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

2026-05-08

AI Summary

This study addresses the challenge of accurately characterizing individual activity spaces from sparse and irregularly sampled GPS data. The authors propose a modeling framework grounded in object-based spatial statistics, which integrates temporal distributions over GIS road networks and place polygons to construct a time-weighted estimator that effectively distinguishes between stationary and mobile behaviors. The approach further incorporates map-enhanced trajectory clustering and a stability metric for robust activity inference. Theoretical analysis provides error bounds accounting for both measurement inaccuracies and entity misclassification from multiple sources. Experimental results demonstrate that the method reliably identifies stable anchor locations and interpretable travel corridors, with both synthetic and real-world datasets confirming the superiority of the time-weighted strategy under irregular sampling conditions.

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-$γ$ 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.

Problem

• GPS data
• human mobility
• activity space
• spatial statistics
• built environment

Innovation

• Object-Oriented Spatial Statistics
• time-weighted estimator
• activity space estimation
• irregular GPS sampling
• map-augmented representation