Evaluating Spatial Accessibility to General Hospitals with Navigation and Social Media Location Data: A Case Study in Nanjing

Abstract

Spatial accessibility to general hospitals is an important indicator of the convenience and ability of residents to obtain medical services. Therefore, developing a model for measuring accessibility to general hospitals by multiple transportation modes is necessary. In this study, considering that the increase in travel time will reduce the attractiveness of general hospitals, we used the Two-Step Floating Catchment Area with the Gaussian attenuation function, in which the supply was presented by capacity of hospitals (i.e., number of beds), and the demand was presented by population in each grid derived with social media data mapping real-time locations of active users. The Gaussian Two-Step Floating Catchment Area (Ga2SFCA) simulates the attenuation tendency of the general hospital service capabilities over transit time. To obtain a highly precise understanding of accessibility to hospitals, transit time on Baidu Maps’ navigation service was used as the impedance condition, and the study area was divided into 1 square kilometer grids as the basic unit of research. Taking Nanjing city as a case study, it is found that the accessibility distribution shape changes from a multi-centered circular pattern to a multi-peak distribution, as the time threshold increases. By comparing the accessibility among 11 districts varying from main urban area to suburbs, the accessibility to general hospitals in Nanjing is significantly regionally unbalanced in both travel modes. By calculating and mapping the Modal Accessibility Gap (MAG) of the two travel modes, different modes of transportation resulted in different general hospital accessibility distributions. Generally, private car is superior in access to general hospitals to public transit in most areas. In the central area, public traffic may not contribute to the access to medical services as much as we thought, rather it plays a role in areas far from hospitals along metro lines and bus routes.

Spatial Difference and Equity Analysis for Accessibility to Three-Level Medical Services Based on Actual Medical Behavior in Shaanxi, China

“…So, the space-time threshold range had to be appropriately expanded to ensure that the model results were more accurate. Time threshold was set to 2 h with reference to other research results [ 30 , 31 ], and the corresponding spatial threshold was 10 km which is a 2 h walking distance.…”

Analyzing Access to Health Facilities by Road Using Unconventional Data Sources

“…By developing a quantitative understanding for the ease with which residents in a given region can physically reach a particular service or facility, distribution of healthcare and equity in access can be understood. Attributes that are taken into consideration for these studies include population density, travel time, distance cost, transportation modes, geographic barriers, and facility capability (i.e., number of patient beds) ( 3 ). Various computational methods have been developed to model access, with two-step floating catchment area (2SFCA) being widely accepted in the health community.…”

“…Various computational methods have been developed to model access, with two-step floating catchment area (2SFCA) being widely accepted in the health community. 2SFCA allows for the number of health facilities available to be considered alongside travel impedance, allowing for a supply–demand approach ( 3 , 4 ).The challenge is that these methodologies, while already a technical challenge for implementation by health departments in advanced economies, tend to be well outside the traditional skill set of health department staff in emerging economies, where underlying data and computing resources are scarce. So, the team looked to the transport sector to identify more accessible tools and data sources.…”

Spatial Accessibility Analysis of Medical Facilities Based on Public Transportation Networks

“…Therefore, it is even more practical to assess the accessibility of medical facilities via public transport. In the current research on the accessibility of public transportation, scholars have established road network datasets using urban pedestrian roads [ 9 ], buses [ 10 , 11 ], rail transit [ 12 , 13 ], and bicycles [ 14 ]. Most scholars compare and analyze the accessibility results under different modes of travel [ 15 ].…”