Pricing dynamics and volatility are accelerating the adoption of global cryptocurrency.Despite challenges,cryptocurrencies such as Bitcoin are gaining widespread acceptance,particularly in countries with unbanked popu...Pricing dynamics and volatility are accelerating the adoption of global cryptocurrency.Despite challenges,cryptocurrencies such as Bitcoin are gaining widespread acceptance,particularly in countries with unbanked populations,the lack of bank controls,and inflation.This study investigates the global patterns of cryptocurrency adoption using Generalized Linear Models and Spatial Autoregressive Models.This research introduces a novel perspective on global cryptocurrency adoption using spatial models.Our findings reveal that cryptocurrency adoption is significantly influenced by economic instability,infrastructure availability,and spatial dynamics,with higher adoption rates in countries with limited access to traditional financial systems.展开更多
Green innovation is an important driving force for high-quality development and an important guarantee for the revitalization of the old industrial base in Northeast China.However,research on green innovation is still...Green innovation is an important driving force for high-quality development and an important guarantee for the revitalization of the old industrial base in Northeast China.However,research on green innovation is still insufficient.Using the super-efficiency epsilon-based measure Malmquist model,kernel density estimation,and spatial econometric model,this study investigated the spatiotemporal evolution characteristics and influencing factors of green innovation efficiency(GIE)in Northeast China from 2005 to 2020.The results reveal that:1)The GIE in Northeast China has obvious phased characteristics,where 2005-2011 was a period of fluctuating decline while 2012-2020 was a period of fluctuating increase,reflecting the severe resource and environmental constraints faced by the green innovation process.2)The GIE in the Northeast China has a significant spatial dependence,which has not formed a relatively stable spatial club feature.The process for improving the GIE in the Northeast China in the future is still arduous and far off.3)The interweaving and mutual influence of nonequilibrium factors have led to the diversity and complexity of the spatiotemporal pattern evolution of GIE.Overall,the level of economic development and industrial structure has a positive effect,while foreign investment and industrial agglomeration have a negative effect.The direct effects of government regulation,resource endowment,science and technology,environmental regulation,and urbanization are not significant.The research conclusion of this article can provide important reference for the revitalization of Northeast China.展开更多
This paper discusses the enrichment and depletion regularities for porphyry coppermolybdenum ore deposits in different regions and varied deposit genetic types in the same area, taking three porphyry copper-molybdenum...This paper discusses the enrichment and depletion regularities for porphyry coppermolybdenum ore deposits in different regions and varied deposit genetic types in the same area, taking three porphyry copper-molybdenum ore deposits （i.e., the Chengmenshan in Jiangxi, Wunugetushan in Inner Mongolia, Baishantang in Gansu） and two copper deposits in Gansu Province （the Huitongshan skarn deposit and Gongpoquan composite deposit） as case studies. The results show that porphyry Cu-Mo deposits or skarn copper deposits include both enrichment of the ore-forming elements and associated elements, and depletion of some lithophile dispersed elements, rare earth elements （REE） and some major elements. And the depleted elements vary with deposits, having generality and their own features. On a deposit scale, the positive anomalies of enriched elements and negative anomalies of depleted elements follow in a sequence to comprise regular anomaly models of spatial structures. The exploration in the Tongchang deposit in Jiangxi and Huitongshan deposit in Gansu suggests that anomaly models play a key role in the identification of mineral occurrences and deposits compared to one single enriched element anomaly. And the anomaly models exert a critical effect on the optimization of prospecting targets and their potential evaluation.展开更多
Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard ...Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process.Besides,due to the properties of this process,the reliability of the model must be taken into consideration when optimizing the MVs.In this work,an optimal design strategy based on the self-learning Gaussian process model(GPM) is proposed to control this kind of spatial batch process.The GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data.Unlike the conventional model based design,the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent Besides,the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties.The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.展开更多
The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized tha...The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.展开更多
This study aims to reveal the spatial structural characteristics of 1,652 Ethnic-Minority Villages(EMV)in China and to analyze the mechanisms driving their spatial heterogeneity.EMV are a special type of settlement sp...This study aims to reveal the spatial structural characteristics of 1,652 Ethnic-Minority Villages(EMV)in China and to analyze the mechanisms driving their spatial heterogeneity.EMV are a special type of settlement space that preserve a large number of historical traces of the ethnic culture of ancient China.They are important carriers of China’s excellent traditional culture and are key to the implementation of rural revitalization strategies.In this study,1652 EMV in China were selected as the research subjects.The Nearest Neighbor Index,kernel density,and spatial autocorrelation index were employed to reveal the spatial structural characteristics of minority villages.Neural network models,spatial lag models,and geographical detectors were used to analyze the formation mechanism of spatial heterogeneity in EMV.The results indicate that:(1)EMV exhibit significant spatial differentiation characterized by“single-core with multiple surrounding sub-centers,”“polarization between east and west,”“decreasing quantity from southwest to east coast to northeast to northwest,”and“large dispersion with small agglomeration.”(2)EMV are mainly distributed in areas rich in intangible cultural heritage,with high vegetation coverage and low altitude,far from central cities,and having limited arable land and an underdeveloped economy and transportation,particularly in shaded or riverbank areas.(3)Distance from the nearest river(X3),distance from central cities(X8),national intangible cultural heritage(X9),and NDVI(X10)were the main driving factors affecting the spatial distribution of EMV,whereas elevation(X1)and GDP(X5)had the weakest influence.As EMV are a relatively unique territorial spatial unit,the identification of their spatial heterogeneity characteristics not only deepens the research content of settlement geography,but also involves the assessment,protection,and development of Minority Villages,which is of great significance for the inheritance and utilization of excellent ethnic cultures in the era.展开更多
Nitrate nitrogen(NO_(3)^(-)N)from agricultural activities and in industrial wastewater has become the main source of groundwater pollution,which has raised widespread concerns,particularly in arid and semi-arid river ...Nitrate nitrogen(NO_(3)^(-)N)from agricultural activities and in industrial wastewater has become the main source of groundwater pollution,which has raised widespread concerns,particularly in arid and semi-arid river basins with little water that meets relevant standards.This study aimed to investigate the performance of spatial and non-spatial regression models in modeling nitrate pollution in a semi-intensive farming region of Iran.To perform the modeling of the groundwater's NO_(3)^(-)N concentration,both natural and anthropogenic factors affecting groundwater NO_(3)^(-)N were selected.The results of Moran's I test showed that groundwater nitrate concentration had a significant spatial dependence on the density of wells,distance from streams,total annual precipitation,and distance from roads in the study area.This study provided a way to estimate nitrate pollution using both natural and anthropogenic factors in arid and semi-arid areas where only a few factors are available.Spatial regression methods with spatial correlation structures are effective tools to support spatial decision-making in water pollution control.展开更多
Cities are important carriers of green innovation.The foundation for accelerating China's ecological civilization construction and fostering regionally coordinated and sustainable development is quantitative analy...Cities are important carriers of green innovation.The foundation for accelerating China's ecological civilization construction and fostering regionally coordinated and sustainable development is quantitative analysis of the spatial evolution pattern and influencing factors of urban green innovation,as well as revealing the development differences between regions.This study's research object includes 284 Chinese cities that are at the prefecture level or above,excluding Xizang,Hong Kong,Macao,and Taiwan of China due to incomplete data.The spatial evolution characteristics of urban green innovation in China between 2005 and 2021 are comprehensively described using the gravity center model and boxplot analysis.The factors that affect urban green innovation are examined using the spatial Durbin model(SDM).The findings indicate that:1)over the period of the study,the gravity center of urban green innovation in China has always been distributed in the Henan-Anhui border region,showing a migration characteristic of‘initially shifting northeast,subsequently southeast',and the migration speed has gradually increased.2)Although there are also noticeable disparities in east-west,the north-south gap is the main cause of the shift in China's urban green innovation gravity center.The primary areas of urban green innovation in China are the cities with green innovation levels higher than the median.3)The main influencing factor of urban green innovation is the industrial structure level.The effect of the financial development level,the government intervention level,and the openness to the outside world degree on urban green innovation is weakened in turn.The environmental regulation degree is not truly influencing urban green innovation.The impact of various factors on green innovation across cities of different sizes,exhibiting heterogeneity.This study is conducive to broadening the academic community's comprehension of the spatial evolution characteristics of urban green innovation and offering a theoretical framework for developing policies for the all-encompassing green transformation of social and economic growth.展开更多
The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination ...The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination tracker dashboard. GIS-based exploratory analysis was conducted to select four variables (poverty, black race, population density, and vaccination) to explain COVID-19 occurrence during the study period. Consequently, spatial statistical techniques such as Moran’s I, Hot Spot Analysis, Spatial Lag Model (SLM), and Spatial Error Model (SEM) were used to explain the COVID-19 occurrence and vaccination rate across the 88 counties in Ohio. The result of the Local Moran’s I analysis reveals that the epicenters of COVID-19 and vaccination followed the same patterns. Indeed, counties like Summit, Franklin, Fairfield, Hamilton, and Medina were categorized as epicenters for both COVID-19 occurrence and vaccination rate. The SEM seems to be the best model for both COVID-19 and vaccination rates, with R2 values of 0.68 and 0.70, respectively. The GWR analysis proves to be better than Ordinary Least Squares (OLS), and the distribution of R2 in the GWR is uneven throughout the study area for both COVID-19 cases and vaccinations. Some counties have a high R2 of up to 0.70 for both COVID-19 cases and vaccinations. The outcomes of the regression analyses show that the SEM models can explain 68% - 70% of COVID-19 cases and vaccination across the entire counties within the study period. COVID-19 cases and vaccination rates exhibited significant positive associations with black race and poverty throughout the study area.展开更多
In this paper,we consider the following spatial Solow-Swan model with density-dependent motion■whereσ>0,α∈(0,1)andΩ⊂ℝn(n≥1)is a bounded domain with smooth boundary andϕ∈C3([0,∞)),ϕ(s)>0 for all s≥0.We p...In this paper,we consider the following spatial Solow-Swan model with density-dependent motion■whereσ>0,α∈(0,1)andΩ⊂ℝn(n≥1)is a bounded domain with smooth boundary andϕ∈C3([0,∞)),ϕ(s)>0 for all s≥0.We prove that if■then there exists a unique time-globally classical solution(u,v)for all n≥1,such a solution is bounded and satisfies u≥0,v>0.Moreover,we show that the above solution will convergence to the steady state(1,1)exponentially in L^(∞)as t→∞.展开更多
Smart cities,a new kind of urbanization,offer a means of achieving the condition in which environmental conservation and economic growth are mutually beneficial.As a result,it is important to think about whether and h...Smart cities,a new kind of urbanization,offer a means of achieving the condition in which environmental conservation and economic growth are mutually beneficial.As a result,it is important to think about whether and how the development of smart cities might support the high-quality growth of urban economies.Based on the panel data of 163 prefecture-level cities in China from 2009–2018,the green total factor productivity(GTFP)of each prefecture-level city is measured using the SBM-GML model,and the appropriate spatial econometric model is screened by various types of tests.The spatial effect of smart city construction on GFTP is studied,and it is concluded that the pilot cities have a significant positive spatial spillover effect.The decomposition econometric model also shows that the pilot cities have a significant positive spatial spillover effect,and it also indicating that the smart city construction can also drive the surrounding cities to jointly improve the quality of economic development.Finally,the robustness of the spatial effect of smart city policy is also verified by changing the spatial measurement model and the type of spatial weight matrix,which also shows that the results of the spatial spillover effect of smart city construction are reliable.展开更多
The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environmen...The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environment.This study aims to build a prediction model to understand the spatial characteristics and piecewise effects of forest fire drivers.Using monthly grid data from 2006 to 2020,a modeling study analyzed fire occurrences during the September to April fire season in Fujian Province,China.We compared the fitting performance of the logistic regression model(LRM),the generalized additive logistic model(GALM),and the spatial generalized additive logistic model(SGALM).The results indicate that SGALMs had the best fitting results and the highest prediction accuracy.Meteorological factors significantly impacted forest fires in Fujian Province.Areas with high fire incidence were mainly concentrated in the northwest and southeast.SGALMs improved the fitting effect of fire prediction models by considering spatial effects and the flexible fitting ability of nonlinear interpretation.This model provides piecewise interpretations of forest wildfire occurrences,which can be valuable for relevant departments and will assist forest managers in refining prevention measures based on temporal and spatial differences.展开更多
To improve the performance of the traditional map matching algorithms in freeway traffic state monitoring systems using the low logging frequency GPS （global positioning system） probe data, a map matching algorithm ...To improve the performance of the traditional map matching algorithms in freeway traffic state monitoring systems using the low logging frequency GPS （global positioning system） probe data, a map matching algorithm based on the Oracle spatial data model is proposed. The algorithm uses the Oracle road network data model to analyze the spatial relationships between massive GPS positioning points and freeway networks, builds an N-shortest path algorithm to find reasonable candidate routes between GPS positioning points efficiently, and uses the fuzzy logic inference system to determine the final matched traveling route. According to the implementation with field data from Los Angeles, the computation speed of the algorithm is about 135 GPS positioning points per second and the accuracy is 98.9%. The results demonstrate the effectiveness and accuracy of the proposed algorithm for mapping massive GPS positioning data onto freeway networks with complex geometric characteristics.展开更多
Based on provincial panel data of water footprint and grey water footprint, and with the help of data envelopment analysis model considering and without considering the unde- sirable output, this paper estimates the w...Based on provincial panel data of water footprint and grey water footprint, and with the help of data envelopment analysis model considering and without considering the unde- sirable output, this paper estimates the water resources utilization efficiency in China from 1997 to 2011. The spatial weighting matrix based on economy-spatial distance function is established to discuss spatial autocorrelation of water resources utilization efficiency. With the help of absolute/3-convergence model, this paper concludes that there exists/%convergence in the water resources utilization efficiency. Under the conditions of considering and without considering the undesirable output, it takes about 52.6 and 5.6 years respectively to achieve the extent of half of convergence. By mean of the spatial Durbin econometric model, this paper studies spatial spillover effects of the provincial water resources utilization efficiency in China. The results are as follows. 1） With considering and without considering the undesir- able output, there is significant spatial correlation in provincial water resource efficiency in China. 2） Under the two cases, the spatial autoregressive coefficients （p） are 0.278 and 0.507 respectively, at 1% significance level. There exist the spatial spillover effects of provin- cial water resources utilization efficiency. 3） With considering the undesirable output, these factors of the education funds, the transportation infrastructure, and the industrial and agri- cultural water consumption proportion have positive impacts. These factors of foreign direct investment, the industry value-added water consumption per ten thousand yuan, per capita water consumption, and the total precipitation have negative impacts. 4） Without considering the undesirable output, the factor of GDP per laborer has a greater positive significant influ- ence on the water resources utilization efficiency. However the facts of industry value-added water consumption in ten thousand yuan and the transportation infrastructure have no sig- nificant influence. 5） Regardless of undesirable output of water resources utilization efficiency the assessment of the present real water resources utilization in China will be distorted and policy-making will be misled. The water efficiency measure considering environmental factors （such as gray water footprint） is more reasonable.展开更多
In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic informatio...In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)datasets.The landslide susceptibility map was produced using 14 causative factors.We applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive accuracy.The resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility modeling.SVR-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of 0.23046.The three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance.展开更多
Engineering excavation GIS (E 2 GIS) is a real-3D GIS serving for geosciences related to geo-engineering, civil engineering and mining engineering based on generalized tri-prism (GTP) model. As two instances of GTP mo...Engineering excavation GIS (E 2 GIS) is a real-3D GIS serving for geosciences related to geo-engineering, civil engineering and mining engineering based on generalized tri-prism (GTP) model. As two instances of GTP model, G\|GTP is used for the real\|3D modeling of subsurface geological bodies, and E\|GTP is used for the real\|3D modeling of subsurface engineering excavations.In the light of the discussions on the features and functions of E 2 GIS, the modeling principles of G\|GTP and E\|GTP are introduced. The two models couple together seamlessly to form an integral model for subsurface spatial objects including both geological bodies and excavations. An object\|oriented integral real\|3D data model and integral spatial topological relations are discussed.展开更多
Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning systems.This study describes the potential application of two archite...Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning systems.This study describes the potential application of two architectures of deep learning neural networks,namely convolutional neural networks(CNN)and recurrent neural networks(RNN),for spatially explicit prediction and mapping of flash flood probability.To develop and validate the predictive models,a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed.The step-wise weight assessment ratio analysis(SWARA)was employed to investigate the spatial interplay between floods and different influencing factors.The CNN and RNN models were trained using the SWARA weights and validated using the receiver operating characteristics technique.The results showed that the CNN model(AUC=0.832,RMSE=0.144)performed slightly better than the RNN model(AUC=0.814,RMSE=0.181)in predicting future floods.Further,these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study area.This study showed that the spatially explicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province,and the resulting probability maps can be used for the development of mitigation plans in response to the future floods.The general policy implication of our study suggests that design,implementation,and verification of flood early warning systems should be directed to approximately 40%of the land area characterized by high and very susceptibility to flooding.展开更多
The spatialization of population of counties in China is significant. Firstly, we can gain the estimated values of population density adaptive to different kinds of regions. Secondly, we can integrate effectively popu...The spatialization of population of counties in China is significant. Firstly, we can gain the estimated values of population density adaptive to different kinds of regions. Secondly, we can integrate effectively population data with other data including natural resources, environment, society and economy, build 1km GRIDs of natural resources reserves per person, population density and other economic and environmental data, which are necessary to the national management and macro adjustment and control of natural resources and dynamic monitoring of population. In order to establish population information system serving national decision making, three steps ought to be followed:1) establishing complete geographical spatial data foundation infrastructure including the establishment of electric map of residence with high resolution using topographical map with large scale and high resolution satellite remote sensing data, the determination of attribute information of housing and office buildings, and creating complete set of attribute database and rapid data updating; 2) establishing complete census systems including improving the transformation efficiency from census data to digital database and strengthening the link of census database and geographical spatial database, meanwhile, the government should attach great importance to the establishment and integration of population migration database; 3) considering there is no GIS software specially serving the analysis and management of population data, a practical approach is to add special modules to present software system, which works as a bridge actualizing the digitization and spatialization of population geography research.展开更多
Daihai Lake, a modern lacustrine rift basin, located in Inner Mongolia, North China, serves as an important modern analog for understanding deltaic depositional processes in an active rift setting. Two of the deltas ...Daihai Lake, a modern lacustrine rift basin, located in Inner Mongolia, North China, serves as an important modern analog for understanding deltaic depositional processes in an active rift setting. Two of the deltas （Yuanzigou delta and Bulianghe delta） on the margins of Daihai Lake were surveyed to compare and contrast stacking patterns using aerial photographs, field trenching and sediment sampling. Shallow cores and trench data collected from the margins of Daihai Lake indicate that a variety of depositional processes have been active since Daihai Lake formed. Two 3-D sedimentation models which employ chronostratigraphic correlation technique were generated. The chronostratigraphic sedimentation models predict and represent the architectures and sand-body continuity of sediments. Stratigraphical coincidence of the broad sheeted drifts and channel erosion suggests a coupling between downslope and alongslope processes. Distributary mouth bars are prevalent in the front of deltas on steeper slopes due to the dominance of down-slope flows. On the contrary, the along-slope currents favor the development of distal bar deposits with sheeted sandbodies on gentle depositional slopes. This study provides an insight into the architecture of complex sedimentary facies associated with highlighting key differences between downslope flows and alongslope currents. The distribution of sand within these deltas is of particular interests, with applications in understanding the architecture of hydrocarbon reservoirs formed in lacustrine rift basin.展开更多
Understanding crop patterns and their changes on regional scale is a critical re- quirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are st...Understanding crop patterns and their changes on regional scale is a critical re- quirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are still lacking. Based on the cross-entropy theory, a spatial production allocation model （SPAM） has been developed for presenting spa- tio-temporal dynamics of maize cropping system in Northeast China during 1980-2010. The simulated results indicated that （1） maize sown area expanded northwards to 48~N before 2000, after that the increased sown area mainly occurred in the central and southern parts of Northeast China. Meanwhile, maize also expanded eastwards to 127°E and lower elevation （less than 100 m） as well as higher elevation （mainly distributed between 200 m and 350 m）; （2） maize yield has been greatly promoted for most planted area of Northeast China, espe- cially in the planted zone between 42°N and 48°N, while the yield increase was relatively homogeneous without obvious longitudinal variations for whole region; （3） maize planting density increased gradually to a moderately high level over the investigated period, which reflected the trend of aggregation of maize cultivation driven by market demand.展开更多
文摘Pricing dynamics and volatility are accelerating the adoption of global cryptocurrency.Despite challenges,cryptocurrencies such as Bitcoin are gaining widespread acceptance,particularly in countries with unbanked populations,the lack of bank controls,and inflation.This study investigates the global patterns of cryptocurrency adoption using Generalized Linear Models and Spatial Autoregressive Models.This research introduces a novel perspective on global cryptocurrency adoption using spatial models.Our findings reveal that cryptocurrency adoption is significantly influenced by economic instability,infrastructure availability,and spatial dynamics,with higher adoption rates in countries with limited access to traditional financial systems.
基金Under the auspices of the National Natural Science Foundation of China(No.42571228,42401212)National Natural Science Foundation of Shandong(No.ZR2024MD022)。
文摘Green innovation is an important driving force for high-quality development and an important guarantee for the revitalization of the old industrial base in Northeast China.However,research on green innovation is still insufficient.Using the super-efficiency epsilon-based measure Malmquist model,kernel density estimation,and spatial econometric model,this study investigated the spatiotemporal evolution characteristics and influencing factors of green innovation efficiency(GIE)in Northeast China from 2005 to 2020.The results reveal that:1)The GIE in Northeast China has obvious phased characteristics,where 2005-2011 was a period of fluctuating decline while 2012-2020 was a period of fluctuating increase,reflecting the severe resource and environmental constraints faced by the green innovation process.2)The GIE in the Northeast China has a significant spatial dependence,which has not formed a relatively stable spatial club feature.The process for improving the GIE in the Northeast China in the future is still arduous and far off.3)The interweaving and mutual influence of nonequilibrium factors have led to the diversity and complexity of the spatiotemporal pattern evolution of GIE.Overall,the level of economic development and industrial structure has a positive effect,while foreign investment and industrial agglomeration have a negative effect.The direct effects of government regulation,resource endowment,science and technology,environmental regulation,and urbanization are not significant.The research conclusion of this article can provide important reference for the revitalization of Northeast China.
基金financially supported by the research special fund of public service sector from the Ministry of Land and Resources (No. 201111008)
文摘This paper discusses the enrichment and depletion regularities for porphyry coppermolybdenum ore deposits in different regions and varied deposit genetic types in the same area, taking three porphyry copper-molybdenum ore deposits （i.e., the Chengmenshan in Jiangxi, Wunugetushan in Inner Mongolia, Baishantang in Gansu） and two copper deposits in Gansu Province （the Huitongshan skarn deposit and Gongpoquan composite deposit） as case studies. The results show that porphyry Cu-Mo deposits or skarn copper deposits include both enrichment of the ore-forming elements and associated elements, and depletion of some lithophile dispersed elements, rare earth elements （REE） and some major elements. And the depleted elements vary with deposits, having generality and their own features. On a deposit scale, the positive anomalies of enriched elements and negative anomalies of depleted elements follow in a sequence to comprise regular anomaly models of spatial structures. The exploration in the Tongchang deposit in Jiangxi and Huitongshan deposit in Gansu suggests that anomaly models play a key role in the identification of mineral occurrences and deposits compared to one single enriched element anomaly. And the anomaly models exert a critical effect on the optimization of prospecting targets and their potential evaluation.
基金Supported by the National High Technology Research and Development Program of China(2014AA041803)the National Natural Science Foundation of China(61320106009)
文摘Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process.Besides,due to the properties of this process,the reliability of the model must be taken into consideration when optimizing the MVs.In this work,an optimal design strategy based on the self-learning Gaussian process model(GPM) is proposed to control this kind of spatial batch process.The GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data.Unlike the conventional model based design,the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent Besides,the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties.The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.
基金supported in part by the NIH grant R01CA241134supported in part by the NSF grant CMMI-1552764+3 种基金supported in part by the NSF grants DMS-1349724 and DMS-2052465supported in part by the NSF grant CCF-1740761supported in part by the U.S.-Norway Fulbright Foundation and the Research Council of Norway R&D Grant 309273supported in part by the Norwegian Centennial Chair grant and the Doctoral Dissertation Fellowship from the University of Minnesota.
文摘The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.
文摘This study aims to reveal the spatial structural characteristics of 1,652 Ethnic-Minority Villages(EMV)in China and to analyze the mechanisms driving their spatial heterogeneity.EMV are a special type of settlement space that preserve a large number of historical traces of the ethnic culture of ancient China.They are important carriers of China’s excellent traditional culture and are key to the implementation of rural revitalization strategies.In this study,1652 EMV in China were selected as the research subjects.The Nearest Neighbor Index,kernel density,and spatial autocorrelation index were employed to reveal the spatial structural characteristics of minority villages.Neural network models,spatial lag models,and geographical detectors were used to analyze the formation mechanism of spatial heterogeneity in EMV.The results indicate that:(1)EMV exhibit significant spatial differentiation characterized by“single-core with multiple surrounding sub-centers,”“polarization between east and west,”“decreasing quantity from southwest to east coast to northeast to northwest,”and“large dispersion with small agglomeration.”(2)EMV are mainly distributed in areas rich in intangible cultural heritage,with high vegetation coverage and low altitude,far from central cities,and having limited arable land and an underdeveloped economy and transportation,particularly in shaded or riverbank areas.(3)Distance from the nearest river(X3),distance from central cities(X8),national intangible cultural heritage(X9),and NDVI(X10)were the main driving factors affecting the spatial distribution of EMV,whereas elevation(X1)and GDP(X5)had the weakest influence.As EMV are a relatively unique territorial spatial unit,the identification of their spatial heterogeneity characteristics not only deepens the research content of settlement geography,but also involves the assessment,protection,and development of Minority Villages,which is of great significance for the inheritance and utilization of excellent ethnic cultures in the era.
文摘Nitrate nitrogen(NO_(3)^(-)N)from agricultural activities and in industrial wastewater has become the main source of groundwater pollution,which has raised widespread concerns,particularly in arid and semi-arid river basins with little water that meets relevant standards.This study aimed to investigate the performance of spatial and non-spatial regression models in modeling nitrate pollution in a semi-intensive farming region of Iran.To perform the modeling of the groundwater's NO_(3)^(-)N concentration,both natural and anthropogenic factors affecting groundwater NO_(3)^(-)N were selected.The results of Moran's I test showed that groundwater nitrate concentration had a significant spatial dependence on the density of wells,distance from streams,total annual precipitation,and distance from roads in the study area.This study provided a way to estimate nitrate pollution using both natural and anthropogenic factors in arid and semi-arid areas where only a few factors are available.Spatial regression methods with spatial correlation structures are effective tools to support spatial decision-making in water pollution control.
基金Under the auspices of National Natural Science Foundation of China(No.42371192)Natural Science Foundation of Hunan Province(No.2023JJ30100)Social Science Foundation of Hunan Province(No.23ZDAJ023,23YBA133)。
文摘Cities are important carriers of green innovation.The foundation for accelerating China's ecological civilization construction and fostering regionally coordinated and sustainable development is quantitative analysis of the spatial evolution pattern and influencing factors of urban green innovation,as well as revealing the development differences between regions.This study's research object includes 284 Chinese cities that are at the prefecture level or above,excluding Xizang,Hong Kong,Macao,and Taiwan of China due to incomplete data.The spatial evolution characteristics of urban green innovation in China between 2005 and 2021 are comprehensively described using the gravity center model and boxplot analysis.The factors that affect urban green innovation are examined using the spatial Durbin model(SDM).The findings indicate that:1)over the period of the study,the gravity center of urban green innovation in China has always been distributed in the Henan-Anhui border region,showing a migration characteristic of‘initially shifting northeast,subsequently southeast',and the migration speed has gradually increased.2)Although there are also noticeable disparities in east-west,the north-south gap is the main cause of the shift in China's urban green innovation gravity center.The primary areas of urban green innovation in China are the cities with green innovation levels higher than the median.3)The main influencing factor of urban green innovation is the industrial structure level.The effect of the financial development level,the government intervention level,and the openness to the outside world degree on urban green innovation is weakened in turn.The environmental regulation degree is not truly influencing urban green innovation.The impact of various factors on green innovation across cities of different sizes,exhibiting heterogeneity.This study is conducive to broadening the academic community's comprehension of the spatial evolution characteristics of urban green innovation and offering a theoretical framework for developing policies for the all-encompassing green transformation of social and economic growth.
文摘The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination tracker dashboard. GIS-based exploratory analysis was conducted to select four variables (poverty, black race, population density, and vaccination) to explain COVID-19 occurrence during the study period. Consequently, spatial statistical techniques such as Moran’s I, Hot Spot Analysis, Spatial Lag Model (SLM), and Spatial Error Model (SEM) were used to explain the COVID-19 occurrence and vaccination rate across the 88 counties in Ohio. The result of the Local Moran’s I analysis reveals that the epicenters of COVID-19 and vaccination followed the same patterns. Indeed, counties like Summit, Franklin, Fairfield, Hamilton, and Medina were categorized as epicenters for both COVID-19 occurrence and vaccination rate. The SEM seems to be the best model for both COVID-19 and vaccination rates, with R2 values of 0.68 and 0.70, respectively. The GWR analysis proves to be better than Ordinary Least Squares (OLS), and the distribution of R2 in the GWR is uneven throughout the study area for both COVID-19 cases and vaccinations. Some counties have a high R2 of up to 0.70 for both COVID-19 cases and vaccinations. The outcomes of the regression analyses show that the SEM models can explain 68% - 70% of COVID-19 cases and vaccination across the entire counties within the study period. COVID-19 cases and vaccination rates exhibited significant positive associations with black race and poverty throughout the study area.
基金supported by the Jilin Scientific and Technological Development Program(20210101466JC).
文摘In this paper,we consider the following spatial Solow-Swan model with density-dependent motion■whereσ>0,α∈(0,1)andΩ⊂ℝn(n≥1)is a bounded domain with smooth boundary andϕ∈C3([0,∞)),ϕ(s)>0 for all s≥0.We prove that if■then there exists a unique time-globally classical solution(u,v)for all n≥1,such a solution is bounded and satisfies u≥0,v>0.Moreover,we show that the above solution will convergence to the steady state(1,1)exponentially in L^(∞)as t→∞.
基金Jilin Province Social Science Project:Path Analysis and Empirical Research on Empowering Rural Industry Integration with Digital Economy in Jilin Province 2023B40Key Project of Education Science Planning in Jilin Province:Exploration of Talent Training Model for Economic Statistics Majors in Universities Based on OBE Theory-Taking Jilin Jianzhu University as an Example.ZD22028.
文摘Smart cities,a new kind of urbanization,offer a means of achieving the condition in which environmental conservation and economic growth are mutually beneficial.As a result,it is important to think about whether and how the development of smart cities might support the high-quality growth of urban economies.Based on the panel data of 163 prefecture-level cities in China from 2009–2018,the green total factor productivity(GTFP)of each prefecture-level city is measured using the SBM-GML model,and the appropriate spatial econometric model is screened by various types of tests.The spatial effect of smart city construction on GFTP is studied,and it is concluded that the pilot cities have a significant positive spatial spillover effect.The decomposition econometric model also shows that the pilot cities have a significant positive spatial spillover effect,and it also indicating that the smart city construction can also drive the surrounding cities to jointly improve the quality of economic development.Finally,the robustness of the spatial effect of smart city policy is also verified by changing the spatial measurement model and the type of spatial weight matrix,which also shows that the results of the spatial spillover effect of smart city construction are reliable.
基金supported by the Fujian Provincial Science and Technology Program“University-Industry Cooperation Project”(2024Y4015)National Key R&D Plan of Strategic International Scientific and Technological Innovation Cooperation Project(2018YFE0207800).
文摘The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environment.This study aims to build a prediction model to understand the spatial characteristics and piecewise effects of forest fire drivers.Using monthly grid data from 2006 to 2020,a modeling study analyzed fire occurrences during the September to April fire season in Fujian Province,China.We compared the fitting performance of the logistic regression model(LRM),the generalized additive logistic model(GALM),and the spatial generalized additive logistic model(SGALM).The results indicate that SGALMs had the best fitting results and the highest prediction accuracy.Meteorological factors significantly impacted forest fires in Fujian Province.Areas with high fire incidence were mainly concentrated in the northwest and southeast.SGALMs improved the fitting effect of fire prediction models by considering spatial effects and the flexible fitting ability of nonlinear interpretation.This model provides piecewise interpretations of forest wildfire occurrences,which can be valuable for relevant departments and will assist forest managers in refining prevention measures based on temporal and spatial differences.
文摘To improve the performance of the traditional map matching algorithms in freeway traffic state monitoring systems using the low logging frequency GPS （global positioning system） probe data, a map matching algorithm based on the Oracle spatial data model is proposed. The algorithm uses the Oracle road network data model to analyze the spatial relationships between massive GPS positioning points and freeway networks, builds an N-shortest path algorithm to find reasonable candidate routes between GPS positioning points efficiently, and uses the fuzzy logic inference system to determine the final matched traveling route. According to the implementation with field data from Los Angeles, the computation speed of the algorithm is about 135 GPS positioning points per second and the accuracy is 98.9%. The results demonstrate the effectiveness and accuracy of the proposed algorithm for mapping massive GPS positioning data onto freeway networks with complex geometric characteristics.
基金National Social Science Foundation of China, No. 11BJY063 Program for New Century Excellent Talents in University, No.NECT-13-0844
文摘Based on provincial panel data of water footprint and grey water footprint, and with the help of data envelopment analysis model considering and without considering the unde- sirable output, this paper estimates the water resources utilization efficiency in China from 1997 to 2011. The spatial weighting matrix based on economy-spatial distance function is established to discuss spatial autocorrelation of water resources utilization efficiency. With the help of absolute/3-convergence model, this paper concludes that there exists/%convergence in the water resources utilization efficiency. Under the conditions of considering and without considering the undesirable output, it takes about 52.6 and 5.6 years respectively to achieve the extent of half of convergence. By mean of the spatial Durbin econometric model, this paper studies spatial spillover effects of the provincial water resources utilization efficiency in China. The results are as follows. 1） With considering and without considering the undesir- able output, there is significant spatial correlation in provincial water resource efficiency in China. 2） Under the two cases, the spatial autoregressive coefficients （p） are 0.278 and 0.507 respectively, at 1% significance level. There exist the spatial spillover effects of provin- cial water resources utilization efficiency. 3） With considering the undesirable output, these factors of the education funds, the transportation infrastructure, and the industrial and agri- cultural water consumption proportion have positive impacts. These factors of foreign direct investment, the industry value-added water consumption per ten thousand yuan, per capita water consumption, and the total precipitation have negative impacts. 4） Without considering the undesirable output, the factor of GDP per laborer has a greater positive significant influ- ence on the water resources utilization efficiency. However the facts of industry value-added water consumption in ten thousand yuan and the transportation infrastructure have no sig- nificant influence. 5） Regardless of undesirable output of water resources utilization efficiency the assessment of the present real water resources utilization in China will be distorted and policy-making will be misled. The water efficiency measure considering environmental factors （such as gray water footprint） is more reasonable.
基金supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources(KIGAM)Project of Environmental Business Big Data Platform and Center Construction funded by the Ministry of Science and ICT。
文摘In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)datasets.The landslide susceptibility map was produced using 14 causative factors.We applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive accuracy.The resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility modeling.SVR-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of 0.23046.The three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance.
文摘Engineering excavation GIS (E 2 GIS) is a real-3D GIS serving for geosciences related to geo-engineering, civil engineering and mining engineering based on generalized tri-prism (GTP) model. As two instances of GTP model, G\|GTP is used for the real\|3D modeling of subsurface geological bodies, and E\|GTP is used for the real\|3D modeling of subsurface engineering excavations.In the light of the discussions on the features and functions of E 2 GIS, the modeling principles of G\|GTP and E\|GTP are introduced. The two models couple together seamlessly to form an integral model for subsurface spatial objects including both geological bodies and excavations. An object\|oriented integral real\|3D data model and integral spatial topological relations are discussed.
基金conducted by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources(KIGAM)funded by the Ministry of Science and ICT。
文摘Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning systems.This study describes the potential application of two architectures of deep learning neural networks,namely convolutional neural networks(CNN)and recurrent neural networks(RNN),for spatially explicit prediction and mapping of flash flood probability.To develop and validate the predictive models,a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed.The step-wise weight assessment ratio analysis(SWARA)was employed to investigate the spatial interplay between floods and different influencing factors.The CNN and RNN models were trained using the SWARA weights and validated using the receiver operating characteristics technique.The results showed that the CNN model(AUC=0.832,RMSE=0.144)performed slightly better than the RNN model(AUC=0.814,RMSE=0.181)in predicting future floods.Further,these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study area.This study showed that the spatially explicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province,and the resulting probability maps can be used for the development of mitigation plans in response to the future floods.The general policy implication of our study suggests that design,implementation,and verification of flood early warning systems should be directed to approximately 40%of the land area characterized by high and very susceptibility to flooding.
文摘The spatialization of population of counties in China is significant. Firstly, we can gain the estimated values of population density adaptive to different kinds of regions. Secondly, we can integrate effectively population data with other data including natural resources, environment, society and economy, build 1km GRIDs of natural resources reserves per person, population density and other economic and environmental data, which are necessary to the national management and macro adjustment and control of natural resources and dynamic monitoring of population. In order to establish population information system serving national decision making, three steps ought to be followed:1) establishing complete geographical spatial data foundation infrastructure including the establishment of electric map of residence with high resolution using topographical map with large scale and high resolution satellite remote sensing data, the determination of attribute information of housing and office buildings, and creating complete set of attribute database and rapid data updating; 2) establishing complete census systems including improving the transformation efficiency from census data to digital database and strengthening the link of census database and geographical spatial database, meanwhile, the government should attach great importance to the establishment and integration of population migration database; 3) considering there is no GIS software specially serving the analysis and management of population data, a practical approach is to add special modules to present software system, which works as a bridge actualizing the digitization and spatialization of population geography research.
基金supported by the Chinese National Natural Science Fund Project(41072084)National Program on Key Basic Research Project(973 Program)(No. 2009CB219502-3)Prof.Qiu Yinan at RIPED(Research Institute for Petroleum Exploration and Development) of CNPC(China National Petroleum Corporation) for his belief in this work and financial support of the research
文摘Daihai Lake, a modern lacustrine rift basin, located in Inner Mongolia, North China, serves as an important modern analog for understanding deltaic depositional processes in an active rift setting. Two of the deltas （Yuanzigou delta and Bulianghe delta） on the margins of Daihai Lake were surveyed to compare and contrast stacking patterns using aerial photographs, field trenching and sediment sampling. Shallow cores and trench data collected from the margins of Daihai Lake indicate that a variety of depositional processes have been active since Daihai Lake formed. Two 3-D sedimentation models which employ chronostratigraphic correlation technique were generated. The chronostratigraphic sedimentation models predict and represent the architectures and sand-body continuity of sediments. Stratigraphical coincidence of the broad sheeted drifts and channel erosion suggests a coupling between downslope and alongslope processes. Distributary mouth bars are prevalent in the front of deltas on steeper slopes due to the dominance of down-slope flows. On the contrary, the along-slope currents favor the development of distal bar deposits with sheeted sandbodies on gentle depositional slopes. This study provides an insight into the architecture of complex sedimentary facies associated with highlighting key differences between downslope flows and alongslope currents. The distribution of sand within these deltas is of particular interests, with applications in understanding the architecture of hydrocarbon reservoirs formed in lacustrine rift basin.
基金Foundation： National Natural Science Foundation of China, No.41171328, No.41201184, No.41101537 National Basic Program of China, No.2010CB951502
文摘Understanding crop patterns and their changes on regional scale is a critical re- quirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are still lacking. Based on the cross-entropy theory, a spatial production allocation model （SPAM） has been developed for presenting spa- tio-temporal dynamics of maize cropping system in Northeast China during 1980-2010. The simulated results indicated that （1） maize sown area expanded northwards to 48~N before 2000, after that the increased sown area mainly occurred in the central and southern parts of Northeast China. Meanwhile, maize also expanded eastwards to 127°E and lower elevation （less than 100 m） as well as higher elevation （mainly distributed between 200 m and 350 m）; （2） maize yield has been greatly promoted for most planted area of Northeast China, espe- cially in the planted zone between 42°N and 48°N, while the yield increase was relatively homogeneous without obvious longitudinal variations for whole region; （3） maize planting density increased gradually to a moderately high level over the investigated period, which reflected the trend of aggregation of maize cultivation driven by market demand.
