Using the Fusion Proximal Area Method and Gravity Method to Identify Areas with Physician Shortages

Abstract

ObjectivesThis paper presents a geographic information system (GIS)-based proximal area method and gravity method for identifying areas with physician shortages. The innovation of this paper is that it uses the appropriate methods to discover each type of health resource and then integrates all these methods to assess spatial access to health resources using population distribution data. In this way, spatial access to health resources for an entire city can be visualized in one neat package, which can help health policy makers quickly comprehend realistic distributions of health resources at a macro level.MethodsFirst, classify health resources according to the trade areas of the patients they serve. Second, apply an appropriate method to each different type of health resource to measure spatial access to those resources. Third, integrate all types of access using population distribution data.ResultsIn case study of Shanghai with the fusion method, areas with physician shortages are located primarily in suburban districts, especially in district junction areas. The result suggests that the government of Shanghai should pay more attention to these areas by investing in new or relocating existing health resources.ConclusionThe fusion method is demonstrated to be more accurate and practicable than using a single method to assess spatial access to health resources.

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

“…Shanghai is considered to be the most populated and dense community in the world (by urban area inhabitants), and a significant international center for trade, trade, tourism and fashion with a population of around 24.15 million people. In 2016, Shanghai is divided into 16 county-level divisions: 15 districts (Baoshan, Changning, Fengxian, Hongkou, Huangpu, Jiading, Jingan, Jinshan, Minhang, Pudong New Area, Putuo, Qingpu, Songjiang, Xuhui, and Yangpu) and 1 county (Chongming) [84]. Seven of the districts (Changning, Hongkou, Huangpu, Jingan, Putuo, Xuhui, and Yangpu) are located in Puxi (literally Huangpu West).…”

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

“…According to Gu, X., S. Tao, and B. Dai [82], in 2015, Shanghai had a total area of 8359 km 2 with a population of around 24.15 million people. In 2016, Shanghai was divided into 16 county-level divisions: 15 districts (Baoshan, Changning, Fengxian, Hongkou, Huangpu, Jiading, Jingan, Jinshan, Minhang, Pudong New Area, Putuo, Qingpu, Songjiang, Xuhui, and Yangpu) and 1 county (Chongming) [83]. Seven of the districts (Changning, Hongkou, Huangpu, Jingan, Putuo, Xuhui, and Yangpu) are located in Puxi (literally Huangpu West).…”

Assessment of the walking accessibility of public libraries in Krakow using the E2SFCA approach

“…As X. Xiong et al (2016) mention, irrespective of the data which researchers also need for the other approach (e.g. locations of supply and demand, traffic network), knowledge about travel times must be present.…”

“…For example, they quote K. J. Cole and A. C. Gatrell (1986) who were using the gravity 2 For more details about the gravity model see Xiong et al (2016).…”