oriented Spatial Statistics Approach for Human Activity Space Estimation

TL;DR

This paper introduces a novel object-oriented spatial statistics method for estimating human activity spaces using GPS data, integrating GIS data to analyze mobility patterns and activity behaviors.

Contribution

It develops a time-weighted estimator and stability summaries within the object-oriented spatial statistics framework for improved activity space estimation from GPS data.

Findings

The framework accurately recovers stationary anchors and travel corridors.

Weighting improves analysis under irregular GPS sampling.

The method distinguishes dwelling and movement behaviors.

Abstract

Human activity spaces are shaped by individual mobility and the built environment, motivating statistical methods that integrate GPS observations with GIS representations of places and routes. We propose a novel methodology to estimate activity spaces in built environments from GPS data within the Object Oriented Spatial Statistics framework. We characterize daily mobility through the distribution of time across spatial polygons and road segments, aiming to capture entity-specific time-use fractions and level-$\gamma$ activity spaces. We develop a time-weighted estimator to handle irregularly sampled GPS observations. We derive an error bound that quantifies the effects of measurement error, nearest-entity misclassification, temporal gaps, boundary crossings, and day-to-day variability. We also develop a map-augmented representation of daily activity patterns, a dwell-time-weighted distance for clustering daily trajectories, and polygon- and road-based stability summaries. Simulation studies and a real-data application demonstrate that the proposed framework recovers concentrated stationary anchors, interpretable travel corridors, and distinct stabilization behavior for dwelling and movement components, supporting the benefits of weighting under irregular sampling. KEYWORDS: GPS data, GIS, human mobility, space-time geography.