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. 2018 Aug 23;17(1):33.
doi: 10.1186/s12942-018-0153-9.

A multi-modal relative spatial access assessment approach to measure spatial accessibility to primary care providers

Yan Lin  1 , Neng Wan  2 , Sagert Sheets  3 , Xi Gong  3 , Angela Davies  3
Affiliations

A multi-modal relative spatial access assessment approach to measure spatial accessibility to primary care providers

Yan Lin et al. Int J Health Geogr. .

Abstract

Two-step floating catchment area (2SFCA) methods that account for multiple transportation modes provide more realistic accessibility representation than single-mode methods. However, the use of the impedance coefficient in an impedance function (e.g., Gaussian function) introduces uncertainty to 2SFCA results. This paper proposes an enhancement to the multi-modal 2SFCA methods through incorporating the concept of a spatial access ratio (SPAR) for spatial access measurement. SPAR is the ratio of a given place's access score to the mean of all access scores in the study area. An empirical study on spatial access to primary care physicians (PCPs) in the city of Albuquerque, NM, USA was conducted to evaluate the effectiveness of SPAR in addressing uncertainty introduced by the choice of the impedance coefficient in the classic Gaussian impedance function. We used ESRI StreetMap Premium and General Transit Specification Feed (GTFS) data to calculate the travel time to PCPs by car and bus. We first generated two spatial access scores-using different catchment sizes for car and bus, respectively-for each demanding population location: an accessibility score for car drivers and an accessibility score for bus riders. We then computed three corresponding spatial access ratios of the above scores for each population location. Sensitivity analysis results suggest that the spatial access scores vary significantly when using different impedance coefficients (p < 0.05); while SPAR remains stable (p = 1). Results from this paper suggest that a spatial access ratio can significantly reduce impedance coefficient-related uncertainties in multi-modal 2SFCA methods.

Keywords: 2SFCA; E2SFCA; Gaussian function; Multi-modal; Primary care; Spatial access.

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Figures

Fig. 1
Fig. 1
The study area of the Albuquerque Metropolitan Area in Central New Mexico
Fig. 2
Fig. 2
Multi-modal network
Fig. 3
Fig. 3
Spatial distribution of Spatial Access Index Scores for car travel in the study area
Fig. 4
Fig. 4
Spatial distributions of Spatial Access Ratio Scores for car travel in the study area
Fig. 5
Fig. 5
Spatial distribution of Spatial Access Index Scores for bus travel in the study area
Fig. 6
Fig. 6
Spatial distribution of Spatial Access Ratio Scores for bus travel in the study area
Fig. 7
Fig. 7
Spatial distribution of Integrated Spatial Access Index (SPAI) scores for both car and bus travel in the study area
Fig. 8
Fig. 8
Spatial distribution of Integrated Spatial Access Ratio (SPAR) scores for both car and bus travel in the study area
Fig. 9
Fig. 9
Sensitivity analysis results of Spatial Access Index for car travel
Fig. 10
Fig. 10
Sensitivity analysis Results of Spatial Access Index for bus travel
Fig. 11
Fig. 11
Sensitivity analysis results of integrated (bus and car) Spatial Access Index
See this image and copyright information in PMC

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