A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Trajectory big data reveals spatial disparity of healthcare accessibility at the residential neighborhood scale
Abstract Quantifying spatial disparities of accessibility to basic services is fundamentally important for achieving the SDGs goal of universal access to these services. Trajectory big data shows great potential to address such disparities, but we know little about its efficacy. Here, taking patients from residential neighborhoods to healthcare in Beijing, China, as an example, we tested the efficacy of applying taxi data on the patients' travel behavior and thereby the potential of measuring accessibility at the residential neighborhood scale. Our results showed that the taxi data quantified a decreased healthcare-seeking behavior with increased distance from hospitals and identified hospitals' catchment areas. Meanwhile, the exponential function provided a more accurate estimation of the decay distance of patients to healthcare than the Gaussian and Power functions. Additionally, results showed that using taxi data had great potential to quantify the accessibility to hospitals, and more importantly, to reveal the spatial disparity of accessibility at a finer scale than blocks or subdistricts. The approach developed in this study can improve our understanding of accessibility and its spatial disparity and provide fundamental data for the increasing interest in the 15-min community life circle planning worldwide, and to address local healthcare inequality.
Highlights Using trajectory big data to accurately capture travel behavior of patients to hospitals. The exponential function provides the most accurate estimate of accessibility. Travel behavior of patients to hospitals reveals spatial disparity of healthcare accessibility. The approach is applicable for understanding other services.
Trajectory big data reveals spatial disparity of healthcare accessibility at the residential neighborhood scale
Abstract Quantifying spatial disparities of accessibility to basic services is fundamentally important for achieving the SDGs goal of universal access to these services. Trajectory big data shows great potential to address such disparities, but we know little about its efficacy. Here, taking patients from residential neighborhoods to healthcare in Beijing, China, as an example, we tested the efficacy of applying taxi data on the patients' travel behavior and thereby the potential of measuring accessibility at the residential neighborhood scale. Our results showed that the taxi data quantified a decreased healthcare-seeking behavior with increased distance from hospitals and identified hospitals' catchment areas. Meanwhile, the exponential function provided a more accurate estimation of the decay distance of patients to healthcare than the Gaussian and Power functions. Additionally, results showed that using taxi data had great potential to quantify the accessibility to hospitals, and more importantly, to reveal the spatial disparity of accessibility at a finer scale than blocks or subdistricts. The approach developed in this study can improve our understanding of accessibility and its spatial disparity and provide fundamental data for the increasing interest in the 15-min community life circle planning worldwide, and to address local healthcare inequality.
Highlights Using trajectory big data to accurately capture travel behavior of patients to hospitals. The exponential function provides the most accurate estimate of accessibility. Travel behavior of patients to hospitals reveals spatial disparity of healthcare accessibility. The approach is applicable for understanding other services.
Trajectory big data reveals spatial disparity of healthcare accessibility at the residential neighborhood scale
Jing, Chuanbao (author) / Zhou, Weiqi (author) / Qian, Yuguo (author) / Zheng, Zhong (author) / Wang, Jia (author) / Yu, Wenjuan (author)
Cities ; 133
2022-11-19
Article (Journal)
Electronic Resource
English
Spatial Interaction Model for Healthcare Accessibility: What Scale Has to Do with It
| DOAJ | 2020
Residential neighborhood effects
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