# Health Care Accessibility Analysis Considering Behavioral Preferences for Hospital Choice
## 3 Case Study and Analysis
### Services Accessibility Calculation  
First, the traditional Ga2 SFCA method was used to calculate the accessibility of 3 AH services for each community using \(d_{l}\) as the input parameter, and the calculation results were stored as an attribute of each community. The population of the study area is much larger than the number of beds, resulting in a small bed supply-to-demand ratio, for example, \(\frac{58910}{4398894}=0.013\) for the whole study area, so the final calculated accessibility was also small. Since the calculated values of accessibility were very small, all calculated values were uniformly multiplied by 1000 in this paper, which was convenient for future analysis, as shown in Figure 2(a).  
Secondly, community polygons were transformed into points located within the corresponding polygons, and the attribute terms of the communities were retained.  
Finally, the kriging interpolation algorithm [35] was used to convert the discrete points into continuous trend surfaces. The coordinates of community points were used as location information, and the corresponding community accessibility was used as an input parameter for spatial interpolation. The interpolation cell size was 100 m, which allowed for fine analysis results. The calculation results were classified into 8 levels according to the natural breakpoint method, as shown in Figure 2(b).  
In the real world, it is common for different communities to cover different sized areas. For example, in general, the further away from the city center, the larger the area covered by the community. In terms of spatial distribution, any two points within a small community are closer, so internal accessibility is less affected by distance. In addition, its accessibility is finer than that of a large community because no other processing is required, and the calculation results are used directly for visualization, which is more realistic. However, this method ignores the influence of distance and is not suitable for situations with large differences in the area of basic units. For example, for a large community, it is not accurate to use only one value to represent the accessibility of the whole area. Because the distances to the hospital in the central and peripheral areas often differ greatly, their accessibility is definitely not the same. Specifically, as in the yellow community at the bottom of Figure 3a, there is one hospital in a large community, but this calculation with the community as the basic unit uses only one value to represent the accessibility of the whole community. The results of this large homogeneous accessibility calculation that ignores the effect of distance can easily lead to spatial misunderstandings that can influence government decisions.  
The result of spatial interpolation of the data in (a) is shown in Figure 3b. It can be seen that, firstly, the spatial distribution patterns of both are basically the same. Then, as shown in (b), the interpolated results retain the spatial distribution characteristics of accessibility of small communities. Meanwhile, the accessibility of large communities achieves refinement by distance, which is more in line with the public perception.  
To overcome the shortcomings of direct calculation with communities as the basic unit, later studies in this paper consider the effect of distance and reprocess the accessibility calculation results by spatial interpolation to ensure that the continuous accessibility varies more finely with distance.  
The improved method follows similar steps to complete the calculation, which use ds and dt as the input parameters.