Evaluating Spatial Inequality of Healthcare in Process of Rapid Urbanization in China By Using Remote Sensing and GIS

China is experiencing rapid urbanization with fast growing population migrating into major cities such as Beijing, Shanghai, etc. Spatial inequality of healthcare is amongst other explicit and distinct impacts of rapid urbanization. On the one hand, growing population in cities increased urban demand for healthcare and on the other hand, good physicians in surrounding regions are strongly attracted to major cities for higher salaries and other precious resources that could only provided by major cities such as better education for kids, more convenient lifestyle and so on, and that exacerbated the spatial inequality of healthcare. In China, some people in small cities would rather to take a trip over 4 hours to seek treatment in the regional major city because they do not trust local physicians. Measuring spatial inequality of healthcare and pinpointing areas inter- and intra- cities is important in planning a sustainable city circle.

Although various methods exploiting GIS have been proposed, indicators calculated with zonal data based on administrative borders, leading to important intra- and inter-relational limitations. Two-step floating catchment area (2SFCA) method partly overcomes these limitations to determine catchment areas. However, more precise spatial distribution of population and some other spatial attributes/predicators are expected. Remote sensing could provide more profound information by analyzing land cover/land use type, or estimate population distribution and economy level through night-time light images, and so on. By using remotely sensed data, we could estimate spatial distribution of socio-economic phenomena more precisely.

As the formation of such spatial inequality is a combination of first-order and second-order process, traditional methods evaluating accessibility or other indicators are insufficient. Spatial analysis exploiting auto-correlation and multivariate analysis would greatly improve accuracy of spatial models. The result would help decision makers to understand the source of urban healthcare burden and how to alleviate spatial inequality of healthcare.