Assessing regional economic development is key for advancing towards the Sustainable Development Goals and ensuring sustainable societal progress.Traditional evaluation methods focus on basic economic metrics like pop...Assessing regional economic development is key for advancing towards the Sustainable Development Goals and ensuring sustainable societal progress.Traditional evaluation methods focus on basic economic metrics like population and capital,which may not fully reflect the complexities of economic activities.Nighttime light(NTL)has been validated as an alternative indicator for regional economic development,yet limitations persist in its evaluation.This study integrates OpenStreetMap(OSM)data and NTL data,providing a novel data integration approach for evaluating economic development.The study uses mainland of China as a case,applying ordinary least squares(OLS)and geographically weighted regression(GWR)to evaluate OSM and NTL data across provincial,municipal,and county levels.It compares OSM,NTL,and their combined use,providing key empirical insights for enhancing data fusion models.The study results reveal:(1)NTL data is more accurate for provincial-level economic activity,while OSM data excels at the county level.(2)GWR demonstrates superior capability over OLS in revealing the spatial dynamics of economic development across scales.(3)Through the integration of both datasets,it is observed that,compared to single-data modeling,the performance is enhanced at the city scale and county scale.The study demonstrates that combining OSM and NTL data effectively assesses economic development in both developed and underdeveloped areas at provincial,municipal,and county levels.The study offers a straightforward and efficient approach to data integration.The findings offer new research perspectives and scientific support for sustainable regional economic growth,particularly valuable in data-scarce,underdeveloped areas.展开更多
China’s economy has developed rapidly since its reform and opening-up.However,the different rates of development in various places due to location and policies have led to significant economic differences.Based on th...China’s economy has developed rapidly since its reform and opening-up.However,the different rates of development in various places due to location and policies have led to significant economic differences.Based on the nighttime lighting data of 281 municipal spatial units in China from 2013 to 2021,this study uses spatial autocorrelation,center of gravity shift,and standard deviation ellipse(SDE)analysis to examine the evolution of the spatial pattern of China’s municipal economy.Based on these,it uses a geographically weighted regression(GWR)model to explore the factors influencing the differences in China’s municipal economy and its spatial heterogeneity.The paper reveals the following results.First,China’s municipal economy as a whole shows a growing trend.Second,the SDE shows a“north-south”distribution pattern,and the concentration of China’s economic development has slightly increased,with a significant centripetal distribution.Third,spatial correlation shows spatial positive correlation,the degree of which is increasing,with strong spatial heterogeneity and regional agglomeration.Finally,measuring the influencing factors according to GWR,the industrial structure and education expenditure coefficients generally show a decreasing trend from the southeast coast to the northwest and inland due to the degree of transformation of industrial structure and the lagging effect of education expenditure on economic growth.Conversely,the innovation driver and urban area coefficients show a decreasing trend from the northwest inland to the southeast coast due to the law of diminishing marginal utility of innovation drivers and differences in urbanization development.Government expenditure coefficients show a higher trend in the East and a lower trend in the West due to policy favoritism and market development level.This research can serve as a theoretical reference for China to achieve high-quality development and move toward common prosperity.展开更多
Balanced development and the reduction of inequality are central objectives of the United Nations Sustainable Development Goals(SDGs).This study explores the use of Nighttime Light(NTL)brightness and the Nighttime Lig...Balanced development and the reduction of inequality are central objectives of the United Nations Sustainable Development Goals(SDGs).This study explores the use of Nighttime Light(NTL)brightness and the Nighttime Light Development Index(NLDI)as indicators of socioeconomic development in urban centers,focusing on six Indian cities.It examines the correlation between these indices and socioeconomic inequality across affluent neighborhoods,urban slums,downtown areas,and general urban areas in 2015,2018,and 2021.The results reveal that lighting brightness in affluent areas can be lower than that in bustling downtowns,due to factors such as lower residential density.This challenges the conventional assumption that higher NTL necessarily indicates greater prosperity.This study further confirmed significant developmental disparities between well-lit downtowns and poorly illuminated peripheral slum areas,as reflected by lower NLDI scores.Notably,the results uncover a phenomenon termed“same value but different spectrum”based on a careful examination of NLDI values of urban centers and their corresponding curves.This suggests that NLDI alone may not fully capture the complexity of urban development,and that underlying development trajectories,along with on-the-ground realities,must be further examined.The findings emphasize the importance of applying NLDI for urban internal analyses.In addition,the study highlights the necessity for nuanced urban planning and targeted policy interventions specifically tailored to the unique conditions of different urban areas.展开更多
In this study,we proposed a multi-source approach for mapping local-scale population density of England.Specifically,we mapped both the working and daytime population densities by integrating the multi-source data suc...In this study,we proposed a multi-source approach for mapping local-scale population density of England.Specifically,we mapped both the working and daytime population densities by integrating the multi-source data such as residential population density,point-of-interest density,point-of-interest category mix,and nighttime light intensity.It is demonstrated that combining remote sensing and social sensing data provides a plausible way to map annual working or daytime population densities.In this paper,we trained models with England-wide data and subsequently tested these models with Wales-wide data.In addition,we further tested the models with England-wide data at a higher level of spatial granularity.Particularly,the random forest and convolutional neural network models were adopted to map population density.The estimated results and validation suggest that the three built models have high prediction accuracies at the local authority district level.It is shown that the convolutional neural network models have the greatest prediction accuracies at the local authority district level though they are most time-consuming.The models trained with the data at the local authority district level are less appropriately applicable to test data at a higher level of spatial granularity.The proposed multi-source approach performs well in mapping local-scale population density.It indicates that combining remote sensing and social sensing data is advantageous to mapping socioeconomic variables.展开更多
City lights,fishing boats,and oil fields are the major sources of nighttime lights,therefore the nighttime light images provide a unique source to map human beings and their activities from outer space.While most of t...City lights,fishing boats,and oil fields are the major sources of nighttime lights,therefore the nighttime light images provide a unique source to map human beings and their activities from outer space.While most of the scholars focused on application of nighttime light remote sensing in urbanization and regional development,the actual fields are much wider.This paper summarized the applications of nighttime light remote sensing into fields such as the estimation of socioeconomic parameters,monitoring urbanization,evaluation of important events,analyzing light pollution,fishery,etc.For estimation of socioeconomic parameters,the most promising progress is that Gross Domestic Product and its growth rate have been estimated with statistical data and nighttime light data using econometric models.For monitoring urbanization,urban area and its dynamics can be extracted using different classification methods,and spatial analysis has been employed to map urban agglomeration.As sharp changes of nighttime light are associated with important socioeconomic events,the images have been used to evaluate humanitarian disasters,especially in the current Syrian and Iraqi wars.Light pollution is another hotspot of nighttime light application,as the night light is related to some diseases and abnormal behavior of animals,and the nighttime light images can provide light pollution information on large scales so that it is much easier to analyze the effects of light pollutions.In each field,we listed typical cases of the applications.At last,future studies of nighttime light remote sensing have been predicted.展开更多
This essay combines the Defense Meteorological Satellite Program Operational Linescan System(DMSP-OLS)nighttime light data and the Visible Infrared Imaging Radiometer Suite(VIIRS)nighttime light data into a“synthetic...This essay combines the Defense Meteorological Satellite Program Operational Linescan System(DMSP-OLS)nighttime light data and the Visible Infrared Imaging Radiometer Suite(VIIRS)nighttime light data into a“synthetic DMSP”dataset,from 1992 to 2020,to retrieve the spatio-temporal variations in energy-related carbon emissions in Xinjiang,China.Then,this paper analyzes several influencing factors for spatial differentiation of carbon emissions in Xinjiang with the application of geographical detector technique.Results reveal that(1)total carbon emissions continued to grow,while the growth rate slowed down in the past five years.(2)Large regional differences exist in total carbon emissions across various regions.Total carbon emissions of these regions in descending order are the northern slope of the Tianshan(Mountains)>the southern slope of the Tianshan>the three prefectures in southern Xinjiang>the northern part of Xinjiang.(3)Economic growth,population size,and energy consumption intensity are the most important factors of spatial differentiation of carbon emissions.The interaction between economic growth and population size as well as between economic growth and energy consumption intensity also enhances the explanatory power of carbon emissions’spatial differentiation.This paper aims to help formulate differentiated carbon reduction targets and strategies for cities in different economic development stages and those with different carbon intensities so as to achieve the carbon peak goals in different steps.展开更多
Since the reform and opening-up program started in 1978,the level of urbanization has increased rapidly in China.Rapid urban expansion and restructuring have had significant impacts on the ecological environment espec...Since the reform and opening-up program started in 1978,the level of urbanization has increased rapidly in China.Rapid urban expansion and restructuring have had significant impacts on the ecological environment especially within built-up areas.In this study,ArcGIS 10,ENVI 4.5,and Visual FoxPro 6.0 were used to analyze the human impacts on vegetation in the built-up areas of 656Chinese cities from 1992 to 2010.Firstly,an existing algorithm was refined to extract the boundaries of the built-up areas based on the Defense Meteorological Satellite Program Operational Linescan System(DMSP_OLS)nighttime light data.This improved algorithm has the advantages of high accuracy and speed.Secondly,a mathematical model(Human impacts(HI))was constructed to measure the impacts of human factors on vegetation during rapid urbanization based on Advanced Very High Resolution Radiometer(AVHRR)Normalized Difference Vegetation Index(NDVI)and Moderate Resolution Imaging Spectroradiometer(MODIS)NDVI.HI values greater than zero indicate relatively beneficial effects while values less than zero indicate proportionally adverse effects.The results were analyzed from four aspects:the size of cities(metropolises,large cities,medium-sized cities,and small cities),large regions(the eastern,central,western,and northeastern China),administrative divisions of China(provinces,autonomous regions,and municipalities)and vegetation zones(humid and semi-humid forest zone,semi-arid steppe zone,and arid desert zone).Finally,we discussed how human factors impacted on vegetation changes in the built-up areas.We found that urban planning policies and developmental stages impacted on vegetation changes in the built-up areas.The negative human impacts followed an inverted′U′shape,first rising and then falling with increase of urban scales.China′s national policies,social and economic development affected vegetation changes in the built-up areas.The findings can provide a scientific basis for municipal planning departments,a decision-making reference for government,and scientific guidance for sustainable development in China.展开更多
The Tibetan Plateau(TP)is undergoing rapid urbanization.To improve urban sustainability and construct eco-logical security barriers,it is essential to quantify the spatial patterns of urbanization level on the TP,but ...The Tibetan Plateau(TP)is undergoing rapid urbanization.To improve urban sustainability and construct eco-logical security barriers,it is essential to quantify the spatial patterns of urbanization level on the TP,but the existing studies on the topic have been limited by the lack of socioeconomic data.This study aims to quantify the urbanization level on the TP in 2018 with Luojia1-01(LJ1-01)high-resolution nighttime light(NTL)data.Specifically,the compounded night light index is used to quantify spatial patterns of urbanization level at mul-tiple scales.The results showed that the TP had a low overall urbanization level with a large internal difference.The urbanization level in the northeast,southeast and south of the TP was relatively high,forming three hotspots centered in Xining City,Lhasa City and Shangri-La City,while the urbanization level in the central and western regions was relatively low.The analysis of influencing factors,based on the random forest model,showed that transportation and topography were the main factors affecting the TP’s spatial patterns of urbanization level.The comparison analysis with socioeconomic statistics and traditional NTL data showed that LJ1-01 NTL data can be used to more effectively quantify the urbanization level since it is more advantageous for reflecting the spatial extent of urban land and describing the spatial structure of socioeconomic activities within urban areas.These advantages are attributed to the high spatial resolution of the data,appropriate imaging time and unaf-fected by saturation phenomena.Thus,the proposed LJ1-01 NTL-based urbanization level measurement method has the potential for wide applications around the world,especially in less-developed regions lacking statistical data.Using this method,we refined the measurement of the TP’s urbanization level in 2018 for multiple scales including the region,basin,prefecture and county levels,which provides basic information for the further urban sustainability research on the TP.展开更多
Comparing the city-size distribution at the urban agglomeration（UA） scale is important for understanding the processes of urban development. However, comparative studies of city-size distribution among China＇s thre...Comparing the city-size distribution at the urban agglomeration（UA） scale is important for understanding the processes of urban development. However, comparative studies of city-size distribution among China＇s three largest UAs, the Beijing-Tianjin-Hebei agglomeration（BTHA）, the Yangtze River Delta agglomeration（YRDA）, and the Pearl River Delta agglomeration（PRDA）, remain inadequate due to the limitation of data availability. Therefore, using urban data derived from time-series nighttime light data, the common characteristics and distinctive features of city-size distribution among the three UAs from 1992 to 2015 were compared by the Pareto regression and the rank clock method. We identified two common features. First, the city-size distribution became more even. The Pareto exponents increased by 0.17, 0.12, and 0.01 in the YRDA, BTHA, and PRDA, respectively. Second, the average ranks of small cities ascended, being 0.55, 0.08 and 0.04 in the three UAs, respectively. However, the average ranks of large and medium cities in the three UAs experienced different trajectories, which are closely related to the similarities and differences in the driving forces for the development of UAs. Place-based measures are encouraged to promote a coordinated development among cities of differing sizes in the three UAs.展开更多
Understanding the dynamics of urbanization is essential to the sustainable development of cities. Meanwhile the analysis of urban development can also provide scientifically and effective information for decision-maki...Understanding the dynamics of urbanization is essential to the sustainable development of cities. Meanwhile the analysis of urban development can also provide scientifically and effective information for decision-making. With the long-term Defense Meteorological Satellite Program’s Operational Linescan System(DMSP/OLS) nighttime light images, a pixel level assessment of urbanization of China from 1992 to 2013 was conducted in this study, and the spatio-temporal dynamics and future trends of urban development were fully detected. The results showed that the urbanization and urban dynamics of China experienced drastic fluctuations from 1992 to 2013, especially for those in the coastal and metropolitan areas. From a regional perspective, it was found that the urban dynamics and increasing trends in North Coast China, East Coast China and South Coast China were much more stable and significant than that in other regions. Moreover, with the sustainability estimating of nighttime light dynamics, the regional agglomeration trends of urban regions were also detected. The light intensity in nearly 50% of lighted pixels may continuously decrease in the future, indicating a severe situation of urbanization within these regions. In this study, The results revealed in this study can provided a new insight in long time urbanization detecting and is thus beneficial to the better understanding of trends and dynamics of urban development.展开更多
The research purpose is to accurately reveal the temporal and spatial law of the urban expansion of Changsha-Zhuzhou-Xiangtan, one of the seven major urban agglomeration areas in China, and provide decision-making bas...The research purpose is to accurately reveal the temporal and spatial law of the urban expansion of Changsha-Zhuzhou-Xiangtan, one of the seven major urban agglomeration areas in China, and provide decision-making basis for the future urban construction land layout and regional development policy-making. Based on the night lighting data (DMSP/OLS), this paper extracts the boundary of the urban construction land of Changsha-Zhuzhou-Xiangtan urban agglomeration from 1993 to 2017, and quantitatively studies the spatial and temporal characteristics of the expansion of the metropolitan area in the past 25 years according to the methods of spatial expansion analysis, center of gravity migration measurement, landscape pattern index, spatial autocorrelation, etc. The results show that: 1) it is scientific and feasible to extract urban agglomeration construction land by the method of auxiliary data comparison for the study of urban expansion;2) the expansion of regional space in Changsha-Zhuzhou-Xiangtan metropolitan area shows a trend of “weakening first and strengthening later”. The construction land keeps increasing, and the expansion form gradually changes from extensive type to intensive type;3) the center of gravity of the metropolitan area fluctuated and repeated in part during the past 25 years, but it was always located in the municipal district of Changsha city. The eastern region, mainly Changsha city, was still the core area of urban agglomeration expansion;4) strengthening the territorial space protection and control of ecological green core in the metropolitan area is a key measure for the high-quality development of urban agglomeration.展开更多
With the continuous development of urbanization in China,the country’s growing population brings great challenges to urban development.By mastering the refined population spatial distribution in administrative units,...With the continuous development of urbanization in China,the country’s growing population brings great challenges to urban development.By mastering the refined population spatial distribution in administrative units,the quantity and agglomeration of population distribution can be estimated and visualized.It will provide a basis for a more rational urban planning.This paper takes Beijing as the research area and uses a new Luojia1-01 nighttime light image with high resolution,land use type data,Points of Interest(POI)data,and other data to construct the population spatial index system,establishing the index weight based on the principal component analysis.The comprehensive weight value of population distribution in the study area was then used to calculate the street population distribution of Beijing in 2018.Then the population spatial distribution was visualize using GIS technology.After accuracy assessments by comparing the result with the WorldPop data,the accuracy has reached 0.74.The proposed method was validated as a qualified method to generate population spatial maps.By contrast of local areas,Luojia 1-01 data is more suitable for population distribution estimation than the NPP/VIIRS(Net Primary Productivity/Visible infrared Imaging Radiometer)nighttime light data.More geospatial big data and mathematical models can be combined to create more accurate population maps in the future.展开更多
Investigating urban expansion patterns aids in the management of urbanization and in ameliorating the socioeconomic and environmental issues associated with economic transformation and sustainable development.Applying...Investigating urban expansion patterns aids in the management of urbanization and in ameliorating the socioeconomic and environmental issues associated with economic transformation and sustainable development.Applying Harmonized Defense Meteorological Satellite Program-Operational Line-scan System(DMSP-OLS)and the Suomi National Polar-Orbiting Partnership-Visible Infrared Imagery Radiometer Suite(NPP-VIIRS)Nighttime Light(NTL)data,this paper investigated the characteristics of urban landscape in West Africa.Using the harmonized NTL data,spatial comparison and empirical threshold methods were employed to detect urban changes from 1993 to 2018.We examined the rate of urban change and calculated the direction of the urban expansion of West Africa using the center-of-gravity method for urban areas.In addition,we used the landscape expansion index method to assess the processes and stages of urban growth in West Africa.The accuracy of urban area extraction based on NTL data were R^(2)=0.8314 in 2000,R^(2)=0.8809 in 2006,R^(2)=0.9051 in 2012 for the DMSP-OLS and the simulated NPP-VIIRS was R^(2)=0.8426 in 2018,by using Google Earth images as validation.The results indicated that there was a high rate and acceleration of urban landscapes in West Africa,with rates of 0.0160,0.0173,0.0189,and 0.0686,and accelerations of 0.31,0.42,0.54,and 0.90 for the periods of 1998–2003,2003–2008,2008–2013,and 2013–2018,respectively.The expansion direction of urban agglomeration in West Africa during 1993–2018 was mainly from the coast to inland.However,cities located in the Sahel Region of Africa and in the middle zone expanded from north to south.Finally,the results showed that the urban landscape of West Africa was mainly in a scattered and disordered’diffusion’process,whereas only a few cities located in coastal areas experiencing the process of’coalescence’according to urban growth phase theory.This study provides urban planners with relevant insights for the urban expansion characteristics of West Africa.展开更多
Air pollution is a problem that directly affects human health,the global environment and the climate.The air quality index(AQI)indicates the degree of air pollution and effect on human health;however,when assessing ai...Air pollution is a problem that directly affects human health,the global environment and the climate.The air quality index(AQI)indicates the degree of air pollution and effect on human health;however,when assessing air pollution only based on AQI monitoring data the fact that the same degree of air pollution is more harmful in more densely populated areas is ignored.In the present study,multi-source data were combined to map the distribution of the AQI and population data,and the analyze their pollution population exposure of Beijing in 2018 was analyzed.Machine learning based on the random forest algorithm was adopted to calculate the monthly average AQI of Beijing in 2018.Using Luojia-1 nighttime light remote sensing data,population statistics data,the population of Beijing in 2018 and point of interest data,the distribution of the permanent population in Beijing was estimated with a high precision of 200 m×200 m.Based on the spatialization results of the AQI and population of Beijing,the air pollution exposure levels in various parts of Beijing were calculated using the population-weighted pollution exposure level(PWEL)formula.The results show that the southern region of Beijing had a more serious level of air pollution,while the northern region was less polluted.At the same time,the population was found to agglomerate mainly in the central city and the peripheric areas thereof.In the present study,the exposure of different districts and towns in Beijing to pollution was analyzed,based on high resolution population spatialization data,it could take the pollution exposure issue down to each individual town.And we found that towns with higher exposure such as Yongshun Town,Shahe Town and Liyuan Town were all found to have a population of over 200000 which was much higher than the median population of townships of51741 in Beijing.Additionally,the change trend of air pollution exposure levels in various regions of Beijing in 2018 was almost the same,with the peak value being in winter and the lowest value being in summer.The exposure intensity in population clusters was relatively high.To reduce the level and intensity of pollution exposure,relevant departments should strengthen the governance of areas with high AQI,and pay particular attention to population clusters.展开更多
Understanding regional carbon emissions from human activities,particularly their spatio-temporal patterns,is essential for implementing decarbonization strategies and cultivating a low-carbon economy.This study develo...Understanding regional carbon emissions from human activities,particularly their spatio-temporal patterns,is essential for implementing decarbonization strategies and cultivating a low-carbon economy.This study develops a spatial visualization model to estimate carbon emissions in Southeast Asia using calibrated nighttime light data,with DMSP-OLS(Defense Meteorological Satellite Program Operational Linescan System)and NPP-VIIRS(National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite)standardized through polynomial regression and machine learning to ensure consistency.Emissions in Southeast Asia increased by 2.51 times from 1992 to 2022,shifting from gradual to rapid growth.Validation against Open-source Data Inventory for Anthropogenic CO2(ODIAC)and Emissions Database for Global Atmospheric Research(EDGAR)shows strong agreement in high-emission urban areas but discrepancies in low-emission rural regions due to data sparsity and satellite sensor limits.Spatial analysis reveals that major Southeast Asian cities and their peripheries exhibit robust,sustained growth,while rural,less-developed areas show slower trends,highlighting persistent urbanrural disparities.These urban regions demonstrate a“circular economy advantage”,where per-unit-area carbon emissions steadily rise in economically advantageous zones.Despite high model accuracy,uncertainties persist due to variations in regional economic activities and the limitations of satellite-based emission proxies.Forecasts suggest elevated emission levels in major cities will continue,while changes in other areas remain relatively minimal.Consequently,achieving a low-carbon economy in Southeast Asia requires a top-down approach,emphasizing infrastructure enhancement,resource and energy optimization,and fostering a sustainable,circular socio-economic system.展开更多
Electricity constitutes a fundamental pillar ofboth the national economy and contemporary lifestyles.Monitoring electric power consumption(EPC)hasimportant implications for energy planning,energyconservation and emiss...Electricity constitutes a fundamental pillar ofboth the national economy and contemporary lifestyles.Monitoring electric power consumption(EPC)hasimportant implications for energy planning,energyconservation and emission reduction,energy security,andsmart city development.However,the current monitoringand evaluation of EPC is less accurate and does not allowfor real-time monitoring and evaluation of EPC.This studyestablished an EPC assessment model based on EPC data,nighttime light remote sensing technology,and GISciencemethodology,aiming to analyze the spatiotemporalvariation of EPC in three major urban agglomerations ofChina from 2012 to 2020 and estimate EPC in 2025.Furthermore,the spatial correlation of EPC was exploredusing Moran’s I spatial analysis method.The resultsindicate that the established model has an average accuracyof 77.56%and can be used for accurate and real-timeestimation of EPC.The EPC showed an increasing trendfrom 2012 to 2020,with the Yangtze River Delta urbanagglomeration(YRD)exhibiting the highest growth rate,as high as 49.60%.The EPC in the Beijing-Tianjin-Hebeiurban agglomeration(BTH)showed a negative spatialcorrelation.However,the YRD and the Guangdong-HongKong-Macao Greater Bay Area urban agglomeration(GBA)exhibited significant positive spatial correlation inEPC.The findings of this study serve a scientific basis andreference data for the development of energy policies andstrategies.Furthermore,this study can help to achieve the“carbon peaking and carbon neutrality goals”proposed bythe Chinese government.展开更多
Accurately identifying the evolving trends of regional economic inequality is crucial for promoting coordinated regional development and achieving Chinese-style modernization.This paper,based on continuous nighttime l...Accurately identifying the evolving trends of regional economic inequality is crucial for promoting coordinated regional development and achieving Chinese-style modernization.This paper,based on continuous nighttime light data from1992 to 2019,uses the Dagum Gini coefficient,emerging hot spot analysis,and XGBoost-SHAP interpretable machine learning models to analyze the spatiotemporal evolution of multi-scale regional economic inequality in China,as well as the spatial cold spots and hot spots and inter-regional and intra-regional inequalities among different subregions.It also reveals the impact contributions of natural and economic factors on regional economic inequality.The results show that:(1) During the study period,provincial-level economic inequality in China gradually increased before 2003 and then decreased afterward.After 2012,the decline at the provincial level slowed down,while city-level and county-level economic inequality slightly increased.(2) The Beijing-Tianjin-Hebei,Yangtze River Delta,and Pearl River Delta city clusters remain persistent nighttime light hotspots.Multiple eastern and central city clusters are gradually becoming new hotspots,while cold spots in western regions are decreasing,and hotspots in northeastern regions are also decreasing.(3) Regional economic inequality in China exhibits significant heterogeneity both within and between regions.Differences between eastern,central,western,and northeastern regions contribute significantly to overall inequality.Differences between regions on either side of the Hu Huanyong line remain stable,but north-south differences have intensified,and differences within and outside the Yangtze Basin Economic Belt have expanded.Differences within the Yellow River Basin region remain stable.(4) Both the first and second nature factors exert complex nonlinear impacts on the formation of regional economic inequality.Factors such as industrial agglomeration,globalization,innovation,and distance from the coast show strong explanatory power.The influence of the demand of first nature and the endogenous capacity of second nature is gradually increasing.Based on the above research findings,this paper proposes policy recommendations to promote balanced regional economic development in China.展开更多
This paper examines the impact on stock return synchronicity of information provided by people who neighbor firms.Neighboring people have inherent advantages in acquiring and interpreting information.We employed novel...This paper examines the impact on stock return synchronicity of information provided by people who neighbor firms.Neighboring people have inherent advantages in acquiring and interpreting information.We employed novel satellite nighttime light data as a proxy for information held by neighboring people.Our analyses confirmed that brighter nighttime light was related to greater and better-quality information production.Using a sample of 18,963 firm-year observations over the 2000-2013 period,we found that information from nearby people facilitated the incorporation of firm-specific information into stock prices,resulting in lower stock return synchronicity.The results were robust when using the slope as an instrumental variable and were supported by various sensitivity checks.The effect of nighttime light intensity was more pronounced for firms operating across multiple geographic regions or diverse industries,those situated near more parks and shopping malls,and those with fewer institutional investors and less media coverage.展开更多
An improved methodology for the extraction and mapping of urban built-up areas at a global scale is presented in this study.The Moderate Resolution Imaging Spectroradiometer(MODIS)-based multispectral data were combin...An improved methodology for the extraction and mapping of urban built-up areas at a global scale is presented in this study.The Moderate Resolution Imaging Spectroradiometer(MODIS)-based multispectral data were combined with the Visible Infrared Imager Radiometer Suite(VIIRS)-based nighttime light(NTL)data for robust extraction and mapping of urban built-up areas.The MODIS-based newly proposed Urban Built-up Index(UBI)was combined with NTL data,and the resulting Enhanced UBI(EUBI)was used as a single master image for global extraction of urban built-up areas.Due to higher variation of the EUBI with respect to geographical regions,a region-specific threshold approach was used to extract urban built-up areas.This research provided 500-m-resolution global urban built-up map of year 2014.The resulted map was compared with three existing moderate-resolution global maps and one high-resolution map in the United States.The comparative analysis demonstrated finer details of the urban built-up cover estimated by the resultant map.展开更多
The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning,resource allocation,government decision-making,disaster assessment,ecological...The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning,resource allocation,government decision-making,disaster assessment,ecological protection,and other sustainability research.However,the existing population datasets such as LandScan and WorldPop all provide data from 2000 with limited time series,while GHS-POP only utilizes land use data with limited accuracy.In view of the limited remote sensing images of long time series,it is necessary to combine existing multi-source remote sensing data for population spatialization research.In this research,we developed a nighttime light desaturation index(NTLDI).Through the cross-sensor calibration model based on an autoencoder convolutional neural network,the NTLDl was calibrated with the same period Visible Infrared Imaging Radiometer Suite Day/Night Band(VIRS-DNB)data.Then,the geographically weighted regression method is used to determine the population density of China from 1990 to 2020 based on the long time series NTL.Furthermore,the change characteristics and the driving factors of China's population spatial distribution are analyzed.The large-scale,long-term population spatialization results obtained in this study are of great significance in government planning and decision-making,disaster assessment,resource allocation,and other aspects.展开更多
基金funded by The Third Comprehensive Scientific Investigation in Xinjiang(Grant No.2021xjkk1001)Program of National Social Science Foundation of China(Grant No.22BJL061)+1 种基金Major Project of Xinjiang Social Science Foundation(Grant No.21AZD008)The National Natural Science Foundation of China(Grant No.41461035).
文摘Assessing regional economic development is key for advancing towards the Sustainable Development Goals and ensuring sustainable societal progress.Traditional evaluation methods focus on basic economic metrics like population and capital,which may not fully reflect the complexities of economic activities.Nighttime light(NTL)has been validated as an alternative indicator for regional economic development,yet limitations persist in its evaluation.This study integrates OpenStreetMap(OSM)data and NTL data,providing a novel data integration approach for evaluating economic development.The study uses mainland of China as a case,applying ordinary least squares(OLS)and geographically weighted regression(GWR)to evaluate OSM and NTL data across provincial,municipal,and county levels.It compares OSM,NTL,and their combined use,providing key empirical insights for enhancing data fusion models.The study results reveal:(1)NTL data is more accurate for provincial-level economic activity,while OSM data excels at the county level.(2)GWR demonstrates superior capability over OLS in revealing the spatial dynamics of economic development across scales.(3)Through the integration of both datasets,it is observed that,compared to single-data modeling,the performance is enhanced at the city scale and county scale.The study demonstrates that combining OSM and NTL data effectively assesses economic development in both developed and underdeveloped areas at provincial,municipal,and county levels.The study offers a straightforward and efficient approach to data integration.The findings offer new research perspectives and scientific support for sustainable regional economic growth,particularly valuable in data-scarce,underdeveloped areas.
基金funded by the Project of Philosophy and Social Science Key Research Base−Industrial Transformation and Innovation Research Center of Zigong Municipal Federation of Social Sciences[Grant No.CZ23B02].
文摘China’s economy has developed rapidly since its reform and opening-up.However,the different rates of development in various places due to location and policies have led to significant economic differences.Based on the nighttime lighting data of 281 municipal spatial units in China from 2013 to 2021,this study uses spatial autocorrelation,center of gravity shift,and standard deviation ellipse(SDE)analysis to examine the evolution of the spatial pattern of China’s municipal economy.Based on these,it uses a geographically weighted regression(GWR)model to explore the factors influencing the differences in China’s municipal economy and its spatial heterogeneity.The paper reveals the following results.First,China’s municipal economy as a whole shows a growing trend.Second,the SDE shows a“north-south”distribution pattern,and the concentration of China’s economic development has slightly increased,with a significant centripetal distribution.Third,spatial correlation shows spatial positive correlation,the degree of which is increasing,with strong spatial heterogeneity and regional agglomeration.Finally,measuring the influencing factors according to GWR,the industrial structure and education expenditure coefficients generally show a decreasing trend from the southeast coast to the northwest and inland due to the degree of transformation of industrial structure and the lagging effect of education expenditure on economic growth.Conversely,the innovation driver and urban area coefficients show a decreasing trend from the northwest inland to the southeast coast due to the law of diminishing marginal utility of innovation drivers and differences in urbanization development.Government expenditure coefficients show a higher trend in the East and a lower trend in the West due to policy favoritism and market development level.This research can serve as a theoretical reference for China to achieve high-quality development and move toward common prosperity.
基金Strategic Priority Research Program of Chinese Academy of Sciences,No.XDA20010303。
文摘Balanced development and the reduction of inequality are central objectives of the United Nations Sustainable Development Goals(SDGs).This study explores the use of Nighttime Light(NTL)brightness and the Nighttime Light Development Index(NLDI)as indicators of socioeconomic development in urban centers,focusing on six Indian cities.It examines the correlation between these indices and socioeconomic inequality across affluent neighborhoods,urban slums,downtown areas,and general urban areas in 2015,2018,and 2021.The results reveal that lighting brightness in affluent areas can be lower than that in bustling downtowns,due to factors such as lower residential density.This challenges the conventional assumption that higher NTL necessarily indicates greater prosperity.This study further confirmed significant developmental disparities between well-lit downtowns and poorly illuminated peripheral slum areas,as reflected by lower NLDI scores.Notably,the results uncover a phenomenon termed“same value but different spectrum”based on a careful examination of NLDI values of urban centers and their corresponding curves.This suggests that NLDI alone may not fully capture the complexity of urban development,and that underlying development trajectories,along with on-the-ground realities,must be further examined.The findings emphasize the importance of applying NLDI for urban internal analyses.In addition,the study highlights the necessity for nuanced urban planning and targeted policy interventions specifically tailored to the unique conditions of different urban areas.
文摘In this study,we proposed a multi-source approach for mapping local-scale population density of England.Specifically,we mapped both the working and daytime population densities by integrating the multi-source data such as residential population density,point-of-interest density,point-of-interest category mix,and nighttime light intensity.It is demonstrated that combining remote sensing and social sensing data provides a plausible way to map annual working or daytime population densities.In this paper,we trained models with England-wide data and subsequently tested these models with Wales-wide data.In addition,we further tested the models with England-wide data at a higher level of spatial granularity.Particularly,the random forest and convolutional neural network models were adopted to map population density.The estimated results and validation suggest that the three built models have high prediction accuracies at the local authority district level.It is shown that the convolutional neural network models have the greatest prediction accuracies at the local authority district level though they are most time-consuming.The models trained with the data at the local authority district level are less appropriately applicable to test data at a higher level of spatial granularity.The proposed multi-source approach performs well in mapping local-scale population density.It indicates that combining remote sensing and social sensing data is advantageous to mapping socioeconomic variables.
基金This work was supported by the Natural Science Foundation of Hubei Province(China)[grant number 2014CFB726]a Special Fund by Surveying and Mapping and Geo-information Research in the Public Interest(China)[grant number 201512026].
文摘City lights,fishing boats,and oil fields are the major sources of nighttime lights,therefore the nighttime light images provide a unique source to map human beings and their activities from outer space.While most of the scholars focused on application of nighttime light remote sensing in urbanization and regional development,the actual fields are much wider.This paper summarized the applications of nighttime light remote sensing into fields such as the estimation of socioeconomic parameters,monitoring urbanization,evaluation of important events,analyzing light pollution,fishery,etc.For estimation of socioeconomic parameters,the most promising progress is that Gross Domestic Product and its growth rate have been estimated with statistical data and nighttime light data using econometric models.For monitoring urbanization,urban area and its dynamics can be extracted using different classification methods,and spatial analysis has been employed to map urban agglomeration.As sharp changes of nighttime light are associated with important socioeconomic events,the images have been used to evaluate humanitarian disasters,especially in the current Syrian and Iraqi wars.Light pollution is another hotspot of nighttime light application,as the night light is related to some diseases and abnormal behavior of animals,and the nighttime light images can provide light pollution information on large scales so that it is much easier to analyze the effects of light pollutions.In each field,we listed typical cases of the applications.At last,future studies of nighttime light remote sensing have been predicted.
基金The Third Xinjiang Scientific Expedition Program(2021xjkk0905)GDAS Special Project of Science and Technology Development(2020GDASYL-20200301003)+2 种基金GDAS Special Project of Science and Technology Development(2020GDASYL-20200102002)National Natural Science Foundation of China(41501144)Project of Department of Natural Resources of Guangdong Province(GDZRZYKJ2022005)。
文摘This essay combines the Defense Meteorological Satellite Program Operational Linescan System(DMSP-OLS)nighttime light data and the Visible Infrared Imaging Radiometer Suite(VIIRS)nighttime light data into a“synthetic DMSP”dataset,from 1992 to 2020,to retrieve the spatio-temporal variations in energy-related carbon emissions in Xinjiang,China.Then,this paper analyzes several influencing factors for spatial differentiation of carbon emissions in Xinjiang with the application of geographical detector technique.Results reveal that(1)total carbon emissions continued to grow,while the growth rate slowed down in the past five years.(2)Large regional differences exist in total carbon emissions across various regions.Total carbon emissions of these regions in descending order are the northern slope of the Tianshan(Mountains)>the southern slope of the Tianshan>the three prefectures in southern Xinjiang>the northern part of Xinjiang.(3)Economic growth,population size,and energy consumption intensity are the most important factors of spatial differentiation of carbon emissions.The interaction between economic growth and population size as well as between economic growth and energy consumption intensity also enhances the explanatory power of carbon emissions’spatial differentiation.This paper aims to help formulate differentiated carbon reduction targets and strategies for cities in different economic development stages and those with different carbon intensities so as to achieve the carbon peak goals in different steps.
基金Under the auspices of National Natural Science Foundation of China(No.41171143,40771064)Program for New Century Excellent Talents in University(No.NCET-07-0398)Fundamental Research Funds for the Central Universities(No.lzu-jbky-2012-k35)
文摘Since the reform and opening-up program started in 1978,the level of urbanization has increased rapidly in China.Rapid urban expansion and restructuring have had significant impacts on the ecological environment especially within built-up areas.In this study,ArcGIS 10,ENVI 4.5,and Visual FoxPro 6.0 were used to analyze the human impacts on vegetation in the built-up areas of 656Chinese cities from 1992 to 2010.Firstly,an existing algorithm was refined to extract the boundaries of the built-up areas based on the Defense Meteorological Satellite Program Operational Linescan System(DMSP_OLS)nighttime light data.This improved algorithm has the advantages of high accuracy and speed.Secondly,a mathematical model(Human impacts(HI))was constructed to measure the impacts of human factors on vegetation during rapid urbanization based on Advanced Very High Resolution Radiometer(AVHRR)Normalized Difference Vegetation Index(NDVI)and Moderate Resolution Imaging Spectroradiometer(MODIS)NDVI.HI values greater than zero indicate relatively beneficial effects while values less than zero indicate proportionally adverse effects.The results were analyzed from four aspects:the size of cities(metropolises,large cities,medium-sized cities,and small cities),large regions(the eastern,central,western,and northeastern China),administrative divisions of China(provinces,autonomous regions,and municipalities)and vegetation zones(humid and semi-humid forest zone,semi-arid steppe zone,and arid desert zone).Finally,we discussed how human factors impacted on vegetation changes in the built-up areas.We found that urban planning policies and developmental stages impacted on vegetation changes in the built-up areas.The negative human impacts followed an inverted′U′shape,first rising and then falling with increase of urban scales.China′s national policies,social and economic development affected vegetation changes in the built-up areas.The findings can provide a scientific basis for municipal planning departments,a decision-making reference for government,and scientific guidance for sustainable development in China.
基金the Second Tibetan Plateau Scientific Expedition and Research Program(Grant No.2019QZKK0405)the National Natural Science Foundation of China(Grant No.41871185&41971270)。
文摘The Tibetan Plateau(TP)is undergoing rapid urbanization.To improve urban sustainability and construct eco-logical security barriers,it is essential to quantify the spatial patterns of urbanization level on the TP,but the existing studies on the topic have been limited by the lack of socioeconomic data.This study aims to quantify the urbanization level on the TP in 2018 with Luojia1-01(LJ1-01)high-resolution nighttime light(NTL)data.Specifically,the compounded night light index is used to quantify spatial patterns of urbanization level at mul-tiple scales.The results showed that the TP had a low overall urbanization level with a large internal difference.The urbanization level in the northeast,southeast and south of the TP was relatively high,forming three hotspots centered in Xining City,Lhasa City and Shangri-La City,while the urbanization level in the central and western regions was relatively low.The analysis of influencing factors,based on the random forest model,showed that transportation and topography were the main factors affecting the TP’s spatial patterns of urbanization level.The comparison analysis with socioeconomic statistics and traditional NTL data showed that LJ1-01 NTL data can be used to more effectively quantify the urbanization level since it is more advantageous for reflecting the spatial extent of urban land and describing the spatial structure of socioeconomic activities within urban areas.These advantages are attributed to the high spatial resolution of the data,appropriate imaging time and unaf-fected by saturation phenomena.Thus,the proposed LJ1-01 NTL-based urbanization level measurement method has the potential for wide applications around the world,especially in less-developed regions lacking statistical data.Using this method,we refined the measurement of the TP’s urbanization level in 2018 for multiple scales including the region,basin,prefecture and county levels,which provides basic information for the further urban sustainability research on the TP.
基金National Natural Science Foundation of China,No.41621061,No.41501092 Talents Training Program from the Beijing Municipal Commission of Education No.201500002012G058
文摘Comparing the city-size distribution at the urban agglomeration（UA） scale is important for understanding the processes of urban development. However, comparative studies of city-size distribution among China＇s three largest UAs, the Beijing-Tianjin-Hebei agglomeration（BTHA）, the Yangtze River Delta agglomeration（YRDA）, and the Pearl River Delta agglomeration（PRDA）, remain inadequate due to the limitation of data availability. Therefore, using urban data derived from time-series nighttime light data, the common characteristics and distinctive features of city-size distribution among the three UAs from 1992 to 2015 were compared by the Pareto regression and the rank clock method. We identified two common features. First, the city-size distribution became more even. The Pareto exponents increased by 0.17, 0.12, and 0.01 in the YRDA, BTHA, and PRDA, respectively. Second, the average ranks of small cities ascended, being 0.55, 0.08 and 0.04 in the three UAs, respectively. However, the average ranks of large and medium cities in the three UAs experienced different trajectories, which are closely related to the similarities and differences in the driving forces for the development of UAs. Place-based measures are encouraged to promote a coordinated development among cities of differing sizes in the three UAs.
基金Under the auspices of State Scholarship Fund of China Scholarship Council(No.201706320300)。
文摘Understanding the dynamics of urbanization is essential to the sustainable development of cities. Meanwhile the analysis of urban development can also provide scientifically and effective information for decision-making. With the long-term Defense Meteorological Satellite Program’s Operational Linescan System(DMSP/OLS) nighttime light images, a pixel level assessment of urbanization of China from 1992 to 2013 was conducted in this study, and the spatio-temporal dynamics and future trends of urban development were fully detected. The results showed that the urbanization and urban dynamics of China experienced drastic fluctuations from 1992 to 2013, especially for those in the coastal and metropolitan areas. From a regional perspective, it was found that the urban dynamics and increasing trends in North Coast China, East Coast China and South Coast China were much more stable and significant than that in other regions. Moreover, with the sustainability estimating of nighttime light dynamics, the regional agglomeration trends of urban regions were also detected. The light intensity in nearly 50% of lighted pixels may continuously decrease in the future, indicating a severe situation of urbanization within these regions. In this study, The results revealed in this study can provided a new insight in long time urbanization detecting and is thus beneficial to the better understanding of trends and dynamics of urban development.
文摘The research purpose is to accurately reveal the temporal and spatial law of the urban expansion of Changsha-Zhuzhou-Xiangtan, one of the seven major urban agglomeration areas in China, and provide decision-making basis for the future urban construction land layout and regional development policy-making. Based on the night lighting data (DMSP/OLS), this paper extracts the boundary of the urban construction land of Changsha-Zhuzhou-Xiangtan urban agglomeration from 1993 to 2017, and quantitatively studies the spatial and temporal characteristics of the expansion of the metropolitan area in the past 25 years according to the methods of spatial expansion analysis, center of gravity migration measurement, landscape pattern index, spatial autocorrelation, etc. The results show that: 1) it is scientific and feasible to extract urban agglomeration construction land by the method of auxiliary data comparison for the study of urban expansion;2) the expansion of regional space in Changsha-Zhuzhou-Xiangtan metropolitan area shows a trend of “weakening first and strengthening later”. The construction land keeps increasing, and the expansion form gradually changes from extensive type to intensive type;3) the center of gravity of the metropolitan area fluctuated and repeated in part during the past 25 years, but it was always located in the municipal district of Changsha city. The eastern region, mainly Changsha city, was still the core area of urban agglomeration expansion;4) strengthening the territorial space protection and control of ecological green core in the metropolitan area is a key measure for the high-quality development of urban agglomeration.
基金Under the auspices of Natural Science Foundation of China(No.42071342,31870713)Beijing Natural Science Foundation Program(No.8182038)Fundamental Research Funds for the Central Universities(No.2015ZCQ-LX-01,2018ZY06)。
文摘With the continuous development of urbanization in China,the country’s growing population brings great challenges to urban development.By mastering the refined population spatial distribution in administrative units,the quantity and agglomeration of population distribution can be estimated and visualized.It will provide a basis for a more rational urban planning.This paper takes Beijing as the research area and uses a new Luojia1-01 nighttime light image with high resolution,land use type data,Points of Interest(POI)data,and other data to construct the population spatial index system,establishing the index weight based on the principal component analysis.The comprehensive weight value of population distribution in the study area was then used to calculate the street population distribution of Beijing in 2018.Then the population spatial distribution was visualize using GIS technology.After accuracy assessments by comparing the result with the WorldPop data,the accuracy has reached 0.74.The proposed method was validated as a qualified method to generate population spatial maps.By contrast of local areas,Luojia 1-01 data is more suitable for population distribution estimation than the NPP/VIIRS(Net Primary Productivity/Visible infrared Imaging Radiometer)nighttime light data.More geospatial big data and mathematical models can be combined to create more accurate population maps in the future.
基金Under the auspices of National Natural Science Foundation of China(No.41971202)。
文摘Investigating urban expansion patterns aids in the management of urbanization and in ameliorating the socioeconomic and environmental issues associated with economic transformation and sustainable development.Applying Harmonized Defense Meteorological Satellite Program-Operational Line-scan System(DMSP-OLS)and the Suomi National Polar-Orbiting Partnership-Visible Infrared Imagery Radiometer Suite(NPP-VIIRS)Nighttime Light(NTL)data,this paper investigated the characteristics of urban landscape in West Africa.Using the harmonized NTL data,spatial comparison and empirical threshold methods were employed to detect urban changes from 1993 to 2018.We examined the rate of urban change and calculated the direction of the urban expansion of West Africa using the center-of-gravity method for urban areas.In addition,we used the landscape expansion index method to assess the processes and stages of urban growth in West Africa.The accuracy of urban area extraction based on NTL data were R^(2)=0.8314 in 2000,R^(2)=0.8809 in 2006,R^(2)=0.9051 in 2012 for the DMSP-OLS and the simulated NPP-VIIRS was R^(2)=0.8426 in 2018,by using Google Earth images as validation.The results indicated that there was a high rate and acceleration of urban landscapes in West Africa,with rates of 0.0160,0.0173,0.0189,and 0.0686,and accelerations of 0.31,0.42,0.54,and 0.90 for the periods of 1998–2003,2003–2008,2008–2013,and 2013–2018,respectively.The expansion direction of urban agglomeration in West Africa during 1993–2018 was mainly from the coast to inland.However,cities located in the Sahel Region of Africa and in the middle zone expanded from north to south.Finally,the results showed that the urban landscape of West Africa was mainly in a scattered and disordered’diffusion’process,whereas only a few cities located in coastal areas experiencing the process of’coalescence’according to urban growth phase theory.This study provides urban planners with relevant insights for the urban expansion characteristics of West Africa.
基金Under the auspices of National Natural Science Foundation of China (No.42071342,31870713,42171329)Natural Science Foundation of Beijing,China (No.8222069,8222052)。
文摘Air pollution is a problem that directly affects human health,the global environment and the climate.The air quality index(AQI)indicates the degree of air pollution and effect on human health;however,when assessing air pollution only based on AQI monitoring data the fact that the same degree of air pollution is more harmful in more densely populated areas is ignored.In the present study,multi-source data were combined to map the distribution of the AQI and population data,and the analyze their pollution population exposure of Beijing in 2018 was analyzed.Machine learning based on the random forest algorithm was adopted to calculate the monthly average AQI of Beijing in 2018.Using Luojia-1 nighttime light remote sensing data,population statistics data,the population of Beijing in 2018 and point of interest data,the distribution of the permanent population in Beijing was estimated with a high precision of 200 m×200 m.Based on the spatialization results of the AQI and population of Beijing,the air pollution exposure levels in various parts of Beijing were calculated using the population-weighted pollution exposure level(PWEL)formula.The results show that the southern region of Beijing had a more serious level of air pollution,while the northern region was less polluted.At the same time,the population was found to agglomerate mainly in the central city and the peripheric areas thereof.In the present study,the exposure of different districts and towns in Beijing to pollution was analyzed,based on high resolution population spatialization data,it could take the pollution exposure issue down to each individual town.And we found that towns with higher exposure such as Yongshun Town,Shahe Town and Liyuan Town were all found to have a population of over 200000 which was much higher than the median population of townships of51741 in Beijing.Additionally,the change trend of air pollution exposure levels in various regions of Beijing in 2018 was almost the same,with the peak value being in winter and the lowest value being in summer.The exposure intensity in population clusters was relatively high.To reduce the level and intensity of pollution exposure,relevant departments should strengthen the governance of areas with high AQI,and pay particular attention to population clusters.
基金supported by the 2024 Open Project of Collaborative Innovation Center for Emissions Trading System Co-constructed by the Province and Ministry(24CICETS-YB013).
文摘Understanding regional carbon emissions from human activities,particularly their spatio-temporal patterns,is essential for implementing decarbonization strategies and cultivating a low-carbon economy.This study develops a spatial visualization model to estimate carbon emissions in Southeast Asia using calibrated nighttime light data,with DMSP-OLS(Defense Meteorological Satellite Program Operational Linescan System)and NPP-VIIRS(National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite)standardized through polynomial regression and machine learning to ensure consistency.Emissions in Southeast Asia increased by 2.51 times from 1992 to 2022,shifting from gradual to rapid growth.Validation against Open-source Data Inventory for Anthropogenic CO2(ODIAC)and Emissions Database for Global Atmospheric Research(EDGAR)shows strong agreement in high-emission urban areas but discrepancies in low-emission rural regions due to data sparsity and satellite sensor limits.Spatial analysis reveals that major Southeast Asian cities and their peripheries exhibit robust,sustained growth,while rural,less-developed areas show slower trends,highlighting persistent urbanrural disparities.These urban regions demonstrate a“circular economy advantage”,where per-unit-area carbon emissions steadily rise in economically advantageous zones.Despite high model accuracy,uncertainties persist due to variations in regional economic activities and the limitations of satellite-based emission proxies.Forecasts suggest elevated emission levels in major cities will continue,while changes in other areas remain relatively minimal.Consequently,achieving a low-carbon economy in Southeast Asia requires a top-down approach,emphasizing infrastructure enhancement,resource and energy optimization,and fostering a sustainable,circular socio-economic system.
基金supported by the National Natural Science Foundation of China(Grant No.42201077)the Natural Science Foundation of Shandong Province(No.ZR2021QD074)+1 种基金the China Postdoctoral Science Foundation(No.2023M732105)the Youth Innovation Team Project of Higher School in Shandong Province,China(No.2024KJH087).
文摘Electricity constitutes a fundamental pillar ofboth the national economy and contemporary lifestyles.Monitoring electric power consumption(EPC)hasimportant implications for energy planning,energyconservation and emission reduction,energy security,andsmart city development.However,the current monitoringand evaluation of EPC is less accurate and does not allowfor real-time monitoring and evaluation of EPC.This studyestablished an EPC assessment model based on EPC data,nighttime light remote sensing technology,and GISciencemethodology,aiming to analyze the spatiotemporalvariation of EPC in three major urban agglomerations ofChina from 2012 to 2020 and estimate EPC in 2025.Furthermore,the spatial correlation of EPC was exploredusing Moran’s I spatial analysis method.The resultsindicate that the established model has an average accuracyof 77.56%and can be used for accurate and real-timeestimation of EPC.The EPC showed an increasing trendfrom 2012 to 2020,with the Yangtze River Delta urbanagglomeration(YRD)exhibiting the highest growth rate,as high as 49.60%.The EPC in the Beijing-Tianjin-Hebeiurban agglomeration(BTH)showed a negative spatialcorrelation.However,the YRD and the Guangdong-HongKong-Macao Greater Bay Area urban agglomeration(GBA)exhibited significant positive spatial correlation inEPC.The findings of this study serve a scientific basis andreference data for the development of energy policies andstrategies.Furthermore,this study can help to achieve the“carbon peaking and carbon neutrality goals”proposed bythe Chinese government.
基金supported by the National Natural Science Foundation of China (Grant No.42171169)。
文摘Accurately identifying the evolving trends of regional economic inequality is crucial for promoting coordinated regional development and achieving Chinese-style modernization.This paper,based on continuous nighttime light data from1992 to 2019,uses the Dagum Gini coefficient,emerging hot spot analysis,and XGBoost-SHAP interpretable machine learning models to analyze the spatiotemporal evolution of multi-scale regional economic inequality in China,as well as the spatial cold spots and hot spots and inter-regional and intra-regional inequalities among different subregions.It also reveals the impact contributions of natural and economic factors on regional economic inequality.The results show that:(1) During the study period,provincial-level economic inequality in China gradually increased before 2003 and then decreased afterward.After 2012,the decline at the provincial level slowed down,while city-level and county-level economic inequality slightly increased.(2) The Beijing-Tianjin-Hebei,Yangtze River Delta,and Pearl River Delta city clusters remain persistent nighttime light hotspots.Multiple eastern and central city clusters are gradually becoming new hotspots,while cold spots in western regions are decreasing,and hotspots in northeastern regions are also decreasing.(3) Regional economic inequality in China exhibits significant heterogeneity both within and between regions.Differences between eastern,central,western,and northeastern regions contribute significantly to overall inequality.Differences between regions on either side of the Hu Huanyong line remain stable,but north-south differences have intensified,and differences within and outside the Yangtze Basin Economic Belt have expanded.Differences within the Yellow River Basin region remain stable.(4) Both the first and second nature factors exert complex nonlinear impacts on the formation of regional economic inequality.Factors such as industrial agglomeration,globalization,innovation,and distance from the coast show strong explanatory power.The influence of the demand of first nature and the endogenous capacity of second nature is gradually increasing.Based on the above research findings,this paper proposes policy recommendations to promote balanced regional economic development in China.
基金support from the National Natural Science Foundation of China (Nos.72272098 and 71972131).
文摘This paper examines the impact on stock return synchronicity of information provided by people who neighbor firms.Neighboring people have inherent advantages in acquiring and interpreting information.We employed novel satellite nighttime light data as a proxy for information held by neighboring people.Our analyses confirmed that brighter nighttime light was related to greater and better-quality information production.Using a sample of 18,963 firm-year observations over the 2000-2013 period,we found that information from nearby people facilitated the incorporation of firm-specific information into stock prices,resulting in lower stock return synchronicity.The results were robust when using the slope as an instrumental variable and were supported by various sensitivity checks.The effect of nighttime light intensity was more pronounced for firms operating across multiple geographic regions or diverse industries,those situated near more parks and shopping malls,and those with fewer institutional investors and less media coverage.
文摘An improved methodology for the extraction and mapping of urban built-up areas at a global scale is presented in this study.The Moderate Resolution Imaging Spectroradiometer(MODIS)-based multispectral data were combined with the Visible Infrared Imager Radiometer Suite(VIIRS)-based nighttime light(NTL)data for robust extraction and mapping of urban built-up areas.The MODIS-based newly proposed Urban Built-up Index(UBI)was combined with NTL data,and the resulting Enhanced UBI(EUBI)was used as a single master image for global extraction of urban built-up areas.Due to higher variation of the EUBI with respect to geographical regions,a region-specific threshold approach was used to extract urban built-up areas.This research provided 500-m-resolution global urban built-up map of year 2014.The resulted map was compared with three existing moderate-resolution global maps and one high-resolution map in the United States.The comparative analysis demonstrated finer details of the urban built-up cover estimated by the resultant map.
基金supported by National Natural Science Foundation of China[Grant Number 41930650]Ningxia Hui Autonomous Region Key Research and Development Project[Grant Number 2022BEG03064]State Key Laboratory INTERNATIONAL JOURNAL OF DIGITAL EARTH 2719 of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR,CASM[Grant Number 2021-03-04].
文摘The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning,resource allocation,government decision-making,disaster assessment,ecological protection,and other sustainability research.However,the existing population datasets such as LandScan and WorldPop all provide data from 2000 with limited time series,while GHS-POP only utilizes land use data with limited accuracy.In view of the limited remote sensing images of long time series,it is necessary to combine existing multi-source remote sensing data for population spatialization research.In this research,we developed a nighttime light desaturation index(NTLDI).Through the cross-sensor calibration model based on an autoencoder convolutional neural network,the NTLDl was calibrated with the same period Visible Infrared Imaging Radiometer Suite Day/Night Band(VIRS-DNB)data.Then,the geographically weighted regression method is used to determine the population density of China from 1990 to 2020 based on the long time series NTL.Furthermore,the change characteristics and the driving factors of China's population spatial distribution are analyzed.The large-scale,long-term population spatialization results obtained in this study are of great significance in government planning and decision-making,disaster assessment,resource allocation,and other aspects.
