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Updated: Jul 16, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Spatio-Temporal Sensitivity of Healthcare Accessibility: Insights from a System Dynamics Model.

Patrick Danso¹, Liang Mao¹

¹Department of Geography, University of Florida.

Computers, Environment and Urban Systems

|July 15, 2026

Summary

Understanding healthcare accessibility requires analyzing factors like travel and demand. Disease rates significantly impact health access, with varying influences across regions and time, guiding targeted interventions.

Keywords:

Health accessibility sensitivity analysis spatio-temporal modeling system dynamics

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Last Updated: Jul 16, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Area of Science:

Healthcare accessibility modeling
Spatiotemporal analysis
Public health systems research

Background:

Existing models of healthcare accessibility lack a nuanced understanding of contributing factors.
The relative importance of factors like travel, service availability, and demand across space and time is not well understood.

Purpose of the Study:

To propose and demonstrate an analytic protocol for evaluating factors influencing healthcare accessibility.
To systematically assess the influence of general, spatial, temporal, and spatio-temporal factors on healthcare accessibility.

Main Methods:

Developed a four-tier analytic protocol using global sensitivity analysis (GSA).
Integrated the protocol with a system dynamics model for a case study in Florida, USA.
Applied general, spatial, temporal, and spatio-temporal GSA to evaluate factor influence.

Main Results:

Disease rate emerged as the most influential factor on overall health accessibility variations.
Spatial GSA revealed geographic heterogeneity: travel propensity is key in rural areas, while facility discharge rates dominate urban settings.
Spatio-temporal GSA demonstrated substantial weekly and ZIP code-level variations in factor influence.

Conclusions:

The proposed GSA framework quantifies factor influence across multiple scales, offering crucial insights for healthcare planning.
Provides evidence-based guidance for designing context-specific interventions (e.g., short-term vs. long-term, local vs. statewide strategies).
Highlights the dynamic and heterogeneous nature of healthcare accessibility determinants.