Distribution Of Gross Domestic Product Research Articles

The dynamic impact of land use change on ecosystem services as the fast GDP growth in Guiyang city

The rapid growth of gross domestic product (GDP) in developing countries may lead to the overconsumption of ecosystem services (ESs). This situation presents as the imbalance of ecological conservation and economic development, as is the case in areas of West China, such as Guiyang city. This study utilized the PLUS model to simulate future land use/land cover (LULC) scenarios and the InVEST model to assess the ESs in relation to rapid GDP growth in Guiyang city. The research findings reveal the following: 1) the growth of developed land contributes to ecosystem degradation and increased potential ecological hazards. The implementation of GDP growth policies may be the potential driver of land use change and a factor influencing changes in ESs. For example, between 2010 and 2020, the area of developed land increased by 339%, but the soil retention and carbon storage decreased by 2.04% and 3.26%, respectively. 2) The changes in GDP and ESs showed spatial heterogeneity. From 2020 to 2030, the general hot spot distribution of ESs and GDP was consistent, which was closely related to land use change. 3) Trade-offs and synergistic effects exist between GDP and ESs, including a trade-off between GDP and soil retention and synergy between GDP and water yield. GDP growth comes at the expense of ESs. The study emphasizes the need to prioritize ecological conservation in the degraded areas of southern Guiyang city. It urges stronger management of ecological redlines, stricter construction land policies, and a balanced approach to economic development and ecological protection.

A dataset of GDP spatial distribution on the Qinghai-Tibet Plateau (2020)

Economic spatial distribution data serve as an indicator for disaster loss assessment. The economy of the Qinghai-Tibet Plateau is relatively backward and geological disasters occur frequently, so it is of great significance to study the impact of disasters on the economy in this area. Based on the method of zoning and industry simulation, this study establishes the corresponding relationship between the GDP1（GDP of Primary Industry）and land use types, DEM（Digital Elevation Model）, village points, roads and river buffers. Spatial models of night lighting, building site and points of interest data after the coupling of the GDP23（GDP of Secondary and Tertiary Industries）and multiple information are established in urban areas and counties. We used the random forest to determine the weights of various indicators in the secondary and tertiary industries, and calculated the grid value of GDP23. Finally, we superimposed the grid of GDP1 and GDP23 and created the spatial distribution map of GDP（Gross Domestic Product）of 100 m×100 m on the Qinghai-Tibet Plateau in 2020. According to the accuracy verification using county-level GDP statistical data, the Root Mean Square Error is 0.0385, which shows that the data can better reflect the spatial distribution of GDP on the Qinghai-Tibet Plateau. This dataset can provide GDP spatial data support for disaster risk assessment, disaster prevention and mitigation on the Qinghai-Tibet Plateau.

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.

Analysis of spatial–temporal dynamic distribution and related factors of tuberculosis in China from 2008 to 2018

Through spatial–temporal scanning statistics, the spatial–temporal dynamic distribution of pulmonary tuberculosis incidence in 31 provinces and autonomous regions of China from 2008 to 2018 is obtained, and the related factors of spatial–temporal aggregation of tuberculosis in China are analyzed to provide strong scientific basis and data support for the prevention and control of pulmonary tuberculosis. This is a retrospective study, using spatial epidemiological methods to reveal the spatial–temporal clustering distribution characteristics of China's tuberculosis epidemic from 2008 to 2018, in which cases data comes from the China Center for Disease Control and prevention. Office Excel is used for general statistical description, and the single factor correlation analysis adopts χ2 Test (or trend χ2 Inspection). Retrospective discrete Poisson distribution space time scanning statistics of SaTScan 9.6 software are used to analyze the space time dynamic distribution of tuberculosis incidence in 31 provinces, cities and autonomous regions in China from 2008 to 2018. ArcGIS 10.2 software is used to visualize the results. The global spatial autocorrelation analysis adopts Moran's I of ArcGIS Map(Monte Carlo randomization simulation times of 999) is used to analyze high-risk areas, low-risk areas and high-low risk areas. From 2008 to 2018, 10,295,212 cases of pulmonary tuberculosis were reported in China, with an average annual incidence rate of 69.29/100,000 (95% CI: (69.29 ± 9.16)/100,000). The annual GDP (gross domestic product) of each province and city showed an upward trend year by year, and the number of annual medical institutions in each province and city showed a sharp increase in 2009, and then tended to be stable; From 2008 to 2018, the national spatiotemporal scanning statistics scanned a total of 6 clusters, including 23 provinces and cities. The national high-low spatiotemporal scanning statistics of the number of pulmonary tuberculosis cases scanned a total of 2 high-risk and low-risk clusters. The high-risk cluster included 8 provinces and cities, and the low-risk cluster included 12 provinces and cities. The global autocorrelation Moran's I index of the incidence rate of pulmonary tuberculosis in all provinces and cities was greater than the expected value (E (I) = −0.0333); The correlation analysis between the average annual GDP and the number of pulmonary tuberculosis cases in each province and city from 2008 to 2018 was statistically significant. From 2008 to 2018, the spatial and temporal scanning and statistical scanning areas of tuberculosis incidence in China were mainly concentrated in the northwest and southern regions of China. There is an obvious positive spatial correlation between the annual GDP distribution of each province and city, and the aggregation degree of the development level of each province and city is increasing year by year. There is a correlation between the average annual GDP of each province and the number of tuberculosis cases in the cluster area. There is no correlation between the number of medical institutions set up in each province and city and the number of pulmonary tuberculosis cases.

A spatial distribution dataset for GDP in Hainan Island at 500 m from 2012 to 2020

Based on ESA (European Space Agency) landcover data, national polar-orbiting partnership-visible infrared imaging radiometer suite (NPP-VIIRS) nighttime light data and Hainan provincial statistical data, we implemented the spatialization of Hainan Island’s Gross Domestic Product (GDP) in this article. According to the relationship between gross product of primary industry and landcover type, spatialization of Hainan Island gross product of primary industry was implemented. And by building the regression model between gross product of secondary and tertiary industries and the nighttime light index, Hainan Island gross product of secondary and tertiary industries is spatialized. Then the spatialization of GDP was realized by combining the results of spatialization of Hainan Island's primary industry, secondary and tertiary industries. Compared with Hainan provincial statistical data, the results show that the maximum relative errors of spatial distribution data and actual statistical data of gross product of primary industry, gross product of secondary and tertiary industries and GDP in each period are 3.25%, 9.57% and 7.49%, respectively, which indicates the dataset can better describe the spatial distribution of GDP in Hainan Island. In addition, this dataset can provide a decision-making basis for the government to formulate policies on economic development of Hainan Island, and provide data support for scholars to explore differences and trends in economic development of Hainan Island.

Characterizing the sectoral development of cities.

Previous research has identified a predictive model of how a nation’s distribution of gross domestic product (GDP) among agriculture (a), industry (i), and services (s) changes as a country develops. Here we use this national model to analyze the composition of GDP for US Metropolitan Statistical Areas (MSA) over time. To characterize the transfer of GDP shares between the sectors in the course of economic development we explore a simple system of differential equations proposed in the country-level model. Fitting the model to more than 120 MSAs we find that according to the obtained parameters MSAs can be classified into 6 groups (consecutive, high industry, re-industrializing; each of them also with reversed development direction). The consecutive transfer (a → i → s) is common but does not represent all MSAs examined. At the 95% confidence level, 40% of MSAs belong to types exhibiting an increasing share of GDP from agriculture. In California, such MSAs, which we classify as part of an agriculture renaissance, are found in the Central Valley.

Budget Regulation as a Key Area of Public Financial Activity

The paper is devoted to the consideration of issues related to the value of the legal mechanism of budgetary regulation for the management of economic and social processes in a modern innovative society. The conceptual basis of the study was formed by the regularities of distributive financial relations that are objective in nature. Acting with the help of the rules of law regulating financial relations, the federal state through its public apparatus, on the one hand, regulates these relations, and on the other hand, forms, on the basis of these relations, financial and legal regulatory mechanisms for managing society as a whole. It has been established that the finance (the financial system), public financial activity and financial law constitute interrelated and interdependent elements of a uniform system that determines the distribution of gross domestic product in the state. For public financial activities aimed at distribution and redistribution of funds within the budgetary system of the Russian Federation, the mechanism of budget regulation governed by the rules of financial law is of key importance. In the course of the study, the author highlights various features of distributive financial relations in a federal state, examines approaches to the characterization of public financial activity, focuses on aspects of the concept of "budget regulation", and analyzes the significance of this mechanism for maintaining the stability of a federal state. The author concludes that groupings of financial law norms regulating certain types of homogeneous interbudgetary public relations associated with the distribution and redistribution of funds within the budgetary system of the Russian Federation constitute separate legal forms (methods) of budgetary regulation subdivided into primary and secondary forms (methods). It is concluded that in an innovative society, budget regulation is of key importance in the process of public financing, de facto performing the role of a universal management tool that ensures a socio-economic balance in the society.

Construction Sector Contribution to Economic Stability: Malaysian GDP Distribution

The construction sector exerts an exceptional impact on economic development all over the world. Adequate buildings and infrastructures made by the construction sector ensure that a country reaches certain targets like social development, industrialization, freight transportation, sustainable development, and urbanization. This study aims to determine the construction sector’s connectivity with other sectors through complex linkages that contribute immensely to the economy and gross domestic product (GDP). The data were collected from the Department of Statistics Malaysia and the World Bank from the year 1970 to 2019, and the Pearson correlation test, the cointegration test, and the Granger causality test were conducted. The vector error correction model (VECM) was created for short-term and long-term equilibrium analysis and impulse response function (IRF) was performed to study construction industry behavior. Afterwards, the forecasting was done for the year 2020 to 2050 of the Malaysian economy and GDP for the required sectors. It was revealed that some sectors, such as agriculture and services, have forward linkages while other sectors, such as manufacturing and mining, are independent of construction sector causality, which signifies the behavior of the contributing sectors when a recession occurs, hence generating significant revenue. The Malaysian economy is moving towards sustainable production with more emphasis on the construction sector. The outcome can be used as a benchmark by other countries to achieve sustainable development. The significance of this study is its usefulness for experts all over the world in terms of allocating resources to make the construction sector a sustainable sector after receiving a shock. A sustainable conceptual framework has been suggested for global application that shows the factors involved in the growth of the construction industry to ensure its sustainable development with time.

Visualizing the geographical network of inter-provincial electricity in China

ABSTRACT This paper empirically visualizes the spatial patterns of the inter-provincial electricity flows in relation to the distribution of gross domestic product (GDP) per capita using spatial geographical information system (GIS) and cartograms. It highlights how developed provinces in eastern China are net importers of electricity from western and central China, which creates a west–east power transmission pattern and helps deepen the regional economy disparity.

ДЕРЖАВНІ ФІНАНСИ В ДЕМОКРАТИЧНОМУ СУСПІЛЬСТВІ

Democratization of public relations requires the creation of appropriate conditions for the functioning of public finances, which is the key to economic growth and the foundation of its development, as well as further improving the material well-being of the population. They are one of the key and most important areas of the financial system of the state, through which and through which the system of distribution of gross domestic product between business structures and territories, and various segments of the population. The article considers public finance as one of the main components of the financial system of each country. Their role in providing the state with its inherent functions in relation to social, economic and political development has been studied. Scientific approaches to the interpretation of the essence of the concept of “public finance” are described, on the basis of which the own position of their definition is substantiated. The key conditions for the functioning of public finances in a democratic society are highlighted. Under today's conditions, there is a process of rethinking the essence of public finance in a democratic society. The development of theoretical views on the role of public finance in a democratic society is associated with a change in public influence on the economy. On the basis of the outlined problems of public finances in a democratic society, the priority directions of their improvement in the conditions of economic transformations are determined. It was emphasized that the optimization in the field of public finance will stimulate the development of entrepreneurial activity, promote the growth of living standards of citizens, will improve the quality of public services and achieve social harmony in society. Today the issue of modernization of public finances is relevant, because the high level of tax burden, low level of social justice of the tax system, inefficient and irrational distribution of budget funds, low level of social security of vulnerable groups and social standards are a consequence of improper functioning of public finances in the context of redistributive processes in the country. In modern conditions, the ability of the state to perform its functions and promote the socio-economic growth of society depends on their effective functioning.

Mapping China’s regional economic activity by integrating points-of-interest and remote sensing data with random forest

Nighttime light imageries are widely used for mapping the gross domestic product (GDP) over large areas. However, nighttime light imagery is inappropriate to disaggregate agricultural GDP and inadequate to differentiate the GDP from the secondary and tertiary sectors. Points-of-interest, a kind of geospatial big data with geographic locations and textual descriptions of the category, can effectively distinguish industrial and commercial areas, and therefore have the potential to improve the precise GDP mapping from secondary and tertiary sectors. In this study, a machine learning method, random forest, was used to disaggregate the 2010 county-level census GDP data of mainland China to 1 km × 1 km grids. Six Random Forest models were constructed for different economic sectors to explore the non-linear relationships between various geographic predictors and GDP from different sectors. By fusing points-of-interest of varying categories, the spatial distribution of economic activities from the secondary and tertiary sectors was effectively distinguished. Compared to previous studies, the strategy of developing specific Random Forest models for different sectors generated a more reasonable distribution of GDP. Our results highlight the feasibility of using point-of-interest data in disaggregating non-agricultural GDP by exploiting the complementary features of the different data sources.

Global socioeconomic exposure of heat extremes under climate change

Growing evidence indicates that the risk of heat extremes will increase as climate change progresses and create a significant threat to public health and the economy. Socioeconomic exposure is the key component for assessing the risk of such events. To quantify socioeconomic exposure to heat extremes for 2016–2035 and 2046–2065, we use the projections of five global climate models forced by using three representative concentration pathways (RCPs) and projections of population and gross domestic product (GDP), and we take into account the geographic change in the distribution in shared socioeconomic pathways (SSPs). The exposure of the global population for 2046–2065 is the greatest under the RCP8.5-SSP3 scenario, up to 1037(±164) × 109 person-days, and the global GDP exposure for 2046–2065 is greatest under the RCP2.6-SSP1 scenario, up to 18(±2) × 1015 dollar-days. Asia has the highest exposure among all continents for both population and GDP, accounting for over half of the global exposure. Africa has the largest increase in exposure, with the annual population and GDP exposures increasing by over 9- and 29-fold, respectively, compared with the base period (1986–2005). The effect of climate makes the dominant contribution (47%–53%) globally for the change in population exposure. Changes in the geographic distribution of GDP cause nearly 50% of the total change in GDP exposure for 2016–2035. Mitigating emissions of greenhouse gases, either at the level of the RCP2.6 scenario or at a more ambitious target, is essential for reducing socioeconomic exposure to heat extremes. In addition, designing and implementing effective measures of adaptation are urgently needed in Asia and Africa to aid socioeconomic systems suffering from heat extremes due to climate change.

Research on the relationship between Urban economic development level and urban spatial structure-A case study of two Chinese cities.

The characteristics of urban spatial structure and the objective evaluation of the development level of urban economy have always been the concern of urban researchers, However, the spatial relationship between urban spatial structure and urban economic development level is often deliberately ignored. Through the point of interest (POI), the identification framework is constructed, the spatial structure of the city is identified and evaluated, and the Geographically Weighted Regression analysis is carried out with the distribution of unit GDP (Gross Domestic Product) in this study. The research shows that Kunming and Guiyang are polycentric spatial structures and Kunming's structure is more significant. Kunming's economic level is generally higher than Guiyang, but the unit area cannot be compared. The city center will promote the development of the central area in this city, and the more urban centers are distributed within the geographical and spatial range, the greater contribution would have to economic development. In addition, the results of this study will have a positive impact on urban planning and construction, and will also provide a new perspective for the study of cities and related disciplines.

Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods

Nighttime light (NTL) data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar Orbiting Partnership (NPP) satellite, has been widely used to evaluate gross domestic product (GDP). Nevertheless, due to the monthly VIIRS data fluctuation and missing data (excluded by producers) over high-latitude regions, the suitability of VIIRS data for longitudinal city-level economic estimation needs to be examined. While GDP distribution in China is always accompanied by regional disparity, previous studies have hardly considered the spatial autocorrelation of the GDP distribution when using NTL imagery. Thus, this paper aims to enhance the precision of the longitudinal GDP estimation using spatial methods. The NTL images are used with road networks and permanent resident population data to estimate the 2013, 2015, and 2017 3-year prefecture-level (342 regions) GDP in mainland China, based on eigenvector spatial filtering (ESF) regression (mean R2 = 0.98). The ordinary least squares (OLS) (mean R2 = 0.86) and spatial error model (SEM) (mean pseudo R2 = 0.89) were chosen as reference models. The ESF regression exhibits better performance for root-mean-square error (RMSE), mean absolute relative error (MARE), and Akaike information criterion (AIC) than the reference models and effectively eliminated the spatial autocorrelation in the residuals in all 3 years. The results indicate that the spatial economic disparity, as well as population distribution across China’s prefectures, is decreasing. The ESF regression also demonstrates that the population is crucial to the local economy and that the contribution of urbanization is growing.

GDP spatialization in Ningbo City based on NPP/VIIRS night-time light and auxiliary data using random forest regression

Accurate spatial distribution information on gross domestic product (GDP) is of great importance for the analysis of economic development, industrial distribution and urbanization processes. Traditional administrative unit-based GDP statistics cannot depict the detailed spatial differences in GDP within each administrative unit. This paper presents a study of GDP spatialization in Ningbo City, China based on National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL) data and town-level GDP statistical data. The Landsat image, land cover, road network and topographic data were also employed as auxiliary data to derive independent variables for GDP modelling. Multivariate linear regression (MLR) and random forest (RF) regression were used to estimate GDP at the town scale and were assessed by cross-validation. The results show that the RF model achieved significantly higher accuracy, with a mean absolute error (MAE) of 109.46 million China Yuan (CNY)·km−2 and a determinate coefficient (R2 = 0.77) than the MLR model (MAE = 161.8 million CNY·km−2, R2 = 0.59). Meanwhile, by comparing with the estimated GDP data at the county level, the town-level estimated data showed a better performance in mapping GDP distribution (MAE decreased from 115.1 million CNY·km−2 to 74.8 million CNY·km−2). Among all of the independent variables, NTL, land surface temperature (Ts) and plot ratio (PR) showed higher impacts on the GDP estimation accuracy than the other variables. The GDP density map generated by the RF model depicted the detailed spatial distribution of the economy in Ningbo City. By interpreting the spatial distribution of the GDP, we found that the GDP of Ningbo was high in the northeast and low in the southwest and formed continuous clusters in the north. In addition, the GDP of Ningbo also gradually decreased from the urban centre to its surrounding areas. The produced GDP map provides a good reference for the future urban planning and socio-economic development strategies.

Global warming has increased global economic inequality

Understanding the causes of economic inequality is critical for achieving equitable economic development. To investigate whether global warming has affected the recent evolution of inequality, we combine counterfactual historical temperature trajectories from a suite of global climate models with extensively replicated empirical evidence of the relationship between historical temperature fluctuations and economic growth. Together, these allow us to generate probabilistic country-level estimates of the influence of anthropogenic climate forcing on historical economic output. We find very high likelihood that anthropogenic climate forcing has increased economic inequality between countries. For example, per capita gross domestic product (GDP) has been reduced 17-31% at the poorest four deciles of the population-weighted country-level per capita GDP distribution, yielding a ratio between the top and bottom deciles that is 25% larger than in a world without global warming. As a result, although between-country inequality has decreased over the past half century, there is ∼90% likelihood that global warming has slowed that decrease. The primary driver is the parabolic relationship between temperature and economic growth, with warming increasing growth in cool countries and decreasing growth in warm countries. Although there is uncertainty in whether historical warming has benefited some temperate, rich countries, for most poor countries there is >90% likelihood that per capita GDP is lower today than if global warming had not occurred. Thus, our results show that, in addition to not sharing equally in the direct benefits of fossil fuel use, many poor countries have been significantly harmed by the warming arising from wealthy countries' energy consumption.

Monitoring spatio-temporal dynamics of habitat quality in Nansihu Lake basin, eastern China, from 1980 to 2015

Rapid industrial development, local urban expansion and intensive agricultural activities directly impact habitat quality and biodiversity. Effective monitoring tools for biodiversity could help assess trade-offs between biodiversity conservation and economic development. Habitat quality is the ability of the ecosystem to support conditions that are suitable for species and is regarded as proxy for biodiversity. This research used the InVEST-Habitat Quality model to monitor the spatio-temporal dynamics of habitat quality in the Nansihu Lake basin from 1980 to 2015; additionally, this research analyzed the potential factors that impacted habitat quality. The results indicated serious habitat degradation in the basin, especially in lake areas, from 1980 to 2015, and the spatial variation in habitat quality decreased extensively, especially from 2005 to 2015. Decreased values of habitat quality in lake areas during past decades suggested that the conservation strategies to natural reserve of lake is not effective. Overall, the habitat quality of most parts of the basin was very low, ranging from 0.10 to 0.20. In addition, only small regions possessed relatively higher habitat quality values, such as hilly areas and lake areas. The spatial patterns of habitat quality were influenced by the physical geographical factors and socio-economic activities. The patterns of habitat quality were significantly related to the physical geographical factors, including DEM (r = 0.50, p < 0.01), slope (r = 0.42, p < 0.01) and NDVI (r = 0.42, p < 0.01), as well as the socio-economic factors, such as land-use intensity (r = −0.88, p < 0.01) and population density (r = −0.32, p < 0.01). The distribution of gross domestic product (GDP) was not correlated with habitat quality. As a surrogate for biodiversity, this approach could be applied for the continuous monitoring of biodiversity in other areas, especially areas that lack biodiversity distribution data; thus, this method can serve as a useful tool for decision makers.

Spatial disparity dynamics of ecosystem service values and GDP in Shaanxi Province, China in the last 30 years

The regional policy in China is shifting from solely gross domestic product (GDP) orientation to development that is more balanced between economic growth and ecological protection, as well as achieving equality among regions. Using land use maps and the adjusted value coefficients to assess ecosystem service values (ESV) for the 1980s, 1995, 2000, and 2010, we estimated the ESV in Shaanxi Province for different years, and characterized the spatial and temporal distribution of ESV and GDP. The results demonstrated that the total value of ecosystem services in Shaanxi Province increased from 208.95 billion Yuan in the 1980s to 309.76 billion Yuan in 2010. Variation Coefficient (Cv) and Theil index (T) were used to reflect the disparities of GDP or ESV within the study area. The values of Cv in descending order are GDP, ESV per capita, ESV, and GDP per capita. The Theil indexes of GDP were much greater than the ones of ESV. Variations of Cv and T showed that disparity in GDP kept increasing from the 1980s to 2000, then decreased; while no significant change in regional disparity of ESV were detected in parallel. The cities with higher GDP usually contributed little to ESV, and vice versa. The variation in GDP and ESV, in terms of the prefectural totals and per capita values, increased from the 1980s to 2010. This study provides an accessible way for local decision makers to evaluate the regional balance between economic growth and ecosystem services.

The Suitability of Different Nighttime Light Data for GDP Estimation at Different Spatial Scales and Regional Levels

Nighttime light data offer a unique view of the Earth’s surface and can be used to estimate the spatial distribution of gross domestic product (GDP). Historically, using a simple regression function, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) has been used to correlate regional and global GDP values. In early 2013, the first global Suomi National Polar-orbiting Partnership (NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light data were released. Compared with DMSP/OLS, they have a higher spatial resolution and a wider radiometric detection range. This paper aims to study the suitability of the two nighttime light data sources for estimating the GDP relationship between the provincial and city levels in Mainland China, as well as of different regression functions. First, NPP/VIIRS nighttime light data for 2014 are corrected with DMSP/OLS data for 2013 to reduce the background noise in the original data. Subsequently, three regression functions are used to estimate the relationship between nighttime light data and GDP statistical data at the provincial and city levels in Mainland China. Then, through the comparison of the relative residual error (RE) and the relative root mean square error (RRMSE) parameters, a systematical assessment of the suitability of the GDP estimation is provided. The results show that the NPP/VIIRS nighttime light data are better than the DMSP/OLS data for GDP estimation, whether at the provincial or city level, and that the power function and polynomial models are better for GDP estimation than the linear regression model. This study reveals that the accuracy of GDP estimation based on nighttime light data is affected by the resolution of the data and the spatial scale of the study area, as well as by the land cover types and industrial structures of the study area.

Euromind‐: A Density Estimate of Monthly Gross Domestic Product for the Euro Area

SummaryEuroMInd‐ is a density estimate of monthly gross domestic product (GDP) constructed according to a bottom‐up approach, pooling the density estimates of 11 GDP components, by output and expenditure type. The components' density estimates are obtained from a medium‐size dynamic factor model handling mixed frequencies of observation and ragged‐edged data structures. They reflect both parameter and filtering uncertainty and are obtained by implementing a bootstrap algorithm for simulating from the distribution of the maximum likelihood estimators of the model parameters, and conditional simulation filters for simulating from the predictive distribution of GDP. Both algorithms process the data sequentially as they become available in real time. The GDP density estimates for the output and expenditure approach are combined using alternative weighting schemes and evaluated with different tests based on the probability integral transform and by applying scoring rules. Copyright © 2016 John Wiley &amp; Sons, Ltd.