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Spatial distribution of rural population using mixed geographically weighted regression: Evidence from Jiangxi Province in China.

ABSTRACT: On the basis of the spatial panel data of 2000, 2005, 2010, and 2015, this study uses a mixed geographically weighted regression model to explore the spatial distribution characteristics and influencing factors of the rural (permanent) population in Jiangxi Province, China. Results show that residents in the county area have a significant spatial positive autocorrelation, especially in the lake and mountain areas and the global Moran' I index is more than 0.05. The influence of social and economic factors presents spatial homogeneity. The effect of urbanization and per capita disposable income is negative, whereas that of agricultural output value and rural electricity consumption is positive. The influence of climate factors presents spatial heterogeneity. The influence coefficient of rainfall in 2015 ranges from [-0.061, 0.133], which has a negative effect on the southwest mountain areas and a positive effect on the northeast lake areas., The influence coefficient of temperature in 2015 ranges from [-0.110, 0.094], which has a positive effect on the southwest mountain areas and a negative effect on the northeast lake areas. The influence coefficients of wind speed and relative humidity range from [-0.090, 0.153] and [-0.069, 0.130] in 2015 respectively, which further reinforce this effect. Therefore, scholars should pay attention to the universal adaptability of economic and social factors. Moreover, they should consider the spatial difference of climatic factors to promote urbanization following the local conditions. Finally, policymakers and concerned non-governmental institutions should fully understand the sensitivity of the rural population in underdeveloped mountain areas to climate factors to promote their rational distribution.

SUBMITTER: Zhang L

PROVIDER: S-EPMC8075265 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIn developing countries, the death probability of a child and mother is more significant than in developed countries; these inequalities in health outcomes are unfair. The present study encompasses a spatial analysis of maternal and child mortalities in Pakistan. The study aims to estimate the District Mortality Index (DMI), measure the inequality ratio and slope, and ascertain the spatial impact of numerous factors on DMI scores across Pakistani districts.MethodThis study used micro-level household datasets from multiple indicator cluster surveys (MICS) to estimate the DMI. To find out how different the DMI scores were, the inequality ratio and slope were used. This study further utilized spatial autocorrelation tests to determine the magnitude and location of the spatial dependence of the clusters with high and low mortality rates. The Geographically Weighted Regression (GWR) model was also applied to examine the spatial impact of socioeconomic, environmental, health, and housing attributes on DMI.ResultsThe inequality ratio for DMI showed that the upper decile districts are 16 times more prone to mortalities than districts in the lower decile, and the districts of Baluchistan depicted extreme spatial heterogeneity in terms of DMI. The findings of the Local Indicator of Spatial Association (LISA) and Moran's test confirmed spatial homogeneity in all mortalities among the districts in Pakistan. The H-H clusters of maternal mortality and DMI were in Baluchistan, and the H-H clusters of child mortality were seen in Punjab. The results of GWR showed that the wealth index quintile has a significant spatial impact on DMI; however, improved sanitation, handwashing practices, and antenatal care adversely influenced DMI scores.ConclusionThe findings reveal a significant disparity in DMI and spatial relationships among all mortalities in Pakistan's districts. Additionally, socioeconomic, environmental, health, and housing variables have an impact on DMI. Notably, spatial proximity among individuals who are at risk of death occurs in areas with elevated mortality rates. Policymakers may mitigate these mortalities by focusing on vulnerable zones and implementing measures such as raising public awareness, enhancing healthcare services, and improving access to clean drinking water and sanitation facilities.

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Spatial distribution of rural population using mixed geographically weighted regression: Evidence from Jiangxi Province in China.

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