Measuring spatio-temporal accessibility to emergency medical services through big GPS data

Tianqi Xia; Xuan Song; Haoran Zhang; Xiaoya Song; Hiroshi Kanasugi; Ryosuke Shibasaki

doi:10.1016/j.healthplace.2019.01.012

Measuring spatio-temporal accessibility to emergency medical services through big GPS data

Health Place. 2019 Mar:56:53-62. doi: 10.1016/j.healthplace.2019.01.012. Epub 2019 Jan 28.

Authors

Tianqi Xia¹, Xuan Song², Haoran Zhang³, Xiaoya Song⁴, Hiroshi Kanasugi⁵, Ryosuke Shibasaki⁶

Affiliations

¹ Center for Spatial Information Science, The University of Tokyo, Japan; Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Japan. Electronic address: xiatianqi@csis.u-tokyo.ac.jp.
² Center for Spatial Information Science, The University of Tokyo, Japan; Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Japan. Electronic address: songxuan@csis.u-tokyo.ac.jp.
³ Center for Spatial Information Science, The University of Tokyo, Japan. Electronic address: zhang_ronan@csis.u-tokyo.ac.jp.
⁴ Center for Spatial Information Science, The University of Tokyo, Japan; Heilongjiang Cold Region Urban-Rural Human Settlements Science Key Laboratory, School of Architecture, Harbin Institute of Technology, China. Electronic address: song.xy@csis.u-tokyo.ac.jp.
⁵ Center for Spatial Information Science, The University of Tokyo, Japan. Electronic address: yok@csis.u-tokyo.ac.jp.
⁶ Center for Spatial Information Science, The University of Tokyo, Japan. Electronic address: shiba@csis.u-tokyo.ac.jp.

PMID: 30703630
DOI: 10.1016/j.healthplace.2019.01.012

Abstract

Medical accessibility is an important indicator for evaluating the effectiveness of public health services. However, the previous medical accessibility studies mainly focus on spatial accessibility without considering temporal variation in population distribution which is significant for evaluating access to emergency medical service (EMS). This paper proposes a model of spatio-temporal accessibility to EMS called ST-E2SFCA based on adapting the enhanced two-step floating catchment area (E2SFCA) method. We apply our method to the greater Tokyo area for a large volume of GPS dataset with millions of users and compare the accessibility difference over space and time. To evaluate our model, we also analyze the distinction of our model over different weight sets and compare the performance of ST-E2SFCA with the traditional E2SFCA. The result shows that our method can illustrate the temporal difference and is suitable for measuring the spatio-temporal accessibility to EMS, thus can guide the hospital location selection and urban planning.

Keywords: Big data; Emergency medical services; Enhanced Two Step Floating Catchment Area; Spatio-temporal analysis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Big Data
Catchment Area, Health*
Emergency Medical Services / statistics & numerical data*
Emergency Medical Services / supply & distribution
Health Services Accessibility / statistics & numerical data*
Hospitals / supply & distribution
Humans
Spatio-Temporal Analysis*
Tokyo