Multi-Mode Two-Step Floating Catchment Area (2SFCA) Method to Measure the Potential Spatial Accessibility of Healthcare Services

Abstract

While great progress in the development of a methodological approach to measure the accessibility of healthcare services has been made, the exclusion of the complex multi-mode travel behavior of urban residents and a rough calculation of travel costs from the origin to the destination limit its potential for making a detailed assessment, especially in urban areas. In this paper, we aim to describe and implement an enhanced method that enables the integration of multiple transportation modes into a two-step floating catchment area (2SFCA) method to estimate accessibility. We used a travel-mode choice survey, based on distance sections, to determine the complex multi-mode travel behavior of urban residents. Taking Nanjing as a study area, we proposed complete door-to-door approaches to determine every aspect of basic transportation modes. Additionally, we processed open data to implement an accurate computing of the origin-destination (OD) time cost. We applied the enhanced method to estimate the accessibility of residents to hospitals and compared it with three single-mode 2SFCA methods. The results showed that the proposed method effectively identified more accessibility details and provided more realistic accessibility values.

An Analysis of Spatial Accessibility Changes According to the Attractiveness Index of Public Libraries Using Social Media Data

“…In general, similarly to libraries, the spatial accessibility of parks, daycare centers and residential care facilities is mainly influenced by three factors: the supply of service facilities, the population demand for the service, and the travel costs between the demanding populations and the service facilities [15]. In an attempt to alleviate the overestimation of population demand, Wan et al [16] applied a method of segmenting the catchment area.…”

“…Recently, in recognition of the fact that the analysis unit should be considered when analyzing accessibility, Bryant and Delamater [18] and Wang et al [19] conducted studies in which the analysis unit was related to the distance at either the macro-or micro-level. In addition, for travel cost, the spatial accessibility of public facilities such as healthcare facilities was assessed either by only making calculations in single-car mode [20,21], or by considering multiple transportation modes [15,22], such as public transportation, cycling and walking as well as traveling by private car. In terms of supply, Tao et al [23] conducted an accessibility analysis considering hierarchical facilities, such as healthcare, education, emergency, firefighting and logistics facilities; that is, facilities that function in a multi-level system, because facilities at different levels provide heterogeneous services and interact with each other.…”

MEASURING SPATIAL HEALTHCARE FACILITIES ACCESSIBILITY USING TWO-STEP FLOATING CATCHMENT ANALYSIS (2SFCA) (Case Study: Cianjur Regency, Indonesia)

“…The first "float" is measuring the physician catchment area, while the second is measuring the population catchment area. Ni et al (2019) claim that the 2SFCA method provides a realistic way to assess the potential spatial accessibility of healthcare facilities. Measuring 2SFCA contains two main stages.…”

“…Kanuganti et al (2016) used this approach to quantify the accessibility of healthcare in Rajashtan, India. Meanwhile, Ni et al (2019) utilized this method to measure healthcare accessibility in Nanjing, China. From this standpoint, this research aims to apply 2SFCA for quantifying the healthcare accessibility in Cianjur Regency, Indonesia.…”

The effect of population distribution measures on evaluating spatial accessibility of primary health-care institutions: A case study from China

“…The 2SFCA method was first applied in industrialized countries, where the geographic or population-weighted centroid of a census tract could be adopted for defining population locations (Luo and Wang, 2003;Delamater, 2013;Delamater et al, 2019;Kaur Khakh et al, 2019), while the geometry of the county or subdistrict, alternatively a population centroid, was usually used for defining this measure in the developing countries (Tao et al, 2018b;Zhu et al, 2019). Some authors disaggregated the population data at the county or sub-district evenly (Pan et al, 2016;Wang et al, 2018;Ni et al, 2019;Zhang et al, 2019a). However, the population data at these levels are low-resolution, outdated and therefore not possible to compare (Gregory et al, 2010).…”