As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limite...As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.展开更多
Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaboratio...Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaborations,and edge computing,spatial-temporal traffic data has taken on a distributed nature.Consequently,noncentralized spatial-temporal traffic prediction solutions have emerged as a recent research focus.Currently,the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station.This method reduces additional burden on communication systems.However,this method has a drawback:it cannot handle irregular traffic data.Due to unstable wireless network environments,device failures,insufficient storage resources,etc.,data missing inevitably occurs during the process of collecting traffic data.This results in the irregular nature of distributed traffic data.Yet,commonly used traffic prediction models such as Recurrent Neural Networks(RNN)and Long Short-Term Memory(LSTM)typically assume that the data is complete and regular.To address the challenge of handling irregular traffic data,this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic.To solve the aforementioned problems,this paper introduces split learning to design a structured distributed learning framework.The framework comprises a Global-level Spatial structure mining Model(GSM)and several Nodelevel Generative Models(NGMs).NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller.Firstly,the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables.Secondly,GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data.Finally,NGM generates future traffic based on latent temporal and spatial feature variables.The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data.Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction,which compensates for missing information in local irregular traffic data.The proposed framework effectively addresses the distributed prediction issues of irregular traffic data.By testing on real world datasets,the proposed framework improves traffic prediction accuracy by 35%compared to other commonly used distributed traffic prediction methods.展开更多
In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed p...In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.展开更多
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.展开更多
Thornthwaite Memorial model and other statistic methods were used to calculate the climate-productivity of plants with the meteorological data from 1961 to 2007 at 9 stations distributed on Inner Mongolia desert stepp...Thornthwaite Memorial model and other statistic methods were used to calculate the climate-productivity of plants with the meteorological data from 1961 to 2007 at 9 stations distributed on Inner Mongolia desert steppe.The spatial and temporal variation characteristics of climate-productivity were analyzed by using the methods of the tendency rate of the climate trend,accumulative anomaly,and spatial difference and so on.The results showed that the climate-productivity kept linear increased trend over Inner Mongolia desert steppe in recent 47 years,but not significant.In spatial distribution,the climate-productivity reduced with the increased latitude.The climate-productivity in southwest part of Inner Mongolia desert steppe was growing while that in the southeast was reducing.The variation rate of the climate-productivity increased from the northwest part to the southeast part of Inner Mongolia desert steppe.In recent 47 years,the climate-productivity in southeast Jurh underwent the greatest decreasing extent,and the region was the sensitive area of the climate-productivity variation.展开更多
[Objective] The aim was to discuss the relationship between forest fire and meterological elements （precipitation and temprature） in each region of China.[Method] Firstly,the average precipitation and temperature in...[Objective] The aim was to discuss the relationship between forest fire and meterological elements （precipitation and temprature） in each region of China.[Method] Firstly,the average precipitation and temperature in forest area of each province in fire season were obtained based on meterological data,forest distribution data,seasonal and monthly data of forest fire in China.Secondly,the relationship among forest fire area,precipitation and temperature was discussed through temporal and correlation analysis.[Result] The changes of precipitation and temperature with time could reflect the annual variation of fire area well.Forest fire area went up with the decrease of precipitation and increase of temprature,and visa versa.Meanwhile,there existed diffirences in the relationship in various regions over time.Correlation analyses revealed that there was positive correlation between forest fire area and temperature,especailly Northwest China （R=0.367,P〈0.01）,Southwest China （R=0.327,P〈0.05）,South China （R=0.33,P〈0.05）,East China （R=0.516,P〈0.01） and Xinjiang （R=0.447,P〈0.05） with obviously positive correlation.At the same time,the correlation between forest fire area and precipitation was significantly positive in Northwest China （R=0.482,P〈0.01）,while it was significantly negaive in South China （R=-0.323,P=0.03）,but there was no significant correlation in other regions.[Conclusion] Relationships between forest fire and meteorological elements （precipitation and temprature） revealed in the study would be useful for fire provention and early warning in China.展开更多
In 2007,China surpassed the USA to become the largest carbon emitter in the world.China has promised a 60%–65%reduction in carbon emissions per unit GDP by 2030,compared to the baseline of 2005.Therefore,it is import...In 2007,China surpassed the USA to become the largest carbon emitter in the world.China has promised a 60%–65%reduction in carbon emissions per unit GDP by 2030,compared to the baseline of 2005.Therefore,it is important to obtain accurate dynamic information on the spatial and temporal patterns of carbon emissions and carbon footprints to support formulating effective national carbon emission reduction policies.This study attempts to build a carbon emission panel data model that simulates carbon emissions in China from 2000–2013 using nighttime lighting data and carbon emission statistics data.By applying the Exploratory Spatial-Temporal Data Analysis(ESTDA)framework,this study conducted an analysis on the spatial patterns and dynamic spatial-temporal interactions of carbon footprints from 2001–2013.The improved Tapio decoupling model was adopted to investigate the levels of coupling or decoupling between the carbon emission load and economic growth in 336 prefecture-level units.The results show that,firstly,high accuracy was achieved by the model in simulating carbon emissions.Secondly,the total carbon footprints and carbon deficits across China increased with average annual growth rates of 4.82%and 5.72%,respectively.The overall carbon footprints and carbon deficits were larger in the North than that in the South.There were extremely significant spatial autocorrelation features in the carbon footprints of prefecture-level units.Thirdly,the relative lengths of the Local Indicators of Spatial Association(LISA)time paths were longer in the North than that in the South,and they increased from the coastal to the central and western regions.Lastly,the overall decoupling index was mainly a weak decoupling type,but the number of cities with this weak decoupling continued to decrease.The unsustainable development trend of China’s economic growth and carbon emission load will continue for some time.展开更多
[Objective] This paper aimed to understand the area change and distribu- tion of medium-low yield farmland, and offered basis to the improvement of mediumlow farmland and its increase of grain production in Tianjin. [...[Objective] This paper aimed to understand the area change and distribu- tion of medium-low yield farmland, and offered basis to the improvement of mediumlow farmland and its increase of grain production in Tianjin. [Method] Based on the statistical date of Tianjin and its relevant counties and districts, the yield standard was set up to classify high-yield, medium-yield and low-yield farmland in Tianjin. The author analyzed area change of medium-low yield farmland in six agricultural counties and districts （including Jixian County, Wuqing District, Baodi District, Ninghe County, Jinghai County and Dagang district of Binghai New Area） from 1980 to 2010. [Result] The results showed that the average yield of grain rose from 2 445 kg/hm^2 in 1980 to 5 130 kg/hm^2 in 2010, increasing 109.82%. The area of mediumlow yield farmland was reduced from 291 250.13 hm^2 in 1985 to 76 489.87 hm^2 in 2010, coming down 74%. In Tianjin, the area of medium-low yield farmland of 2010 accounted for 19% of the total farmland, of which the ratios of medium-low yield farmland of Jinghai County, Jixian County, Dagang district of Binghai New Area, Wuqing District, Baodi District and Ninghe County were 43.12%, 18.59%, 17.23%, 14.01%, 7.05% and 0, respectively. Low soil nutrient content, drought and water shortage, as well as soil salinization were the main yield limiting factors to mediumlow yield farmland in Tianjin in 2010. [Conclusion] The countermeasures to improve the medium-low yield farmland were proposed, involving enhancing the investment of the government, strengthening the construction of water conservancy infrastructure, further improving the soil fertility, as well as saline and alkaline land, optimizing the farming system and planting drought and salt tolerance crops, etc.展开更多
The purpose of this study was to find out the spatial-temporal rules and driving force of cultivated land quality in Henan Province in the last ten years. Agricultural land grading factor evaluation was used to evalua...The purpose of this study was to find out the spatial-temporal rules and driving force of cultivated land quality in Henan Province in the last ten years. Agricultural land grading factor evaluation was used to evaluate the cultivated land quality of 2002 and 2012 in Henan Province, and to research the change laws. Method of correlation coefficient was employed to select the driving forces affecting cultivated land quality evolution. The results indicated that the cultivated land quality in Henan Province increased slightly in the last ten years in general, and in spatial there were unchanged regions, increased regions and decreased regions. The cultivated land quality in spatial presented the trend of good becoming better, bad becoming worse, which should be highly valued in cultivated land quality protection and management. Land development and consolidation projects had significant contributions to increasing the cultivated land quality. Driving forces between the sudden change regions and gradual change regions were significantly different. The paper concluded that the research on the spatial-temporal evolution and driving force of cultivated land quality based on cultivated land quality evolution had important academic significance and practical value.展开更多
Macroscopic grasp of agricultural carbon emissions status, spatial-temporal characteristics as well as driving factors are the basic premise in further research on China’s agricultural carbon emissions. Based on 23 k...Macroscopic grasp of agricultural carbon emissions status, spatial-temporal characteristics as well as driving factors are the basic premise in further research on China’s agricultural carbon emissions. Based on 23 kinds of major carbon emission sources including agricultural materials inputs, paddy ifeld, soil and livestock breeding, this paper ifrstly calculated agricultural carbon emissions from 1995 to 2010, as well as 31 provinces and cities in 2010 in China. We then made a decomposed analysis to the driving factors of carbon emissions with logarithmic mean Divisia index （LMDI） model. The results show：（1） The amount of agricultural carbon emissions is 291.1691 million t in 2010. Compared with 249.5239 million t in 1995, it increased by 16.69%, in which, agricultural materials inputs, paddy ifeld, soil, enteric fermentation, and manure management accounted for 33.59, 22.03, 7.46, 17.53 and 19.39%of total agricultural carbon emissions, respectively. Although the amount exist ups and downs, it shows an overall trend of cyclical rise; （2） There is an obvious difference among regions：the amount of agricultural carbon emissions from top ten zones account for 56.68%, while 9.84%from last 10 zones. The traditional agricultural provinces, especially the major crop production areas are the main source regions. Based on the differences of carbon emission rations, 31 provinces and cities are divided into ifve types, namely agricultural materials dominant type, paddy ifeld dominant type, enteric fermentation dominant type, composite factors dominant type and balanced type. The agricultural carbon emissions intensity in west of China is the highest, followed by the central region, and the east zone is the lowest; （3） Compared with 1995, efifciency, labor and structure factors cut down carbon emissions by 65.78, 27.51 and 3.19%, respectively;while economy factor increase carbon emissions by 113.16%.展开更多
To probe the processes and mechanisms of soil organic carbon (SOC) changes during forest recovery, a 150-yearchronosequence study on SOC was conducted for various vegetation succession stages at the Ziwuling area, in ...To probe the processes and mechanisms of soil organic carbon (SOC) changes during forest recovery, a 150-yearchronosequence study on SOC was conducted for various vegetation succession stages at the Ziwuling area, in the centralpart of the Loess Plateau, China. Results showed that during the 150 years of local vegetation rehabilitation SOC increasedsignificantly (P < 0.05) over time in the initial period of 55-59 years, but slightly decreased afterwards. Average SOCdensities for the 0-100 cm layer of farmland, grassland, shrubland and forest were 4.46, 5.05, 9.95, and 7.49 kg C m-3,respectively. The decrease in SOC from 60 to 150 years of abandonment implied that the soil carbon pool was a sink forCO2 before the shrubland stage and became a source in the later period. This change resulted from the spatially variedcomposition and structure of the vegetation. Vegetation recovery had a maximum effect on the surface (0-20 cm) SOCpool. It. was concluded that vegetation recovery on the Loess Plateau could result in significantly increased sequestrationof atmospheric CO2 in soil and vegetation, which was ecologically important for mitigating the increase of atmosphericconcentration of CO2 and for ameliorating the local eco-environment.展开更多
Negative air ions are natural components of the air we breathe Forests are the main continuous natural source of negative air ions （NAI）. The spatio-temporal patterns of negative air ions were explored in Shanghai, ...Negative air ions are natural components of the air we breathe Forests are the main continuous natural source of negative air ions （NAI）. The spatio-temporal patterns of negative air ions were explored in Shanghai, based on monthly monitoring in 15 parks from March 2009 to February 2010. In each park, sampling sites were selected in forests and open spaces. The annual variation in negative air ion concentrations （NAIC） showed peak values from June to October and minimum values from December to January. NAIC were highest in summer and autumn, intermediate in spring, and lowest in winter. During spring and summer, NAIC in open spaces were significantly higher in rural areas than those in suburban areas. However, there were no significant differences in NAIC at forest sites among seasons. For open spaces, total suspended particles （TSP） were the dominant determining factor of NAIC in sum- mer, and air temperature and air humidity were the dominant determining factors of NAIC in spring, which were tightly correlated with Shanghai＇s ongoing urbanization and its impacts on the environment. R is suggested that urbanization could induce variation in NAIC along the urban-rural gradient, but that may not change the temporal variation pattern. Fur- thermore, the effects of urbanization on NAIC were limited in non-vegetated or less-vegetated sites, such as open spaces, but not in well-vegetated areas, such as urban forests. Therefore, we suggest that urban greening, especially urban forest, has significant resistance to theeffect of urbanization on NAIC.展开更多
Overwhelming water-deficiency conditions and an unbalanced water supply and demand have been major concerns of both the Chinese government and the general public during recent decades. Studying the spatial-temporal pa...Overwhelming water-deficiency conditions and an unbalanced water supply and demand have been major concerns of both the Chinese government and the general public during recent decades. Studying the spatial-temporal patterns and impact factors that influence water retention in China is important to enhance the management of water resources in China and other similar countries. We employed a revised Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST) model and regression analyses to investigate the water retention service in China. The results showed that the southeastern China generally performed much better than Northwest China in terms of the spatial distribution of water retention. In general, the efficacy of the water retention service in China increased from 2000 to 2014; although some areas still had a downward trend. Water retention service increased significantly(P < 0.05) in aggregate in the Qinghai-Tibet Plateau, and the Da Hinggan Mountains and Xiao Hinggan Mountains. However, the service in southwestern China showed a decreasing trend(P < 0.05), which would have significant negative impact on the downstream population. This study also showed that in China the changes in water retention service were primarily due to climate change(which could explain 83.49% of the total variance), with anthropogenic impact as a secondary influence(likewise the ecological programs and socioeconomic development could explain 9.47% and 1.06%, respectively). Moreover, the identification of water retention importance indicated that important areas conservation and selection based on downstream beneficiaries is vital for optimization protection of ecosystem services, and has practical significance for natural resources and ecosystem management.展开更多
The change in land development intensity is an important perspective to reflect the variation in regional social and economic development and spatial differentiation.In this paper,spatial statistical analysis,Ordinary...The change in land development intensity is an important perspective to reflect the variation in regional social and economic development and spatial differentiation.In this paper,spatial statistical analysis,Ordinary Least Squares(OLS),and Geographically weighted regression(GWR)methods are used to systematically analyse the spatial-temporal characteristics and driving forces of land development intensity for 131 spatial units in the western China from 2000 to 2015.The findings of the study are as follows:1)The land development intensity in the western China has been increasing rapidly.From 2000 to 2015,land development intensity increased by 3.4 times on average.2)The hotspot areas have shifted from central Inner Mongolia,northern Shaanxi and the Beibu Gulf of Guangxi to the Guanzhong Plain and the Chengdu-Chongqing urban agglomeration.The areas of cold spots were mainly concentrated in the Qinghai-Tibet Plateau,Yunnan,and Xinjiang.3)Investment intensity and the natural environment have always been the main drivers of land development intensity in the western China.Investment played a powerful role in promoting land development intensity,while the natural and ecological environment distinctly constrained such development.The effect of the economic factors on land development intensity in the western China has changed,which is reflected in the driving factor of construction land development shifting from economic growth in 2000 to economic structure,especially industrial structure,in 2015.展开更多
Haze pollution has become a severe environmental problem in the daily life of the people in China. PM2.s makes a significant contribution to poor air quality. The spatio-temporal features of China＇s PM2.s concentrati...Haze pollution has become a severe environmental problem in the daily life of the people in China. PM2.s makes a significant contribution to poor air quality. The spatio-temporal features of China＇s PM2.s concentrations should be investigated. This paper, based on ob- served data from 945 newly located monitoring sites in 2014 and industrial working population data obtained from International Standard Industrial Classification （ISIC）, reveals the spa- tio-temporal variations of PM2.5 concentrations in China and the correlations among different industries. We tested the spatial autocorrelation of PM2.5 concentrations in the cities of China with the spatial autocorrelation model. A correlation coefficient to examine the correlativity of PM2.5 concentrations and 23 characteristic variables for 190 cities in China in 2014, from which the most important ones were chosen, and then a regression model was built to further reveal the social and economic factors affecting PMg.g concentrations. Results： （1） The Hu Huanyong Line and the Yangtze River were the E-W divide and S-N divide between high and low values of China. （2） The PM2.5 concentrations shows great seasonal variation, which is high in autumn and winter but low in spring and summer. The monthly average shows a U-shaped pattern, and daily average presents a periodic and impulse-shaped change. （3） PM2.5 concentrations had a distinct characteristic of spatial agglomeration. The North China Plain was the predominant region of agglomeration, and the southeastern coastal area had stable good air quality.展开更多
Comprehensive study on land-use change of spatial pattern and temporal process is the key component in LUCC study nowadays. Based on the theories and methods of Geo-information Tupu (Carto-methodology in Geo-informati...Comprehensive study on land-use change of spatial pattern and temporal process is the key component in LUCC study nowadays. Based on the theories and methods of Geo-information Tupu (Carto-methodology in Geo-information, CMGI), integration of spatial pattern and temporal processes of land-use change in the Yellow River Delta (YRD) are studied in the paper, which is supported by ERDAS and ARC/INFO software. The main contents include: (1) concept models of Tupu by spatial-temporal integration on land-use change, whose Tupu unit is synthesized by "Spatial·Attribute·Process" features and composed of relatively homogeneous geographical unit and temporal unit; (2) data sources and handling process, where four stages of spatial features in 1956, 1984, 1991, and 1996 are acquired; (3) integration of series of temporal-spatial Tupu, reconstruction series of "Arising" Tupu, spatial-temporal Process Tupu and the spatial temporal Pattern Tupu on land-use change by remap tables; (4) Pattern Tupu analysis on land-use change in YRD during 1956-1996; and (5) spatial difference of the Pattern Tupu analysis by dynamic Tupu units. The various landform units and seven sub-deltas generated by the Yellow River since 1855 are different. The Tupu analysis on land-use in the paper is a promising try on the comprehensive research of "spatial pattern of dynamic process" and "temporal process of spatial pattern" in LUCC research. The Tupu methodology would be a powerful and efficient tool on integrated studies of spatial pattern and temporal process in Geo-science.展开更多
Urban air pollution is a prominent problem related to the urban development in China, especially in the densely populated urban agglomerations. Therefore, scientific examination of regional variation of air quality an...Urban air pollution is a prominent problem related to the urban development in China, especially in the densely populated urban agglomerations. Therefore, scientific examination of regional variation of air quality and its dominant factors is of great importance to regional environmental management. In contrast to traditional air pollution researches which only concentrate on a single year or a single pollutant, this paper analyses spatiotemporal patterns and determinants of air quality in disparate regions based on the air quality index(AQI) of the Yangtze River Delta region(YRD) of China from 2014 to 2016. Results show that the annual average value of the AQI in the YRD region decreases from 2014 to 2016 and exhibit a basic characteristic of ‘higher in winter, lower in summer and slightly high in spring and autumn'. The attainment rate of the AQI shows an apparently spatial stratified heterogeneity, Hefei metropolitan area and Nanjing metropolitan area keeping the worst air quality. The frequency of air pollution occurring in large regions was gradually decreasing during the study period. Drawing from entropy method analysis, industrialization and urbanization represented by per capita GDP and total energy consumption were the most important factors. Furthermore, population agglomeration is a factor that cannot be ignored especially in some mega-cities. Limited to data collection, more research is needed to gain insight into the spatiotemporal pattern and influence mechanism in the future.展开更多
This study has revealed spatial-temporal changes in Recreational Business Dis- tricts （RBDs） in Beijing and examined the relationship between the location of urban RBDs and traffic conditions, resident and tourist d...This study has revealed spatial-temporal changes in Recreational Business Dis- tricts （RBDs） in Beijing and examined the relationship between the location of urban RBDs and traffic conditions, resident and tourist density, scenic spots, and land prices. A more reasonable classification of urban RBDs （LSC, CPS, and ULA） is also proposed. Quantitative methods such as Gini Coefficient, Spatial Interpolation, Kernel Density Estimation, and Geographical Detector were employed to collect and analyze the data from three types of urban RBDs in Beijing in 1990, 2000, and 2014, respectively, and the spatial-temporal pat- terns as well as the distribution characteristics of urban RBDs were analyzed using ArcGIS software. It was concluded that （1） both the number and scale of urban RBDs in Beijing have been expanding and the trend for all types of urban RBDs in Beijing to be spatially agglom- erated is continuing; （2） the spatial-temporal evolution pattern of urban RBDs in Beijing is ＂single-core agglomeration-dual-core agglomeration-multi-core diffusion＂; and （3） urban RBDs were always located in areas with low traffic density, tourist attractions, high resident and tourist population density, and relatively high land valuations; these factors also affect the scale size of RBDs.展开更多
As one of the areas with numerous lakes on the Tibetan Plateau, the Hoh Xil region plays an extremely important role in the fragile plateau eco-environment. Based on topographic maps in the 1970s and Landsat TM/ETM＋ ...As one of the areas with numerous lakes on the Tibetan Plateau, the Hoh Xil region plays an extremely important role in the fragile plateau eco-environment. Based on topographic maps in the 1970s and Landsat TM/ETM＋ remote sensing images iin the 1990s and the period from 2000 to 2011, the data of 83 lakes with an area above 10 km2 each were obtained by digitization method and artificial visual interpretation technology, and the causes for lake variations were also analyzed. Some conclusions can be drawn as follows. （1） From the 1970s to 2011, the lakes in the Hoh Xil region firstly shrank and then expanded, in particular, the area of lakes generally decreased during the 1970s-1990s. Then the lakes expanded from the 1990s to 2000 and the area was slightly higher than that in the 1970s. The area of lakes dramatically increased after 2000. （2） From 2000 to 2011, the lakes with different area ranks in the Hoh Xil region showed an overall expansion trend. Meanwhile, some regional differences were also discovered. Most of the lakes expanded and were widely distributed in the northern, central and western parts of the region. Some lakes were merged together or overflowed due to their rapid expansion. A small number of lakes with the trend of area decrease or strong fluctuation were scattered in the central and southern parts of the study area. And their variations were related to their own supply conditions or hydraulic connection with the downstream lakes or rivers. （3） The increase in precipitation was the dominant factor resulting in the expansion of lakes in the Hoh Xil region. The secondary factor was the increase in meltwater from glaciers and frozen soil due to climate warming.展开更多
Analysis of spatial-temporal variations of desert vegetation under the background of climate changes can provide references for ecological restoration in arid and semi-arid areas. In this study, we used the Global Inv...Analysis of spatial-temporal variations of desert vegetation under the background of climate changes can provide references for ecological restoration in arid and semi-arid areas. In this study, we used the Global Inventory Modeling and Mapping Studies （GIMMS） NDVI data from 1982 to 2006 and Moderate Resolution Imaging Spectroradiometer （MODIS） NDVI data from 2000 to 2013 to reveal the dynamics of desert vegetation in Hexi region of Northwest China over the past three decades. We also used the annual temperature and precipitation data acquired from the Chinese meteorological stations to analyze the response of desert vegetation to climatic variations. The average value of NDVImax （the maximum NDVI during the growing season） for desert vegetation in Hexi region increased at the rate of 0.65x10-3/a （P〈0.05） from 1982 to 2013, and the significant increases of NDVImax mainly appeared in the typical desert vegetation areas. Vegetation was significantly improved in the lower reaches of Shule and Shiyang river basins, and the weighted mean center of desert vegetation mainly shifted toward the lower reaches of the two basins. Almost 95.32% of the total desert vegetation area showed positive correlation between NDVImax and annual precipitation, indicating that precipitation is the key factor for desert vegetation growth in the entire study area. Moreover, the areas with non-significant positive correlation between NDVImax and annual precipitation mainly located in the lower reaches of Shiyang and Shule river basins, this may be due to human activities. Only 7.64% of the desert vegetation showed significant positive correlation between NDVImax and annual precipitation in the Shule River Basin （an extremely arid area）, indicating that precipitation is not the most important factor for vegetation growth in this basin, and further studies are needed to investigate the mechanism for this phenomenon.展开更多
基金supported by the National Natural Science Foundation of China(Nos.U19A2044,42105132,42030609,41975037,and 42105133)the National Key Research and Development Program of China(No.2022YFC3703502)+1 种基金the Plan for Anhui Major Provincial Science&Technology Project(No.202203a07020003)Hefei Ecological Environment Bureau Project(No.2020BFFFD01804).
文摘As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.
基金supported by the Beijing Natural Science Foundation(Certificate Number:L234025).
文摘Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaborations,and edge computing,spatial-temporal traffic data has taken on a distributed nature.Consequently,noncentralized spatial-temporal traffic prediction solutions have emerged as a recent research focus.Currently,the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station.This method reduces additional burden on communication systems.However,this method has a drawback:it cannot handle irregular traffic data.Due to unstable wireless network environments,device failures,insufficient storage resources,etc.,data missing inevitably occurs during the process of collecting traffic data.This results in the irregular nature of distributed traffic data.Yet,commonly used traffic prediction models such as Recurrent Neural Networks(RNN)and Long Short-Term Memory(LSTM)typically assume that the data is complete and regular.To address the challenge of handling irregular traffic data,this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic.To solve the aforementioned problems,this paper introduces split learning to design a structured distributed learning framework.The framework comprises a Global-level Spatial structure mining Model(GSM)and several Nodelevel Generative Models(NGMs).NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller.Firstly,the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables.Secondly,GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data.Finally,NGM generates future traffic based on latent temporal and spatial feature variables.The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data.Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction,which compensates for missing information in local irregular traffic data.The proposed framework effectively addresses the distributed prediction issues of irregular traffic data.By testing on real world datasets,the proposed framework improves traffic prediction accuracy by 35%compared to other commonly used distributed traffic prediction methods.
基金supported by the National Office for Philosophy and Social Sciences(grant reference 22&ZD067).
文摘In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
基金Supported by The Inner Mongolia Natural Science Foundation (2009ms0603)Inner Mongolia Scientific Innovation Program (nmqxkjcx200706)Special Fund for Scientific Research in Central Public Welfare Institution Fundamental(Grassland Research Institute of Chinese Academy of Agricultural Science)
文摘Thornthwaite Memorial model and other statistic methods were used to calculate the climate-productivity of plants with the meteorological data from 1961 to 2007 at 9 stations distributed on Inner Mongolia desert steppe.The spatial and temporal variation characteristics of climate-productivity were analyzed by using the methods of the tendency rate of the climate trend,accumulative anomaly,and spatial difference and so on.The results showed that the climate-productivity kept linear increased trend over Inner Mongolia desert steppe in recent 47 years,but not significant.In spatial distribution,the climate-productivity reduced with the increased latitude.The climate-productivity in southwest part of Inner Mongolia desert steppe was growing while that in the southeast was reducing.The variation rate of the climate-productivity increased from the northwest part to the southeast part of Inner Mongolia desert steppe.In recent 47 years,the climate-productivity in southeast Jurh underwent the greatest decreasing extent,and the region was the sensitive area of the climate-productivity variation.
基金Supported by National Natural Science Foundation of China(40801216/D011002)~~
文摘[Objective] The aim was to discuss the relationship between forest fire and meterological elements （precipitation and temprature） in each region of China.[Method] Firstly,the average precipitation and temperature in forest area of each province in fire season were obtained based on meterological data,forest distribution data,seasonal and monthly data of forest fire in China.Secondly,the relationship among forest fire area,precipitation and temperature was discussed through temporal and correlation analysis.[Result] The changes of precipitation and temperature with time could reflect the annual variation of fire area well.Forest fire area went up with the decrease of precipitation and increase of temprature,and visa versa.Meanwhile,there existed diffirences in the relationship in various regions over time.Correlation analyses revealed that there was positive correlation between forest fire area and temperature,especailly Northwest China （R=0.367,P〈0.01）,Southwest China （R=0.327,P〈0.05）,South China （R=0.33,P〈0.05）,East China （R=0.516,P〈0.01） and Xinjiang （R=0.447,P〈0.05） with obviously positive correlation.At the same time,the correlation between forest fire area and precipitation was significantly positive in Northwest China （R=0.482,P〈0.01）,while it was significantly negaive in South China （R=-0.323,P=0.03）,but there was no significant correlation in other regions.[Conclusion] Relationships between forest fire and meteorological elements （precipitation and temprature） revealed in the study would be useful for fire provention and early warning in China.
基金National Natural Science Foundation of China Youth Science Foundation ProjectNo.41701170+1 种基金National Natural Science Foundation of China,No.41661025,No.42071216Fundamental Research Funds for the Central Universities,No.18LZUJBWZY068。
文摘In 2007,China surpassed the USA to become the largest carbon emitter in the world.China has promised a 60%–65%reduction in carbon emissions per unit GDP by 2030,compared to the baseline of 2005.Therefore,it is important to obtain accurate dynamic information on the spatial and temporal patterns of carbon emissions and carbon footprints to support formulating effective national carbon emission reduction policies.This study attempts to build a carbon emission panel data model that simulates carbon emissions in China from 2000–2013 using nighttime lighting data and carbon emission statistics data.By applying the Exploratory Spatial-Temporal Data Analysis(ESTDA)framework,this study conducted an analysis on the spatial patterns and dynamic spatial-temporal interactions of carbon footprints from 2001–2013.The improved Tapio decoupling model was adopted to investigate the levels of coupling or decoupling between the carbon emission load and economic growth in 336 prefecture-level units.The results show that,firstly,high accuracy was achieved by the model in simulating carbon emissions.Secondly,the total carbon footprints and carbon deficits across China increased with average annual growth rates of 4.82%and 5.72%,respectively.The overall carbon footprints and carbon deficits were larger in the North than that in the South.There were extremely significant spatial autocorrelation features in the carbon footprints of prefecture-level units.Thirdly,the relative lengths of the Local Indicators of Spatial Association(LISA)time paths were longer in the North than that in the South,and they increased from the coastal to the central and western regions.Lastly,the overall decoupling index was mainly a weak decoupling type,but the number of cities with this weak decoupling continued to decrease.The unsustainable development trend of China’s economic growth and carbon emission load will continue for some time.
文摘[Objective] This paper aimed to understand the area change and distribu- tion of medium-low yield farmland, and offered basis to the improvement of mediumlow farmland and its increase of grain production in Tianjin. [Method] Based on the statistical date of Tianjin and its relevant counties and districts, the yield standard was set up to classify high-yield, medium-yield and low-yield farmland in Tianjin. The author analyzed area change of medium-low yield farmland in six agricultural counties and districts （including Jixian County, Wuqing District, Baodi District, Ninghe County, Jinghai County and Dagang district of Binghai New Area） from 1980 to 2010. [Result] The results showed that the average yield of grain rose from 2 445 kg/hm^2 in 1980 to 5 130 kg/hm^2 in 2010, increasing 109.82%. The area of mediumlow yield farmland was reduced from 291 250.13 hm^2 in 1985 to 76 489.87 hm^2 in 2010, coming down 74%. In Tianjin, the area of medium-low yield farmland of 2010 accounted for 19% of the total farmland, of which the ratios of medium-low yield farmland of Jinghai County, Jixian County, Dagang district of Binghai New Area, Wuqing District, Baodi District and Ninghe County were 43.12%, 18.59%, 17.23%, 14.01%, 7.05% and 0, respectively. Low soil nutrient content, drought and water shortage, as well as soil salinization were the main yield limiting factors to mediumlow yield farmland in Tianjin in 2010. [Conclusion] The countermeasures to improve the medium-low yield farmland were proposed, involving enhancing the investment of the government, strengthening the construction of water conservancy infrastructure, further improving the soil fertility, as well as saline and alkaline land, optimizing the farming system and planting drought and salt tolerance crops, etc.
文摘The purpose of this study was to find out the spatial-temporal rules and driving force of cultivated land quality in Henan Province in the last ten years. Agricultural land grading factor evaluation was used to evaluate the cultivated land quality of 2002 and 2012 in Henan Province, and to research the change laws. Method of correlation coefficient was employed to select the driving forces affecting cultivated land quality evolution. The results indicated that the cultivated land quality in Henan Province increased slightly in the last ten years in general, and in spatial there were unchanged regions, increased regions and decreased regions. The cultivated land quality in spatial presented the trend of good becoming better, bad becoming worse, which should be highly valued in cultivated land quality protection and management. Land development and consolidation projects had significant contributions to increasing the cultivated land quality. Driving forces between the sudden change regions and gradual change regions were significantly different. The paper concluded that the research on the spatial-temporal evolution and driving force of cultivated land quality based on cultivated land quality evolution had important academic significance and practical value.
基金supported by the National Natural Science Foundation of China (71273105)the Fundamental Research Funds for the Central Universities,China (2013YB12)
文摘Macroscopic grasp of agricultural carbon emissions status, spatial-temporal characteristics as well as driving factors are the basic premise in further research on China’s agricultural carbon emissions. Based on 23 kinds of major carbon emission sources including agricultural materials inputs, paddy ifeld, soil and livestock breeding, this paper ifrstly calculated agricultural carbon emissions from 1995 to 2010, as well as 31 provinces and cities in 2010 in China. We then made a decomposed analysis to the driving factors of carbon emissions with logarithmic mean Divisia index （LMDI） model. The results show：（1） The amount of agricultural carbon emissions is 291.1691 million t in 2010. Compared with 249.5239 million t in 1995, it increased by 16.69%, in which, agricultural materials inputs, paddy ifeld, soil, enteric fermentation, and manure management accounted for 33.59, 22.03, 7.46, 17.53 and 19.39%of total agricultural carbon emissions, respectively. Although the amount exist ups and downs, it shows an overall trend of cyclical rise; （2） There is an obvious difference among regions：the amount of agricultural carbon emissions from top ten zones account for 56.68%, while 9.84%from last 10 zones. The traditional agricultural provinces, especially the major crop production areas are the main source regions. Based on the differences of carbon emission rations, 31 provinces and cities are divided into ifve types, namely agricultural materials dominant type, paddy ifeld dominant type, enteric fermentation dominant type, composite factors dominant type and balanced type. The agricultural carbon emissions intensity in west of China is the highest, followed by the central region, and the east zone is the lowest; （3） Compared with 1995, efifciency, labor and structure factors cut down carbon emissions by 65.78, 27.51 and 3.19%, respectively;while economy factor increase carbon emissions by 113.16%.
基金the National Key Basic Research Support Foundation of China (No. 2002CB111502), the NationalNatural Science Foundation of China (Nos. 40371074 and 40025106) and the China Postdoctoral Science Foundation(No. 2003033023).
文摘To probe the processes and mechanisms of soil organic carbon (SOC) changes during forest recovery, a 150-yearchronosequence study on SOC was conducted for various vegetation succession stages at the Ziwuling area, in the centralpart of the Loess Plateau, China. Results showed that during the 150 years of local vegetation rehabilitation SOC increasedsignificantly (P < 0.05) over time in the initial period of 55-59 years, but slightly decreased afterwards. Average SOCdensities for the 0-100 cm layer of farmland, grassland, shrubland and forest were 4.46, 5.05, 9.95, and 7.49 kg C m-3,respectively. The decrease in SOC from 60 to 150 years of abandonment implied that the soil carbon pool was a sink forCO2 before the shrubland stage and became a source in the later period. This change resulted from the spatially variedcomposition and structure of the vegetation. Vegetation recovery had a maximum effect on the surface (0-20 cm) SOCpool. It. was concluded that vegetation recovery on the Loess Plateau could result in significantly increased sequestrationof atmospheric CO2 in soil and vegetation, which was ecologically important for mitigating the increase of atmosphericconcentration of CO2 and for ameliorating the local eco-environment.
基金supported by the National Natural Science Foundation of China(No.40971041)
文摘Negative air ions are natural components of the air we breathe Forests are the main continuous natural source of negative air ions （NAI）. The spatio-temporal patterns of negative air ions were explored in Shanghai, based on monthly monitoring in 15 parks from March 2009 to February 2010. In each park, sampling sites were selected in forests and open spaces. The annual variation in negative air ion concentrations （NAIC） showed peak values from June to October and minimum values from December to January. NAIC were highest in summer and autumn, intermediate in spring, and lowest in winter. During spring and summer, NAIC in open spaces were significantly higher in rural areas than those in suburban areas. However, there were no significant differences in NAIC at forest sites among seasons. For open spaces, total suspended particles （TSP） were the dominant determining factor of NAIC in sum- mer, and air temperature and air humidity were the dominant determining factors of NAIC in spring, which were tightly correlated with Shanghai＇s ongoing urbanization and its impacts on the environment. R is suggested that urbanization could induce variation in NAIC along the urban-rural gradient, but that may not change the temporal variation pattern. Fur- thermore, the effects of urbanization on NAIC were limited in non-vegetated or less-vegetated sites, such as open spaces, but not in well-vegetated areas, such as urban forests. Therefore, we suggest that urban greening, especially urban forest, has significant resistance to theeffect of urbanization on NAIC.
基金National Key Technology Research and Development Program of China(No.2011BAC09B08)Special Issue of National Remote Sensing Survey and Assessment of Eco-Environment Change between 2000 and 2010(No.STSN-04-01)
文摘Overwhelming water-deficiency conditions and an unbalanced water supply and demand have been major concerns of both the Chinese government and the general public during recent decades. Studying the spatial-temporal patterns and impact factors that influence water retention in China is important to enhance the management of water resources in China and other similar countries. We employed a revised Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST) model and regression analyses to investigate the water retention service in China. The results showed that the southeastern China generally performed much better than Northwest China in terms of the spatial distribution of water retention. In general, the efficacy of the water retention service in China increased from 2000 to 2014; although some areas still had a downward trend. Water retention service increased significantly(P < 0.05) in aggregate in the Qinghai-Tibet Plateau, and the Da Hinggan Mountains and Xiao Hinggan Mountains. However, the service in southwestern China showed a decreasing trend(P < 0.05), which would have significant negative impact on the downstream population. This study also showed that in China the changes in water retention service were primarily due to climate change(which could explain 83.49% of the total variance), with anthropogenic impact as a secondary influence(likewise the ecological programs and socioeconomic development could explain 9.47% and 1.06%, respectively). Moreover, the identification of water retention importance indicated that important areas conservation and selection based on downstream beneficiaries is vital for optimization protection of ecosystem services, and has practical significance for natural resources and ecosystem management.
基金Under the auspices of Fundamental Research Funds for the Central University(No.310827171012)National Natural Science Foundation of China(No.41971178+4 种基金3167054931170664)National Key Research&Development Program of China(2017YFC0504705)Open Fund of Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity(No.SKLESS201807)Key Research&Development Program of Shaanxi Province(No.2019SF-245)
文摘The change in land development intensity is an important perspective to reflect the variation in regional social and economic development and spatial differentiation.In this paper,spatial statistical analysis,Ordinary Least Squares(OLS),and Geographically weighted regression(GWR)methods are used to systematically analyse the spatial-temporal characteristics and driving forces of land development intensity for 131 spatial units in the western China from 2000 to 2015.The findings of the study are as follows:1)The land development intensity in the western China has been increasing rapidly.From 2000 to 2015,land development intensity increased by 3.4 times on average.2)The hotspot areas have shifted from central Inner Mongolia,northern Shaanxi and the Beibu Gulf of Guangxi to the Guanzhong Plain and the Chengdu-Chongqing urban agglomeration.The areas of cold spots were mainly concentrated in the Qinghai-Tibet Plateau,Yunnan,and Xinjiang.3)Investment intensity and the natural environment have always been the main drivers of land development intensity in the western China.Investment played a powerful role in promoting land development intensity,while the natural and ecological environment distinctly constrained such development.The effect of the economic factors on land development intensity in the western China has changed,which is reflected in the driving factor of construction land development shifting from economic growth in 2000 to economic structure,especially industrial structure,in 2015.
基金Major Program of the Natural Science Foundation of China,No.41590842
文摘Haze pollution has become a severe environmental problem in the daily life of the people in China. PM2.s makes a significant contribution to poor air quality. The spatio-temporal features of China＇s PM2.s concentrations should be investigated. This paper, based on ob- served data from 945 newly located monitoring sites in 2014 and industrial working population data obtained from International Standard Industrial Classification （ISIC）, reveals the spa- tio-temporal variations of PM2.5 concentrations in China and the correlations among different industries. We tested the spatial autocorrelation of PM2.5 concentrations in the cities of China with the spatial autocorrelation model. A correlation coefficient to examine the correlativity of PM2.5 concentrations and 23 characteristic variables for 190 cities in China in 2014, from which the most important ones were chosen, and then a regression model was built to further reveal the social and economic factors affecting PMg.g concentrations. Results： （1） The Hu Huanyong Line and the Yangtze River were the E-W divide and S-N divide between high and low values of China. （2） The PM2.5 concentrations shows great seasonal variation, which is high in autumn and winter but low in spring and summer. The monthly average shows a U-shaped pattern, and daily average presents a periodic and impulse-shaped change. （3） PM2.5 concentrations had a distinct characteristic of spatial agglomeration. The North China Plain was the predominant region of agglomeration, and the southeastern coastal area had stable good air quality.
文摘Comprehensive study on land-use change of spatial pattern and temporal process is the key component in LUCC study nowadays. Based on the theories and methods of Geo-information Tupu (Carto-methodology in Geo-information, CMGI), integration of spatial pattern and temporal processes of land-use change in the Yellow River Delta (YRD) are studied in the paper, which is supported by ERDAS and ARC/INFO software. The main contents include: (1) concept models of Tupu by spatial-temporal integration on land-use change, whose Tupu unit is synthesized by "Spatial·Attribute·Process" features and composed of relatively homogeneous geographical unit and temporal unit; (2) data sources and handling process, where four stages of spatial features in 1956, 1984, 1991, and 1996 are acquired; (3) integration of series of temporal-spatial Tupu, reconstruction series of "Arising" Tupu, spatial-temporal Process Tupu and the spatial temporal Pattern Tupu on land-use change by remap tables; (4) Pattern Tupu analysis on land-use change in YRD during 1956-1996; and (5) spatial difference of the Pattern Tupu analysis by dynamic Tupu units. The various landform units and seven sub-deltas generated by the Yellow River since 1855 are different. The Tupu analysis on land-use in the paper is a promising try on the comprehensive research of "spatial pattern of dynamic process" and "temporal process of spatial pattern" in LUCC research. The Tupu methodology would be a powerful and efficient tool on integrated studies of spatial pattern and temporal process in Geo-science.
基金Under the auspices of Key Projects of the National Social Science Fund(No.16AJL015)Youth Project of Natural Science Foundation of Jiangsu Province(No.BK20170440)+1 种基金Open Foundation of Key Laboratory of Watershed Geographical Science(No.WSGS2017004)Project of Nantong Key Laboratory(No.CP12016005)
文摘Urban air pollution is a prominent problem related to the urban development in China, especially in the densely populated urban agglomerations. Therefore, scientific examination of regional variation of air quality and its dominant factors is of great importance to regional environmental management. In contrast to traditional air pollution researches which only concentrate on a single year or a single pollutant, this paper analyses spatiotemporal patterns and determinants of air quality in disparate regions based on the air quality index(AQI) of the Yangtze River Delta region(YRD) of China from 2014 to 2016. Results show that the annual average value of the AQI in the YRD region decreases from 2014 to 2016 and exhibit a basic characteristic of ‘higher in winter, lower in summer and slightly high in spring and autumn'. The attainment rate of the AQI shows an apparently spatial stratified heterogeneity, Hefei metropolitan area and Nanjing metropolitan area keeping the worst air quality. The frequency of air pollution occurring in large regions was gradually decreasing during the study period. Drawing from entropy method analysis, industrialization and urbanization represented by per capita GDP and total energy consumption were the most important factors. Furthermore, population agglomeration is a factor that cannot be ignored especially in some mega-cities. Limited to data collection, more research is needed to gain insight into the spatiotemporal pattern and influence mechanism in the future.
基金Foundation： National Natural Science Foundation of China, No.41071110
文摘This study has revealed spatial-temporal changes in Recreational Business Dis- tricts （RBDs） in Beijing and examined the relationship between the location of urban RBDs and traffic conditions, resident and tourist density, scenic spots, and land prices. A more reasonable classification of urban RBDs （LSC, CPS, and ULA） is also proposed. Quantitative methods such as Gini Coefficient, Spatial Interpolation, Kernel Density Estimation, and Geographical Detector were employed to collect and analyze the data from three types of urban RBDs in Beijing in 1990, 2000, and 2014, respectively, and the spatial-temporal pat- terns as well as the distribution characteristics of urban RBDs were analyzed using ArcGIS software. It was concluded that （1） both the number and scale of urban RBDs in Beijing have been expanding and the trend for all types of urban RBDs in Beijing to be spatially agglom- erated is continuing; （2） the spatial-temporal evolution pattern of urban RBDs in Beijing is ＂single-core agglomeration-dual-core agglomeration-multi-core diffusion＂; and （3） urban RBDs were always located in areas with low traffic density, tourist attractions, high resident and tourist population density, and relatively high land valuations; these factors also affect the scale size of RBDs.
基金National Science-technology Support Plan Project, No.2012BAC 19B07 National Natural Science Foundation of China, No.41071044+2 种基金 No.41261016 No.41190084 Youth Teacher Scientific Capability Promoting Project of Northwest Normal University, No.NWNU-LKQN- 10-35
文摘As one of the areas with numerous lakes on the Tibetan Plateau, the Hoh Xil region plays an extremely important role in the fragile plateau eco-environment. Based on topographic maps in the 1970s and Landsat TM/ETM＋ remote sensing images iin the 1990s and the period from 2000 to 2011, the data of 83 lakes with an area above 10 km2 each were obtained by digitization method and artificial visual interpretation technology, and the causes for lake variations were also analyzed. Some conclusions can be drawn as follows. （1） From the 1970s to 2011, the lakes in the Hoh Xil region firstly shrank and then expanded, in particular, the area of lakes generally decreased during the 1970s-1990s. Then the lakes expanded from the 1990s to 2000 and the area was slightly higher than that in the 1970s. The area of lakes dramatically increased after 2000. （2） From 2000 to 2011, the lakes with different area ranks in the Hoh Xil region showed an overall expansion trend. Meanwhile, some regional differences were also discovered. Most of the lakes expanded and were widely distributed in the northern, central and western parts of the region. Some lakes were merged together or overflowed due to their rapid expansion. A small number of lakes with the trend of area decrease or strong fluctuation were scattered in the central and southern parts of the study area. And their variations were related to their own supply conditions or hydraulic connection with the downstream lakes or rivers. （3） The increase in precipitation was the dominant factor resulting in the expansion of lakes in the Hoh Xil region. The secondary factor was the increase in meltwater from glaciers and frozen soil due to climate warming.
基金supported by the Opening Foundation of the State Key Laboratory Breeding Base of Desertification and Aeolian Sand Disaster Combating,Gansu Desert Control Research Institute (GSDC201503)the National Natural Science Foundation of China (41271024,31260129,31360204)+1 种基金the Program for Innovative Research Group of Gansu Province,China (1506RJIA155)Lanzhou University for providing Arc GIS technical support in the data processing
文摘Analysis of spatial-temporal variations of desert vegetation under the background of climate changes can provide references for ecological restoration in arid and semi-arid areas. In this study, we used the Global Inventory Modeling and Mapping Studies （GIMMS） NDVI data from 1982 to 2006 and Moderate Resolution Imaging Spectroradiometer （MODIS） NDVI data from 2000 to 2013 to reveal the dynamics of desert vegetation in Hexi region of Northwest China over the past three decades. We also used the annual temperature and precipitation data acquired from the Chinese meteorological stations to analyze the response of desert vegetation to climatic variations. The average value of NDVImax （the maximum NDVI during the growing season） for desert vegetation in Hexi region increased at the rate of 0.65x10-3/a （P〈0.05） from 1982 to 2013, and the significant increases of NDVImax mainly appeared in the typical desert vegetation areas. Vegetation was significantly improved in the lower reaches of Shule and Shiyang river basins, and the weighted mean center of desert vegetation mainly shifted toward the lower reaches of the two basins. Almost 95.32% of the total desert vegetation area showed positive correlation between NDVImax and annual precipitation, indicating that precipitation is the key factor for desert vegetation growth in the entire study area. Moreover, the areas with non-significant positive correlation between NDVImax and annual precipitation mainly located in the lower reaches of Shiyang and Shule river basins, this may be due to human activities. Only 7.64% of the desert vegetation showed significant positive correlation between NDVImax and annual precipitation in the Shule River Basin （an extremely arid area）, indicating that precipitation is not the most important factor for vegetation growth in this basin, and further studies are needed to investigate the mechanism for this phenomenon.
