Evaluating the Inequality of Medical Service Accessibility Using Smart Card Data

Abstract

The measurement of medical service accessibility is typically based on driving or Euclidean distance. However, in most non-emergency cases, public transport is the travel mode used by the public to access medical services. Yet, there has been little evaluation of the public transport system-based inequality of medical service accessibility. This work uses massive real smart card data (SCD) and an improved potential model to estimate the public transport-based medical service accessibility in Beijing, China. These real SCD data are used to calculate travel costs in terms of time and distance, and medical service accessibility is estimated using an improved potential model. The spatiotemporal variations and patterns of medical service accessibility are explored, and the results show that it is unevenly spatiotemporally distributed across the study area. For example, medical service accessibility in urban areas is higher than that in suburban areas, accessibility during peak periods is higher than that during off-peak periods, and accessibility on weekends is generally higher than that on weekdays. To explore the association of medical service accessibility with socio-economic factors, the relationship between accessibility and house price is investigated via a spatial econometric analysis. The results show that, at a global level, house price is positively correlated with medical service accessibility. In particular, the medical service accessibility of a higher-priced spatial housing unit is lower than that of its neighboring spatial units, owing to the positive spatial spillover effect of house price. This work sheds new light on the inequality of medical service accessibility from the perspective of public transport, which may benefit urban policymakers and planners.

Keywords: accessibility; inequality; medical service; smart card data.

Figure 1

(a) Study area within the 6th Ring Road in Beijing, China; (b) expanded study area, showing its 2745 hexagonal (1 km × 1 km) spatial units.

Figure 2

The spatial distribution of the 192 hospitals in the study area colored by (a) their class and (b) their number of sickbeds.

Figure 3

The distribution of public transport stations in Beijing: (a) bus stops; (b) subway stations.

Figure 4

The schematic diagram of the derived origin and destination matrix (where OD = origin–destination, O–ID = origin identifier, D–ID = destination identifier).

Figure 5

The spatial distribution of (a) population and (b) the house price of each spatial unit.

Figure 6

Workflow of the research.

Figure 7

The relationship between potential patients and (a) travel costs and (b) travel time.

Figure 8

Distribution of medical accessibility in the study area.

Figure 9

Medical service accessibility in different time periods, where (a,c,e) show the weekday dynamic accessibility and (b,d,f) show the weekend dynamic accessibility.

Figure 9

Medical service accessibility in different time periods, where (a,c,e) show the weekday dynamic accessibility and (b,d,f) show the weekend dynamic accessibility.

Figure 10

Local indicators for spatial association cluster maps of hospital accessibility. (a) Moran’s I of the overall patterns in accessibility; (b,d,f) Moran’s I of weekday accessibility in different time periods; and (c,e,g) Moran’s I of weekend accessibility in different periods.

Figure 10

Local indicators for spatial association cluster maps of hospital accessibility. (a) Moran’s I of the overall patterns in accessibility; (b,d,f) Moran’s I of weekday accessibility in different time periods; and (c,e,g) Moran’s I of weekend accessibility in different periods.