Analysis of Regional Economic Disparities in Guizhou Province Based on ESDA-GIS
Wang Haili, Yuan Tian-feng, Hu Xiaodong, Qu Xiaobin
Abstract
Open-access reader
Wang Haili, Yuan Tian-feng, Hu Xiaodong, Qu Xiaobin
Abstract
Open-access reader
Take the county as the research scale and the per capita GDP as the measure index as well to reveal the difference of Guizhou Province’s regional economy which based on ESDA and GeoDA-GIS. It shows that the level of economic develop of Guizhou’s central area is high and surrounded area is low. The difference between North and south is greater than the difference between East and West. There is a clear spatial correlation among them. Moran scatter diagram shows that the majority of counties are located in the first and third quadrants, which accounted for 73.86% of the total number of the county. The number of “L-L” type is more than the number of “H-H” type 37 counties. Most parts of the provinces are relatively poor. Finding the “H-H” area and “L-L” area and “L-H” area and “H-L” area of economic development level of county based on the spatial correlation model. That can provide scientific basis for the future economic construction and social development of Guizhou province.
OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Take the county as the research scale and the per capita GDP as the measure index as well to reveal the difference of Guizhou Province’s regional economy which based on ESDA and GeoDA-GIS. It shows that the level of economic develop of Guizhou’s central area is high and surrounded area is low. The difference between North and south is greater than the difference between East and West. There is a clear spatial correlation among them. Moran scatter diagram shows that the majority of counties are located in the first and third quadrants, which accounted for 73.86% of the total number of the county. The number of “L-L” type is more than the number of “H-H” type 37 counties. Most parts of the provinces are relatively poor. Finding the “H-H” area and “L-L” area and “L-H” area and “H-L” area of economic development level of county based on the spatial correlation model. That can provide scientific basis for the future economic construction and social development of Guizhou province.
Key concepts: Geography, Scatter plot, Per capita, Scale (ratio), Socioeconomics, Regional science, Cartography, Demography