Distribution Of Domestic Product Research Articles

Mapping Gross Domestic Product Distribution at 1 km Resolution across Thailand Using the Random Forest Area-to-Area Regression Kriging Model

Accurate spatial distribution of gridded gross domestic product (GDP) data is crucial for revealing regional disparities within administrative units, thus facilitating a deeper understanding of regional economic dynamics, industrial distribution, and urbanization trends. The existing GDP spatial models often rely on prediction residuals for model evaluation or utilize residual distribution to improve the final accuracy, frequently overlooking the modifiable areal unit problem within residual distribution. This paper introduces a hybrid downscaling model that combines random forest and area-to-area kriging to map gridded GDP. Employing Thailand as a case study, GDP distribution maps were generated at a 1 km spatial resolution for the year 2015 and compared with five alternative downscaling methods and an existing GDP product. The results demonstrate that the proposed approach yields higher accuracy and greater precision in detailing GDP distribution, as evidenced by the smallest mean absolute error and root mean squared error values, which stand at USD 256.458 and 699.348 ten million, respectively. Among the four different sets of auxiliary variables considered, one consistently exhibited a higher prediction accuracy. This particular set of auxiliary variables integrated classification-based variables, illustrating the advantages of incorporating such integrated variables into modeling while accounting for classification characteristics.

Mapping China’s Changing Gross Domestic Product Distribution Using Remotely Sensed and Point-of-Interest Data with Geographical Random Forest Model

Accurate knowledge of the spatiotemporal distribution of gross domestic product (GDP) is critical for achieving sustainable development goals (SDGs). However, there are rarely continuous multitemporal gridded GDP datasets for China in small geographies, and less is known about the variable importance of GDP mapping. Based on remotely sensed and point-of-interest (POI) data, a geographical random forest model was employed to map China’s multitemporal GDP distribution from 2010 to 2020 and to explore the regional differences in the importance of auxiliary variables to GDP modeling. Our new GDP density maps showed that the areas with a GDP density higher than 0.1 million CNY/km2 account for half of China, mainly distributed on the southeast side of the Hu-line. The proportion of the areas with GDP density lower than 0.05 million CNY/km2 has decreased by 11.38% over the past decade and the areas with an increase of 0.01 million CNY/km2 account for 70.73% of China. Our maps also showed that the GDP density of most nonurban areas in northeast China declined, especially during 2015–2020, and the barycenter of China’s GDP moved 128.80 km to the southwest. These results indicate China’s achievements in alleviating poverty and the widening gaps between the South and the North. Meanwhile, the number of counties with the highest importance score for POI density, population density, and nighttime lights in GDP mapping accounts for 52.76%, 23.66%, and 23.56%, respectively, which suggests that they play a crucial role in GDP mapping. Moreover, the relationship between GDP and auxiliary variables displayed obvious regional differences. Our results provide a reference for the formulation of a sustainable development strategy.

O contexto do transplante renal no Brasil e sua disparidade geográfica

The Brazilian National Transplantation System coordinates and regulates perhaps the largest public transplantation program worldwide. Since its implementation in 1997, the number of kidney transplantations increased from 920 (5.8 pmp) in 1998, to 4,630 (24.1 pmp) in 2010. This growth was primarily due to the increased number of effective donors (from 1.8 pmp in 1998 to 9.3 pmp in 2010), with a corresponding increased number of kidneys transplanted from deceased donors (3.8 pmp in 1999 versus 9.9 pmp in 2010).The number of kidney transplantations from living donors has not increased significantly, from 1,065 (6.7 pmp) in 1998 to 1,641 (8.6 pmp) in 2010, either as a consequence of the observed increase in the deceased donor program or perhaps because of strict government regulations allowing only transplantations from related donors. From 2000 to 2009, the mean age of living donors increased from 40 to 45 years, while it increased from 33 to 41 years for deceased donors, of whom roughly 50% die of stroke. There are clear regional disparities in transplantation performance across the national regions. While the state of São Paulo is ranked first in organ donation and recovery (22.5 pmp), some states of the Northern region have much poorer performances. These disparities are directly related to different regional population densities, gross domestic product distribution, and number of trained transplantation physicians. The initial evaluation of the centers with robust outcomes indicates no clear differences in graft survival in comparison with centers in the USA and Europe. Ethnicity and time on dialysis, but not the type of immunosuppressive regimen, decisively influence the measured outcomes. Since the implementation of national clinical research regulations in 1996, Brazilian centers have participated in a number of national and international collaborative trials for the development of immunosuppressive regimens. Besides the challenge of reducing the regional disparities related to access to transplantation, further improvements can be obtained by the creation of a national registry of the outcomes of transplanted patients and living donors, and also by the promotion of clinical and experimental studies to better understand the transplantation-related immune response of the Brazilian population.