Fig 4 - uploaded by Li Zhang
Content may be subject to copyright.
Source publication
All countries around the world and many international bodies, including the United Nations Development Program (UNDP), United Nations Food and Agricultural Organization (FAO), the International Fund for Agricultural Development (IFAD) and the International Labor Organization (ILO), have to eliminate rural poverty. Estimation of regional poverty lev...
Context in source publication
Context 1
... Gansu, Guizhou and Xizang, are all located in western China, where regional economy is mainly based on agriculture with less industry and poor transportation and other public utilities. A large population in these provinces is suffering from poverty. The above IPIs have been grouped into the following five classes by cluster analysis, as shown in Fig. 4: very high IPIs (>=1.7), high IPIs (0-1.7), medium IPIs (À0.3-0), low IPIs (À0.6-À0.3), very low IPIs (<À0.6). Provinces with high IPIs are all located in eastern China. The two municipalities, Shanghai and Beijing have much higher values than any other regions. Shanghai is a global city, having influences over finance, commerce, ...
Citations
... Traditional poverty measurements rely primarily on survey data from the government and field surveys (Gouveia, Seixas, and Long 2018;Jean et al. 2016). Because most survey data are based on administrative divisions, the specific characteristics of poverty distribution within these units cannot be observed (Lin et al. 2022;Wang, Cheng, and Zhang 2012). Additionally, nationally representative household surveys are expensive and time-consuming, and developing and less-developed countries may not be able to afford up-to-date data (Han et al. 2021;Henderson, Storeygard, and Weil 2012;Sandefur and Glassman 2015;Shi et al. 2020). ...
Poverty continues to pose significant global challenges. Analyzing poverty distribution is pivotal for identifying spatial and demographic disparities, informing targeted policy interventions, and fostering inclusive and equitable development. The absence of a worldwide pixel-scale time-series poverty dataset has hampered effective policy formulation. To address this gap, we employ the international wealth index (IWI) derived from household survey data to represent poverty levels. Subsequently, a random forest regression model was constructed, with IWI serving as the dependent variable and representative features extracted from nighttime lights, land cover, digital elevation model, and World Bank statistical data serving as independent variables. This yielded a global map of the IWI for low- and middle-income nations at a 10-km resolution spanning 2005 to 2020. The model demonstrated robust performance with an R² value of 0.74. Over the studied period, areas and populations with IWI ≤ 50 decreased by 8.85% and 16.17%, indicating a steady decrease in global poverty regions. Changes in the IWI at the pixel scale indicate that areas closer to cities have faster growth rates. Furthermore, our poverty estimation models present a novel method for real-time pixel-scale poverty assessments. This study provides valuable insights into the dynamics of poverty, both globally and nationally.
... For instance, mapping long-term urbanization processes benefits from the unique advantage of the NTL observations spanning a relatively long period, including urban extent 4,5 , urban boundary 6,7 , impervious surface areas 8,9 , urban land use 8,10,11 , and built-up infrastructure [12][13][14] . Furthermore, long-term NTL datasets have proved to successfully estimate the population 15,16 , the gross domestic product (GDP) 17 and income [18][19][20] , but also the poverty [21][22][23] and freight traffic 24 . ...
Nighttime light remote sensing has been an increasingly important proxy for human activities. Despite an urgent need for long-term products and pilot explorations in synthesizing them, the publicly available long-term products are limited. A Night-Time Light convolutional LSTM network is proposed and applied the network to produce a 1-km annual Prolonged Artificial Nighttime-light DAtaset of China (PANDA-China) from 1984 to 2020. Assessments between modeled and original images show that on average the RMSE reaches 0.73, the coefficient of determination (R²) reaches 0.95, and the linear slope is 0.99 at the pixel level, indicating a high confidence in the quality of generated data products. Quantitative and visual comparisons witness PANDA-China’s superiority against other NTL datasets in its significantly longer NTL dynamics, higher temporal consistency, and better correlations with socioeconomics (built-up areas, gross domestic product, population) characterizing the most relevant indicator in different development phases. The PANDA-China product provides an unprecedented opportunity to trace nighttime light dynamics in the past four decades.
... This could provide an improvement to the economy studies that are explicitly focused on DMSP-OLS data for analysing socio-economic indicators, output, population and nighttime light relationship. Some examples of these studies are: Wang et al. (2012), Forbes (2013, Bagan and Yamagata (2015), Proville et al. (2017), Yu et al. (2018), Kumar et al. (2019), Xiao et al. (2020), Sangkasem and Puttanapong (2021). Second, rural areas were distinguished from urban areas using MODIS-Enhanced Vegetation Index and Corine Land Cover (CLC) which were used as ancillary data to identify the nighttime lights emitted from rural areas. ...
Poverty and inequality are the outstanding challenges in both developing and developed countries in the globe. Using Suomi National Polar-orbiting Partnership (NPP)-Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NTL) images and socio-economic data from administrative sources, this chapter focuses on the association between nighttime lights and economic activities with an aim of computing regional income inequality indices for the year 2015 in Turkey. Gini, the Atkinson and Theil statistics were used to establish regional inequality indices using both NTL and statistics data. The findings indicated that urban NTLs are strongly correlated with economic activity while the correla- tion is much weaker regarding rural nightlights and agricultural output. It can be noted that there was increasing regional inequality in north-west, south, and south-east regions whereas regional equality was more homogeneously distributed. The results indicated that NPP-VIIRS nightlight data can help to perform regional inequality assessments for the urban areas in Turkey.
... In this study, we selected a total of 17 independent variables from nighttime light and geospatial data to model the county-level MPI in Fujian province (Table 3). These 17 features, extensively cited in the literature [3,[17][18][19][20] as being closely related to poverty levels and demonstrating strong performance in poverty assessment, were selected as explanatory variables for model construction in this study. NL data effectively reflect human activities at night, demonstrating a robust relationship with economic development. ...
The accurate and timely acquisition of poverty information within a specific region is crucial for formulating effective development policies. Nighttime light (NL) remote sensing data and geospatial information provide the means for conducting precise and timely evaluations of poverty levels. However, current assessment methods predominantly rely on NL data, and the potential of combining multi-source geospatial data for poverty identification remains underexplored. Therefore, we propose an approach that assesses poverty based on both NL and geospatial data using machine learning models. This study uses the multidimensional poverty index (MPI), derived from county-level statistical data with social, economic, and environmental dimensions, as an indicator to assess poverty levels. We extracted a total of 17 independent variables from NL and geospatial data. Machine learning models (random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)) and traditional linear regression (LR) were used to model the relationship between the MPI and independent variables. The results indicate that the RF model achieved significantly higher accuracy, with a coefficient of determination (R2) of 0.928, a mean absolute error (MAE) of 0.030, and a root mean square error (RMSE) of 0.037. The top five most important variables comprise two (NL_MAX and NL_MIN) from the NL data and three (POI_Ed, POI_Me, and POI_Ca) from the geographical spatial data, highlighting the significant roles of NL data and geographical data in MPI modeling. The MPI map that was generated by the RF model depicted the detailed spatial distribution of poverty in Fujian province. This study presents an approach to county-level poverty evaluation that integrates NL and geospatial data using a machine learning model, which can contribute to a more reliable and efficient estimate of poverty.
... Pan and Hu (2018) identified China's multi-dimensional relative poverty spatially and then divided it into seven poverty levels to prinoritize poverty alleviation efforts. Wang et al. (2012) investigated the multi-dimensional relative poverty by comparing the NTL index and found the NTL is also highly plausible for identifying poverty areas. Investigating the Poverty-Returning Risk (PRR) via nighttime lighting is considered an efficient and convenient tool. ...
... The results of the linear regression analysis indicate that RSDI only captures about 60 % of the information content of ANLI, however, the correlation between these two indicators increases over time. ANLI measures the nighttime light intensity, also reflects population concentration, nighttime activity intensity, urban morphological changes, and industrial transformation (Wang et al., 2012). It accurately evaluates most statistical indicators related to the unit area and is suitable for the precise evaluation of a single development indicator, thereby corroborating the credibility of official statistics (Andries et al., 2023). ...
Principal component analysis (PCA) Spatio-temporal relationship A B S T R A C T Quantifying progress towards the United Nations (UN) Sustainable Development Goals (SDGs) requires countries to make profound changes and major efforts in monitoring and measurement. However, there is still a need for a simple and easy-to-use means to quantify subnational SDGs performance and to determine the applicability of remote sensing big data tools, such as night-time lighting, for tracking the sustainable development process. This study used hierarchical clustering and principal component analysis (PCA), to construct a regional sustainable development index (RSDI), which aims to quantify China's progress towards the SDGs at the subnational level in 2013-2020. The average R 2 of the linear regression between China's subnational RSDIs from 2013 to 2020 is 0.9. The average R 2 of the RSDI and the Average Night-light Index (ANLI) is 0.57, and the average R 2 of the RSDI and Gross Domestic Product per capita (CGDP) is 0.85. The RSDI has remained stable over time as an observation framework and is suitable for tracking China's sustainable development in the long term, with a strong link to the CGDP and ANLI, which reflect economic development and big data method. The RSDI in China's eastern region reduces by 0.32 % annually, while the RSDI in central and western regions grows by 1.26 % annually. The RSDI in China's northern region reduces by 0.14 % annually, while the RSDI in China's southern regions grows by 2.62 % annually. We also attempted to incorporate ANLI and CGDP into the RSDI framework to capture potential application scenarios for tracking sustainability by using nighttime lighting. The RSDI can track how the SDGs are implemented and can be used in other countries subnational areas to analyze the spatial-temporal dynamics of SDGs achievement.
... Nighttime satellite imagery was obtained from the NOAA-Defense Meteorological Satellite Program (http://ngdc.noaa.gov/eog/dmsp/ downloadV4composites.html) and was used as a proxy for poverty estimates [46,47]. Accessibility was summarized in terms of travel time by land or sea [48], as connectivity between population sites is an important variable in estimating the potential distributions of disease vectors and emerging diseases [29,49]. ...
Crimean-Congo haemorrhagic fever (CCHF) is the most widely distributed tick-borne viral disease in humans and is caused by the Crimean-Congo haemorrhagic fever virus (CCHFV). The virus has a broader distribution, expanding from western China and South Asia to the Middle East, southeast Europe, and Africa. The historical known distribution of the CCHFV vector Hyalomma marginatum in Europe includes most of the Mediterranean and the Balkan countries, Ukraine, and southern Russia. Further expansion of its potential distribution may have occurred in and out of the Mediterranean region. This study updated the distributional map of the principal vector of CCHFV, H . marginatum , in the Old World using an ecological niche modeling approach based on occurrence records from the Global Biodiversity Information Facility (GBIF) and a set of covariates. The model predicted higher suitability of H . marginatum occurrences in diverse regions of Africa and Asia. Furthermore, the model estimated the environmental suitability of H . marginatum across Europe. On a continental scale, the model anticipated a widespread potential distribution encompassing the southern, western, central, and eastern parts of Europe, reaching as far north as the southern regions of Scandinavian countries. The distribution of H . marginatum also covered countries across Central Europe where the species is not autochthonous. All models were statistically robust and performed better than random expectations (p < 0.001). Based on the model results, climatic conditions could hamper the successful overwintering of H . marginatum and their survival as adults in many regions of the Old World. Regular updates of the models are still required to continually assess the areas at risk using up-to-date occurrence and climatic data in present-day and future conditions.
... Nighttime lights provide a rapid, accurate, and objective approach for obtaining information on surface characteristics and human activities (Hillger et al., 2013). The nighttime light index (NLI) has been widely used in various fields, including regional economic modeling , urban expansion (Zheng et al., 2022a,b), and public services (Lan et al., 2020), especially in analyses aimed at measuring regional poverty (Wang et al., 2012). The remote sensing ecological index (RSEI) is a convenient ecological index that integrates remote sensing data on the normalized difference vegetation index, normalized difference built-up and soil index, wetness component of the tasseled cap transformation, and land surface temperature (Xu, 2013). ...
... Nighttime light data provide a valuable indicator for assessing the socio-economic conditions in different regions, particularly in areas where data are limited (Wang et al., 2012;Wu et al., 2013). The intensity of nighttime light is positively correlated with economic development and negatively correlated with poverty levels. ...
The development of the Yellow River Basin (YRB) has been constrained by resource utilization, ecological conservation, and economic growth. The aim of this study was to establish a theoretical framework for characterizing the relationships among resources, economy, and ecology in the YRB using panel data and remote sensing data from nine provinces in the basin from 2002 to 2022. Furthermore, a coupling coordination degree model and geographical detector model were used to evaluate the spatiotemporal coupling and relevant factors. Our findings were manifold. (1) The coupling coordination degree in the YRB and its provinces has improved to varying degrees, with upstream provinces showing stronger coordination than midstream and downstream provinces. (2) The most significant growth rates were observed during 2002-2006 and 2014-2018. However, a noticeable decline in the coupling coordination degree occurred during 2006-2010, indicating that ecological constraints and negative effects were pronounced when resource constraints were not apparent. (3) The average coupling coordination degree of resources, economy, and ecology in the YRB in 2022 was 0.684. Excellent coordination was observed in Shandong Province and Henan Province, and good coordination was observed in Shaanxi Province and Shanxi Province. (4) In the initial part of the study period, the water resource supply, demand, and allocation had strong effects on the coordination degree. Over time, the focus shifted from a single system to multiple systems, which increased the similarity in the explanatory power of different factors. Additionally , an in-depth analysis using the coupling coordination degree and relevant factors was conducted to characterize the policy orientations of different periods in the YRB. Policy recommendations were provided on the basis of the natural conditions and socioeconomic conditions of the YRB. The findings of this study have implications for coupling coordination relationships and high-quality development in the YRB. Generally, our results provide key insights that will aid coordinated and sustainable regional development.
... The progress of human activity reflected by the change of nighttime lighting in a long time series relies on the Defense Meteorological Satellite Program's Operational Line Scan (DMSP-OLS) data available since 1992 and the Suomi National Polar-orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data available since 2012 [24]. The research hotspots mainly focus on the application of nighttime lighting in socioeconomic studies, which has expanded rapidly during the last two decades with focus on a range of topics, including mapping urban expansion and population dynamics [26][27][28][29][30], tracking electricity consumption [31,32], estimating GDP [23,33,34], poverty [35][36][37], and the environmental impacts of light emissions (light pollution) [38], including the impact on human health [39]. The research has the advantages of low acquisition cost, guaranteed update cycle, long time series, effective avoidance of administrative boundary change, and human interference [40]. ...
China’s poverty alleviation projects have made significant contributions to global poverty eradication. This study investigates the impact of China’s poverty alleviation projects on nighttime lighting in 831 state-level impoverished counties using the “NPP-VIIRS-like” dataset and discusses the difference of land use change under different nighttime light clusters in order to provide reference for future policy formulation and implementation. Our results show that the growth of total intensity of nighttime lighting (GRTNL) and the year-on-year growth rate of total intensity of nighttime lighting (YGRTNL) in China’s impoverished counties are 103.74% and 9.69% from 2013 to 2021, respectively, which are both higher than the average levels of all counties (67.16%, 6.77%) and non-poor counties (64.68%, 6.56%) in China during the same period. Additionally, we discovered that impoverished counties that lifted out of poverty earlier had significantly higher nighttime lighting intensity than those later. Regional analysis reveals that the growth of nighttime lighting intensity shows a trend of decreasing from the central (1550.89 nW·cm−2·sr−1) to the eastern (924.57), western (762.57), and northeastern regions (588.07), while the growth rate decreases from western regions (282.46%) to the eastern (189.13%), central (178.56%), and northeastern (108.07%). We also identified that Gini coefficient of nighttime lighting has a trend of “slow and short-term rise-rapid and continuous decline”. Moreover, nighttime lighting growth had similar trends with land use change, especially construction land. Overall, our study provides novel insights into the relationship between poverty alleviation effects and nighttime lighting in China’s impoverished counties, which could inform future policy-making and research in this area.
... Previous studies have shown variations in NTL intensity well mimic the spatiotemporal changes of human activities (Levin & Duke, 2012;Ma et al., 2015) and exhibit a strong correlation with numerous key socioeconomic variables, such as GDP (Doll et al., 2006;G. Li & Fang, 2014;Wu et al., 2013), poverty (Jean et al., 2016;Pokhriyal & Jacques, 2017;Wang et al., 2012), disaster impacts (Brock, 2019;Fusilli et al., 2014;Khan et al., 2020), population density (Amaral et al., 2005;X. Li & Zhou, 2018), and international economic and trading activities (Henderson et al., 2016). ...
China shares its board with one developed and thirteen developing countries. A timely, precise, and efficient socioeconomic study of border regions is vital for evaluating political problems and identifying potential economic prospects. Usually, conventional socioeconomic statistical data suffer from significant time lags and unequal statistical scales. This study utilized the random forest model to establish a connection between satellite-derived nighttime light data and the improved human development index (IHDI). The relationship was then applied to predict the IHDI, and differences in its strength, trend, and change pattern by bordering statistical units from 2000 to 2020 were evaluated. Our findings indicate that China's administrative units (AUCs) are more developed and have a greater development trend than their neighbors (AUNs). Except for the Tibet Autonomous Region, all AUCs are spatially more developed than AUNs, with the discrepancy widening between 2000 and 2020. Socioeconomic changes in AUCs predominantly exhibit a forward-leaping development pattern, which may be represented by a logarithmic (53%) or sigmoid (22.6%) function, whereas AUNs' socioeconomic changes exhibit either a late-leaping exponential (34.2%) or static development (18.6%) trend. The IHDI values in AUCs exhibit greater disparity as measured by the Theil index, than the AUNs, primarily due to the natural environment, resource availability, and development policies. In less developed regions, harsh natural surroundings, temperatures, and scarce natural resources hinder socioeconomic growth.
