Mapping the spatial disparities in urban health care services using taxi trajectories data

Abstract

The understanding of spatial inequality in health care services is critical for reasonable policy-making and management. In this study, we present a novel approach to analyze the demand-supply of health care services using taxi data. Taxi data provide observations of individual travel activities, and hence can be used to characterize the actual demand-supply of health care services. We apply the proposed approach in Guangzhou, China to carry out a case study. The resultsshow the spatial disparities in health care service access. About 21.05% of the total population has high hospital accessibility, while the remaining 78.95% has relatively low hospital accessibility (i.e., roughly an "80/20" distribution). It is found that 6.29% of the population lives in high-density suburban communities but has relatively low hospital accessibility. Most of the hospitals serve a population that is compatible with their capacity. One hospital is found to have a small capacity but to serve a large population, while two hospitals have relatively high capacities but serve small populations. These findings can help improve our understanding of spatial inequalities in public service provision, and may also provide useful information to address the health care problems of an aging population in contemporary, rapidly urbanizing China.

Spatial Difference of Transit-Based Accessibility to Hospitals by Regions Using Spatially Adjusted ANOVA

“…In China, tertiary hospitals, especially 3A hospitals, contain the most important medical resources [15]. Therefore, in this study, we focus mainly on 3A hospitals.…”

“…Although these studies can intuitively describe the distribution characteristics and variation levels within data, they tend to be descriptive and exploratory. Some scholars used z-scores and its corresponding Low-High map to determine spatial disparity in accessibility [9,14,15]. This method could roughly identify whether accessibility within a subdistrict is above the standard or not as well as indicating accessibility-population mismatch, but it cannot determine which subdistrict has significantly higher or lower accessibility.…”

Equity of Accessibility to Health Care Services and Identification of Underserved Areas

“…At regional scale, Crooks and Schuurman (2012) explored the potential spatial access to primary health care physi-cians in five Canadian provinces and territories. At urban scale, several studies found that the areas with higher accessibility are mainly concentrated in urban areas, while those with lower accessibility are mainly concentrated in marginal areas (Brabyn and Skelly, 2002;Chen et al, 2018). At neighborhood scale, several studies indicated that accessibility significantly differs from spatial dimensions (Bell et al, 2013;Cabrera-Barona et al, 2016).…”

Analyzing accessibility to fire stations: A floating catchment area model for stochastic transportation networks with travel time correlation

“…Following this research, different functions were calibrated to capture the characteristics of the impedance factor. For example, Chen et al (2017), Chen, Wang, Liu, and Li (2018), and Song et al (2018 respectively used exponential, power, and kernel density functions. Another extension to improve 2SFCA measures rests on the modeling of choices from the multiple facilities.…”