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
Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide.Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research probl...Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide.Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem.Previous studies relied on statistical regression models that failed to capture the complex nonlinear relationships between carbon emissions and characteristic variables.In this study,we propose a machine learning algorithm for carbon emissions,a Bayesian optimized XGboost regression model,using multi-year energy carbon emission data and nighttime lights(NTL)remote sensing data from Shaanxi Province,China.Our results demonstrate that the XGboost algorithm outperforms linear regression and four other machine learning models,with an R^(2)of 0.906 and RMSE of 5.687.We observe an annual increase in carbon emissions,with high-emission counties primarily concentrated in northern and central Shaanxi Province,displaying a shift from discrete,sporadic points to contiguous,extended spatial distribution.Spatial autocorrelation clustering reveals predominantly high-high and low-low clustering patterns,with economically developed counties showing high-emission clustering and economically relatively backward counties displaying low-emission clustering.Our findings show that the use of NTL data and the XGboost algorithm can estimate and predict carbon emissionsmore accurately and provide a complementary reference for satellite remote sensing image data to serve carbon emission monitoring and assessment.This research provides an important theoretical basis for formulating practical carbon emission reduction policies and contributes to the development of techniques for accurate carbon emission estimation using remote sensing data.展开更多
In the past two to three years, the world has been heavily affected by the infectious coronavirus disease and Malawi has not been spared due to its interconnection with neighboring countries. There is no management to...In the past two to three years, the world has been heavily affected by the infectious coronavirus disease and Malawi has not been spared due to its interconnection with neighboring countries. There is no management tool to identify and model the vulnerabilities of Malawi’s districts in prioritizing health services as far as coronavirus prevalence and other infectious diseases are concerned. The aim of this study was to model coronavirus vulnerability in all districts in Malawi using Geographic Information System (GIS) to monitor the disease’s cumulative prevalence over the severely affected period between 2020 and 2021. To achieve this, four parameters associated with coronavirus prevalence, including population density, percentage of older people, temperature, and humidity, were prepared in a GIS environment and used in the modelling process. A multiscale geographically weighted regression (MGWR) model was used to model and determine the vulnerability of coronavirus in Malawi. In the MGWR modelling, the Fixed Spatial Kernel was used following a Gaussian distribution model type. The Results indicated that population density and older people (age greater than 60 years) have a more significant impact on coronavirus prevalence in Malawi. The modelling further shows that Malawi, between April 2020 and May 2021, Lilongwe, Blantyre and Thyolo were more vulnerable to coronavirus than other districts. This research has shown that spatial variability of Covid-19 cases using MGWR has the potential of providing useful insights to policymakers for targeted interventions that could otherwise not be possible to detect using non-geovisualization techniques.展开更多
Cropland abandonment is prevalent throughout China,particularly in mountainous regions.However,the varying degrees of land marginalization and the specific factors influencing different types of cropland remain insuff...Cropland abandonment is prevalent throughout China,particularly in mountainous regions.However,the varying degrees of land marginalization and the specific factors influencing different types of cropland remain insufficiently understood.This knowledge gap hampers targeted management strategies for mitigating cropland abandonment in these areas.This study used spatial analysis,logistic regression,and random forest models to explore the spatial patterns and key drivers of abandonment across flatland,terrace,and slopeland in Chongyi county,a mountainous region in Jiangxi Province of South China,utilizing data from the 2009 and 2019 national land surveys of China.Results showed that the overall cropland abandonment rate in Chongyi county was 36.46%,with particularly high rates in core terrace conservation areas.Abandoned cropland was spatially clustered,predominantly in high-elevation regions.The abandonment rates were 9.71%for flatland,37.52%for terrace,and 43.02%for slopeland.Variations in vegetation cover on abandoned cropland suggest that abandonment started earlier on slopeland and later extended to terraces and flatlands.The factors driving the abandonment of flatland,terrace,and slopeland included land altitude,cultivation height difference,and distance to forest land,respectively.Consequently,the results recommend implementing land consolidation and developing small machinery projects for the fragmented flatland.Promoting the multifunctional use of terraces could realize their ecological value.Additionally,the Program of Converting Farmland to Forest should be implemented on the low-quality slopeland.展开更多
This paper examines the current state of three of the key areas of geospatial science in Australia:positioning;earth observation(EO);and spatial infrastructures.The paper discusses the limitations and challenges that ...This paper examines the current state of three of the key areas of geospatial science in Australia:positioning;earth observation(EO);and spatial infrastructures.The paper discusses the limitations and challenges that will shape the development of these three areas of geospatial science over the next decade and then profiles what each may look like in about 2026.Australia’s national positioning infrastructure plan is guiding the development of a nation-wide,sub decimeter,real-time,outdoor positioning capability based on multi-GNSS and in particular the emerging precise point positioning−real-time kinematic(PPP-RTK)capability.Additional positioning systems including the ground-based Locata system,location-based indoor systems,and beacons,among others are also discussed.The importance of the underpinning role of a next generation dynamic datum is considered.The development of Australia’s first EO strategy is described along with the key national needs of the products of remote sensing.The development of massive on-line multi-decadal geospatial imagery data stores and processing engines for co-registered stacks of continuous base-line satellite imagery are explored.Finally,perspectives on the evolution of a future spatial knowledge infrastructure(SKI)emerging from today’s traditional spatial data infrastructures(SDIs)are provided together with discussion of the growing importance of geospatial analytics for transforming whole supply chains.展开更多
Perfluoroalkyl acid analogs(PFAAs)are a class of chemically stable environmentally persistent organic pollutants(POPs)that are difficult to degrade and have a strong capacity to accumulate in the human body.PFAAs have...Perfluoroalkyl acid analogs(PFAAs)are a class of chemically stable environmentally persistent organic pollutants(POPs)that are difficult to degrade and have a strong capacity to accumulate in the human body.PFAAs have been found to be biotoxic to humans and have been detected in various environmental media,especially in the cryosphere at trace concentrations.The cryosphere,sensitively responds to climate change,plays a crucial role in the global water,carbon and energy cycles.However,researches on cryosphere PFAAs especially in Tibetan Plateau(TP)is limited.Therefore,we summarize the physicochemical properties,physiological toxicity,spatiotemporal distribution,sources,diffusion and migration pathways,as well as analysis and removal methods of PFAAs in the cryosphere regions.The results show that PFAAs pollutants are mainly produced and distributed in the more economically developed countries in Europe and the United States,as well as in East Asia,and PFAAs can be transported by atmospheric circulation and water cycle to remote regions including cryosphere regions.The current detection methods for PFAAs in cryosphere need to be further refined for increased accuracy and convenience.There is also a need to develop more effective removal methods that will reduce the environmental and human threats posed by these PFAAs.Finally,we propose key scientific questions for future research in cryosphere including PFAAs redistribution influenced by cryosphere changes,human activities,and the interaction of other spheres.展开更多
In general, geospatial data can be divided into two formats, raster and vector formats. A raster consists of a matrix of cells where each cell contains a value representing quantitative information, such as temperatur...In general, geospatial data can be divided into two formats, raster and vector formats. A raster consists of a matrix of cells where each cell contains a value representing quantitative information, such as temperature, vegetation intensity, land use/cover, elevation, etc. A vector data consists of points, lines and polygons representing location or distance or area of landscape features in graphical forms. Many raster data are derived from remote sensing techniques using sophisticated sensors by quantitative approach and many vector data are generated from GIS processes by qualitative approach. Among them, land use/cover data is frequently used in many GIS analyses and spatial modeling processes. However, proper use of quantitative and qualitative geospatial data is important in spatial modeling and decision making. In this article, we discuss common geospatial data formats, their origins and proper use in spatial modelling and decision making processes.展开更多
The continuous degradation of ecosystem services is an important challenge faced by the world.Improvements in transportation infrastructure have had substantial impacts on economic development and ecosystem services.E...The continuous degradation of ecosystem services is an important challenge faced by the world.Improvements in transportation infrastructure have had substantial impacts on economic development and ecosystem services.Exploring the influence of traffic accessibility on ecosystem services can delay or stop their deterioration;however,studies on its impact are lacking.This study addresses this gap by analysing the impact of traffic accessibility on ecosystem services using an integrated spatial regression approach based on an evaluation of the ecosystem services value(ESV)and traffic accessibility in the Middle Reaches of the Yangtze River Urban Agglomeration(MRYRUA)in China.The results indicated that the ESV in the MRYRUA continuously decreased during the study period,and the average ESV in plain areas,areas surrounding the core cities,and areas along the main traffic routes was significantly lower than that in areas along the Yangtze River and the surrounding mountainous areas.Traffic accessibility continued to increase during the study period,and the high-value areas centred on Wuhan,Changsha,Nanchang,and Yichang were radially distributed.The global bivariate spatial autocorrelation coefficient between the average ESV and traffic accessibility was negative.The average ESV and traffic accessibility exhibited significant spatial dependence and spatial heterogeneity.Spatial regression also proved that there was a negative association between the average ESV and traffic accessibility,and scale effects were evident.The findings of this study have important policy implications for future ecological protection and transportation planning.展开更多
This study used time-series of global inventory modeling and mapping studies （GIMMS） normalized difference vegetation index （NDVI） datasets at a spatial resolution of 8 km and 15-d interval to investigate the spat...This study used time-series of global inventory modeling and mapping studies （GIMMS） normalized difference vegetation index （NDVI） datasets at a spatial resolution of 8 km and 15-d interval to investigate the spatial patterns of cropland phenology in China. A smoothing algorithm based on an asymmetric Gaussian function was first performed on NDVI dataset to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination. Subsequent processing for identifying cropping systems and extracting phenological parameters, the starting date of growing season （SGS） and the ending date of growing season （EGS） was based on the smoothed NVDI time-series data. The results showed that the cropping systems in China became complex as moving from north to south of China. Under these cropping systems, the SGS and EGS for the first growing season varied largely over space, and those regions with multiple cropping systems generally presented a significant advanced SGS and EGS than the regions with single cropping patterns. On the contrary, the phenological events of the second growing season including both the SGS and EGS showed little difference between regions. The spatial patterns of cropping systems and phenology in Chinese cropland were highly related to the geophysical environmental factors. Several anthropogenic factors, such as crop variety, cultivation levels, irrigation, and fertilizers, could profoundly influence crop phenological status. How to discriminate the impacts of biophysical forces and anthropogenic drivers on phenological events of cultivation remains a great challenge for further studies.展开更多
In order to examine the effect of the Zagros Mountains on precipitation, first, the annual and Seasonal rainfall indices （rain days frequency, rain amount, daily rainfall intensity, and heavy rains） from 43 stations...In order to examine the effect of the Zagros Mountains on precipitation, first, the annual and Seasonal rainfall indices （rain days frequency, rain amount, daily rainfall intensity, and heavy rains） from 43 stations in 1995 - 2004 between the 30° N to 35° N parallels over the mountain range were analyzed. Second, the effect of the Zagros Mountains was studied through the computation of the spatial correlations between the precipitation parameters and the topographic indices （station site elevation, station mean elevation within a radius of 2.5 km, mean elevation of 9 blocks along each of the eight Cartesian directions, and the elevation differences of these 9 blocks from the station mean elevation）. The results showed that in the cold season the maximal rainfall occurs on the upper range of west slope, while in warm season it spreads over the study area. The correlations between precipitation and elevation indices were positive on the north of the stations and negative on the south of the stations, that is, the higher elevations of the stations to the north force the uplifting of the moist air masses and increase rainfall at the stations, while the lower elevations to their south lead the movement of the moist air masses to the stations. This is due to the fact that these stations or slopes are exposed to the moist air masses coming from the Mediterranean Sea and the Persian Gulf. The heavy rain days and the summer sporadic rain events do not show significant correlations with the topographic indices. The findings indicate that the Zagros Mountains intensify the cold period frontal rains especially over the west slope and block the moist air masses from entering the interior parts of the country. Moreover, these mountains play a secondary role in creating rain days. But they are very important in the production of precipitation in the area. Therefore, their absence will decrease the amount of rainfall to their west and, in return, expand the dry climates of their west and east.展开更多
Although big data are widely used in various fields,its application is still rare in the study of mining subsidence prediction(MSP)caused by underground mining.Traditional research in MSP has the problem of oversimpli...Although big data are widely used in various fields,its application is still rare in the study of mining subsidence prediction(MSP)caused by underground mining.Traditional research in MSP has the problem of oversimplifying geological mining conditions,ignoring the fluctuation of rock layers with space.In the context of geospatial big data,a data-intensive FLAC3D(Fast Lagrangian Analysis of a Continua in 3 Dimensions)model is proposed in this paper based on borehole logs.In the modeling process,we developed a method to handle geospatial big data and were able to make full use of borehole logs.The effectiveness of the proposed method was verified by comparing the results of the traditional method,proposed method,and field observation.The findings show that the proposed method has obvious advantages over the traditional prediction results.The relative error of the maximum surface subsidence predicted by the proposed method decreased by 93.7%and the standard deviation of the prediction results(which was 70 points)decreased by 39.4%,on average.The data-intensive modeling method is of great significance for improving the accuracy of mining subsidence predictions.展开更多
The urban heat island(UHI) effect has significant effects on the quality of life and public health. Numerous studies have addressed the relationship between UHI and the increase in urban impervious surface area(ISA), ...The urban heat island(UHI) effect has significant effects on the quality of life and public health. Numerous studies have addressed the relationship between UHI and the increase in urban impervious surface area(ISA), but few of them have considered the impact of the spatial configuration of ISA on UHI. Land surface temperature(LST) may be affected not only by urban land cover, but also by neighboring land cover. The aim of this research was to investigate the effects of the abundance and spatial association of ISAs on LST. Taking Harbin City, China as an example, the impact of ISA spatial association on LST measurements was examined. The abundance of ISAs and the LST measurements were derived from Landsat Thematic Mapper(TM) imagery of 2000 and 2010, and the spatial association patterns of ISAs were calculated using the local Moran’s I index. The impacts of ISA abundance and spatial association on LST were examined using correlation analysis. The results suggested that LST has significant positive associations with both ISA abundance and the Moran’s I index of ISAs, indicating that both the abundance and spatial clustering of ISAs contribute to elevated values of LST. It was also found that LST is positively associated with clustering of high-ISA-percentage areas(i.e.,>50%) and negatively associated with clustering of low-ISA-percentage areas(i.e.,<25%). The results suggest that, in addition to the abundance of ISAs,their spatial association has a significant effect on UHIs.展开更多
The disruptive effects of the COVID-19 pandemic has rapidly shifted how individuals navigate in cities.Governments are concerned that travel behavior will shift toward a car-driven and homeworking future,shifting dema...The disruptive effects of the COVID-19 pandemic has rapidly shifted how individuals navigate in cities.Governments are concerned that travel behavior will shift toward a car-driven and homeworking future,shifting demand away from public transport use.These concerns place the recovery of public transport in a possible crisis.A resilience perspective may aid the discussion around recovery-particularly one that deviates from pre-pandemic behavior.This paper presents an empirical study of London’s public transport demand and introduces a perspective of spatial resilience to the existing body of research on post-pandemic public transport demand.This study defines spatial resilience as the rate of recovery in public transport demand within census boundaries over a period after lockdown restrictions were lifted.The relationship between spatial resilience and urban socioeconomic factors was investigated by a global spatial regression model and a localized perspective through Geographically Weighted Regression(GWR)model.In this case study of London,the analysis focuses on the period after the first COVID-19 lockdown restrictions were lifted(June 2020)and before the new restrictions in mid-September 2020.The analysis shows that outer London generally recovered faster than inner London.Factors of income,car ownership and density of public transport infrastructure were found to have the greatest influence on spatial patterns in resilience.Furthermore,influential relationships vary locally,inviting future research to examine the drivers of this spatial heterogeneity.Thus,this research recommends transport policymakers capture the influences of homeworking,ensure funding for a minimum level of service,and advocate for a polycentric recovery post-pandemic.展开更多
Mining operations can usually lead to environmental deteriorations. Underground mining activities could cause an extensive decrease in groundwater level and thus a dramatic variation in soil moisture content（SMC）. I...Mining operations can usually lead to environmental deteriorations. Underground mining activities could cause an extensive decrease in groundwater level and thus a dramatic variation in soil moisture content（SMC）. In this study, the spatial and temporal variations of SMC from 2001 to 2015 at two spatial scales（i.e., the Shendong coal mining area and the Daliuta Coal Mine） were analyzed using an improved thermal inertia model with a long-term series of Landsat TM/OLI（TM=Thematic Mapper and OLI=Operational Land Imager） data. Our results show that at large spatial scale（the Shendong coal mining area）, underground mining activities had insignificant negative impacts on SMC and that at small spatial scale（the Daliuta Coal Mine）, underground mining activities had significant negative impacts on SMC. Trend analysis of SMC demonstrated that areas with decreasing trend of SMC were mainly distributed in the mined area, indicating that underground mining is a primary cause for the drying trend in the mining region in this arid environment.展开更多
Formation and evolution of rural settlement patterns in Lianzhou City,Guangdong Province were analyzed from the perspective of space and time,on the basis of its gazetteer and relevant historical data.The results show...Formation and evolution of rural settlement patterns in Lianzhou City,Guangdong Province were analyzed from the perspective of space and time,on the basis of its gazetteer and relevant historical data.The results show that Lianzhou was first founded in the sixth year of Yuanding Period of the Western Han Dynasty,and its development could be roughly classified into 6 stages according to the construction of south–north traffic lines and regional development progress,and it witnessed the fastest development in the Ming and Qing Dynasty.In terms of spatial distribution,rural settlements in the local area show spatial continuity,Lianzhou Town is the core of rural settlement growth in the city,and towns with the most concentrated rural settlements in all stages are located in central-west and northeast parts of the city,and those with lower density of rural settlements are mostly located in minority regions in the north and mountainous areas in the east.On the basis of the above facts,the paper studies the influence of natural geological conditions,immigrant,traffic,economic development and ethnic composition on the establishment and development of rural settlements in Lianzhou City.展开更多
Urban construction land has relatively high human activity and high carbon emissions.Research on urban construction land prediction under carbon peak and neutrality goals(hereafter“dual carbon”goals)is important for...Urban construction land has relatively high human activity and high carbon emissions.Research on urban construction land prediction under carbon peak and neutrality goals(hereafter“dual carbon”goals)is important for territorial spatial planning.This study analyzed quantitative relationships between carbon emissions and urban construction land,and then modified the construction land demand prediction model.Thereafter,an integrated model for urban construction land demand prediction and spatial pattern simulation under“dual carbon”goals was developed,where urban construction land suitability was modified based on carbon source and sink capacity of different land-use types.Using Guangzhou as a case study,the integrated model was validated and applied to simulate the spatiotemporal dynamics of its urban construction land during 2030–2060 under baseline development and“dual carbon”goals scenarios.The simulation results showed that Guangzhou’s urban construction land expanded rapidly until 2030,with the spatial pattern not showing an intensive development trend.Guangzhou’s urban construction land expansion slowed during 2030–2060,with an average annual growth rate of 0.2%,and a centralized spatial pattern trend.Under the“dual carbon”goal scenario,Guangzhou’s urban construction land evolved into a polycentric development pattern in 2030.Compared with the baseline development scenario,urban construction land expansion in Guangzhou during 2030–2060 is slower,with an average annual growth rate of only 0.1%,and the polycentric development pattern of urban construction land was more prominent.Furthermore,land maintenance and growth,that is,a carbon sink,is more obvious under the“dual carbon”goals scenario,with the forest land area nearly 10.6%higher than that under the baseline development scenario.The study of urban construction land demand prediction and spatial pattern simulation under“dual carbon”goals provides a scientific decision-making support tool for territorial spatial planning,aiding in quantifying territorial spatial planning.展开更多
The high precision population forecasting and spatial distribution modeling are very important for the theory and application of population sociology,city planning and Geo-Informatics.However,the two problems need to ...The high precision population forecasting and spatial distribution modeling are very important for the theory and application of population sociology,city planning and Geo-Informatics.However,the two problems need to be solved for providing the high precision population information.One is how to improve the population forecasting precision of small area(e.g.,street scale);another is how to improve the spatial resolution of urban population distribution model.To solve the two problems,some new methods are proposed in this contribution.(1)To improve the precision of small area population forecasting,a new method is developed based on the fade factor and the slide window.(2)To improve the spatial resolution of urban population distribution model,a new method is proposed based on the land classification,public facility information and the artificial intelligence technology.For validation of the proposed methods,the real population data of 15 streets in Xicheng district,Beijing,China from 2010 to 2016,the remote sensing images and the public facility data are collected and used.A number of experiments are performed.The results show that the spatial resolution of proposed model reaches 30m*30m and the forecasting precision is better than 5%using the proposed method to forecast the population of 15 streets in Xicheng district in the next four years.展开更多
Water depths and flow velocities decisively influence the damage caused by flash floods.Geographic Information System(GIS)is a powerful and useful tool,allowing the spatial analysis of results obtained by hydraulic mo...Water depths and flow velocities decisively influence the damage caused by flash floods.Geographic Information System(GIS)is a powerful and useful tool,allowing the spatial analysis of results obtained by hydraulic modelling,namely from the HEC-RAS/HEC-GeoRAS software.The GIS spatial analysis performed in this study seeks to explain and quantify the spatial relationships between the stream channel features and flow components during flash flood events.Despite these relationships are generically known,there are few studies exploring this subject in different geographic contexts.A 1D hydraulic model was applied in a small watershed in Portugal,providing good results in the definition of floodable areas,water depths and longitudinal velocities.No direct relationship was found between water depths and velocities in the floodable areas;however,negative strong correlations were found between the two flow components along the stream centerlines.Bed slope,channel and flood width,and roughness prove to be highly relevant on the longitudinal variations of water depths and velocities and on the location of maximum values.Increasing peak discharges and return periods(R;)can change the relationships between water depths and velocities at the same location.Results can be improved with more accurate elevation data for stream channels and floodplains.展开更多
The scattering-model-based(SMB)speckle filtering for polarimetric SAR(Pol SAR)data is reasonably effective in preserving dominant scattering mechanisms.However,the efficiency strongly depends on the accuracies of both...The scattering-model-based(SMB)speckle filtering for polarimetric SAR(Pol SAR)data is reasonably effective in preserving dominant scattering mechanisms.However,the efficiency strongly depends on the accuracies of both the decomposition and classification of the scattering properties.In addition,a relatively weak speckle reduction particularly in distributed media was reported in the related literatures.In this work,an improved SMB filtering strategy is proposed considering the aforementioned deficiencies.First,the orientation angle compensation is incorporated into the SMB filtering process to remedy the overestimation of the volume scattering contribution in the Freeman-Durden decomposition.In addition,an algorithm to select the homogenous pixels is developed based on the spatial majority rule for adaptive speckle reduction.We demonstrate the superiority of the proposed methods in terms of scattering property preservation and speckle noise reduction using L-band Pol SAR data sets of San Francisco that were acquired by the NASA/JPL airborne SAR(AIRSAR)system.展开更多
The Guanzhong Plain urban agglomeration is a response to the Belt and Road Initiative in Northwest China that aims to promote regional development.The direct impact of high-speed railway construction is to shorten the...The Guanzhong Plain urban agglomeration is a response to the Belt and Road Initiative in Northwest China that aims to promote regional development.The direct impact of high-speed railway construction is to shorten the spatial-temporal distance among regions,improve the accessibility of regional transportation,and promote socioeconomic linkages.From the perspective of accessibility,this study analyzes the impact of high-speed railway construction on the spatial pattems and county-level economic relationships of the Guanzhong Plain urban agglom-eration.The results show that the construction of high-speed railway significantly improves regional accessibility,increases the potential for urban economic development,and gradually narrows the gaps in economic potential among cities.The construction of high-speed railway has increased the intensity of extenal economic relations among numerous counties in the Guanzhong Plain urban agglomeration,and most of the areas with increased connections are located in the direction of routes extension.The development of the internal economic network of the Guanzhong Plain urban agglomeration is unbalanced,and a complex network is gradually emerging with a few large cities at the core,but the construction of high-speed railway is changing the struicture of the economic network.In general,a certain degree of intrinsic coupling exists between regional accessibility change and the evolution of economic relations caused by high-speed railway,reflecting the requirements of the regional overall development strategy.展开更多
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 Key Research and Development Program in Shaanxi Province,China(No.2022ZDLSF07-05)the Fundamental Research Funds for the Central Universities,CHD(No.300102352901)。
文摘Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide.Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem.Previous studies relied on statistical regression models that failed to capture the complex nonlinear relationships between carbon emissions and characteristic variables.In this study,we propose a machine learning algorithm for carbon emissions,a Bayesian optimized XGboost regression model,using multi-year energy carbon emission data and nighttime lights(NTL)remote sensing data from Shaanxi Province,China.Our results demonstrate that the XGboost algorithm outperforms linear regression and four other machine learning models,with an R^(2)of 0.906 and RMSE of 5.687.We observe an annual increase in carbon emissions,with high-emission counties primarily concentrated in northern and central Shaanxi Province,displaying a shift from discrete,sporadic points to contiguous,extended spatial distribution.Spatial autocorrelation clustering reveals predominantly high-high and low-low clustering patterns,with economically developed counties showing high-emission clustering and economically relatively backward counties displaying low-emission clustering.Our findings show that the use of NTL data and the XGboost algorithm can estimate and predict carbon emissionsmore accurately and provide a complementary reference for satellite remote sensing image data to serve carbon emission monitoring and assessment.This research provides an important theoretical basis for formulating practical carbon emission reduction policies and contributes to the development of techniques for accurate carbon emission estimation using remote sensing data.
文摘In the past two to three years, the world has been heavily affected by the infectious coronavirus disease and Malawi has not been spared due to its interconnection with neighboring countries. There is no management tool to identify and model the vulnerabilities of Malawi’s districts in prioritizing health services as far as coronavirus prevalence and other infectious diseases are concerned. The aim of this study was to model coronavirus vulnerability in all districts in Malawi using Geographic Information System (GIS) to monitor the disease’s cumulative prevalence over the severely affected period between 2020 and 2021. To achieve this, four parameters associated with coronavirus prevalence, including population density, percentage of older people, temperature, and humidity, were prepared in a GIS environment and used in the modelling process. A multiscale geographically weighted regression (MGWR) model was used to model and determine the vulnerability of coronavirus in Malawi. In the MGWR modelling, the Fixed Spatial Kernel was used following a Gaussian distribution model type. The Results indicated that population density and older people (age greater than 60 years) have a more significant impact on coronavirus prevalence in Malawi. The modelling further shows that Malawi, between April 2020 and May 2021, Lilongwe, Blantyre and Thyolo were more vulnerable to coronavirus than other districts. This research has shown that spatial variability of Covid-19 cases using MGWR has the potential of providing useful insights to policymakers for targeted interventions that could otherwise not be possible to detect using non-geovisualization techniques.
基金supported by the National Natural Science Foundation of China[Grant No.42201232]the Science and Technology Research Project of Jiangxi Provincial Department of Education[Grant No.GJJ210539].
文摘Cropland abandonment is prevalent throughout China,particularly in mountainous regions.However,the varying degrees of land marginalization and the specific factors influencing different types of cropland remain insufficiently understood.This knowledge gap hampers targeted management strategies for mitigating cropland abandonment in these areas.This study used spatial analysis,logistic regression,and random forest models to explore the spatial patterns and key drivers of abandonment across flatland,terrace,and slopeland in Chongyi county,a mountainous region in Jiangxi Province of South China,utilizing data from the 2009 and 2019 national land surveys of China.Results showed that the overall cropland abandonment rate in Chongyi county was 36.46%,with particularly high rates in core terrace conservation areas.Abandoned cropland was spatially clustered,predominantly in high-elevation regions.The abandonment rates were 9.71%for flatland,37.52%for terrace,and 43.02%for slopeland.Variations in vegetation cover on abandoned cropland suggest that abandonment started earlier on slopeland and later extended to terraces and flatlands.The factors driving the abandonment of flatland,terrace,and slopeland included land altitude,cultivation height difference,and distance to forest land,respectively.Consequently,the results recommend implementing land consolidation and developing small machinery projects for the fragmented flatland.Promoting the multifunctional use of terraces could realize their ecological value.Additionally,the Program of Converting Farmland to Forest should be implemented on the low-quality slopeland.
文摘This paper examines the current state of three of the key areas of geospatial science in Australia:positioning;earth observation(EO);and spatial infrastructures.The paper discusses the limitations and challenges that will shape the development of these three areas of geospatial science over the next decade and then profiles what each may look like in about 2026.Australia’s national positioning infrastructure plan is guiding the development of a nation-wide,sub decimeter,real-time,outdoor positioning capability based on multi-GNSS and in particular the emerging precise point positioning−real-time kinematic(PPP-RTK)capability.Additional positioning systems including the ground-based Locata system,location-based indoor systems,and beacons,among others are also discussed.The importance of the underpinning role of a next generation dynamic datum is considered.The development of Australia’s first EO strategy is described along with the key national needs of the products of remote sensing.The development of massive on-line multi-decadal geospatial imagery data stores and processing engines for co-registered stacks of continuous base-line satellite imagery are explored.Finally,perspectives on the evolution of a future spatial knowledge infrastructure(SKI)emerging from today’s traditional spatial data infrastructures(SDIs)are provided together with discussion of the growing importance of geospatial analytics for transforming whole supply chains.
基金supported by the Gansu Provincial Science and Technology Program(22ZD6FA005)West Light Foundation of The Chinese Academy of Sciences(xbzg-zdsys-202306)+2 种基金Science Fund for Creative Research Groups of Gansu Province(Grant No.23JRRA567)Taishan Scholars Program of Shandong Province(No.tsqn202312158)Key Research and Development Program of Gansu Province(22YF7FA041)。
文摘Perfluoroalkyl acid analogs(PFAAs)are a class of chemically stable environmentally persistent organic pollutants(POPs)that are difficult to degrade and have a strong capacity to accumulate in the human body.PFAAs have been found to be biotoxic to humans and have been detected in various environmental media,especially in the cryosphere at trace concentrations.The cryosphere,sensitively responds to climate change,plays a crucial role in the global water,carbon and energy cycles.However,researches on cryosphere PFAAs especially in Tibetan Plateau(TP)is limited.Therefore,we summarize the physicochemical properties,physiological toxicity,spatiotemporal distribution,sources,diffusion and migration pathways,as well as analysis and removal methods of PFAAs in the cryosphere regions.The results show that PFAAs pollutants are mainly produced and distributed in the more economically developed countries in Europe and the United States,as well as in East Asia,and PFAAs can be transported by atmospheric circulation and water cycle to remote regions including cryosphere regions.The current detection methods for PFAAs in cryosphere need to be further refined for increased accuracy and convenience.There is also a need to develop more effective removal methods that will reduce the environmental and human threats posed by these PFAAs.Finally,we propose key scientific questions for future research in cryosphere including PFAAs redistribution influenced by cryosphere changes,human activities,and the interaction of other spheres.
文摘In general, geospatial data can be divided into two formats, raster and vector formats. A raster consists of a matrix of cells where each cell contains a value representing quantitative information, such as temperature, vegetation intensity, land use/cover, elevation, etc. A vector data consists of points, lines and polygons representing location or distance or area of landscape features in graphical forms. Many raster data are derived from remote sensing techniques using sophisticated sensors by quantitative approach and many vector data are generated from GIS processes by qualitative approach. Among them, land use/cover data is frequently used in many GIS analyses and spatial modeling processes. However, proper use of quantitative and qualitative geospatial data is important in spatial modeling and decision making. In this article, we discuss common geospatial data formats, their origins and proper use in spatial modelling and decision making processes.
基金National Natural Science Foundation of China,No.42001187,No.41701629。
文摘The continuous degradation of ecosystem services is an important challenge faced by the world.Improvements in transportation infrastructure have had substantial impacts on economic development and ecosystem services.Exploring the influence of traffic accessibility on ecosystem services can delay or stop their deterioration;however,studies on its impact are lacking.This study addresses this gap by analysing the impact of traffic accessibility on ecosystem services using an integrated spatial regression approach based on an evaluation of the ecosystem services value(ESV)and traffic accessibility in the Middle Reaches of the Yangtze River Urban Agglomeration(MRYRUA)in China.The results indicated that the ESV in the MRYRUA continuously decreased during the study period,and the average ESV in plain areas,areas surrounding the core cities,and areas along the main traffic routes was significantly lower than that in areas along the Yangtze River and the surrounding mountainous areas.Traffic accessibility continued to increase during the study period,and the high-value areas centred on Wuhan,Changsha,Nanchang,and Yichang were radially distributed.The global bivariate spatial autocorrelation coefficient between the average ESV and traffic accessibility was negative.The average ESV and traffic accessibility exhibited significant spatial dependence and spatial heterogeneity.Spatial regression also proved that there was a negative association between the average ESV and traffic accessibility,and scale effects were evident.The findings of this study have important policy implications for future ecological protection and transportation planning.
基金supported by the National Natural Science Foundation of China (40930101,40971218)the 948 Program,Ministry of Agriculture of China (2009-Z31)the Foundation for National Non-Profit Scientific Institution,Ministry of Finance of China (IARRP-2010-2)
文摘This study used time-series of global inventory modeling and mapping studies （GIMMS） normalized difference vegetation index （NDVI） datasets at a spatial resolution of 8 km and 15-d interval to investigate the spatial patterns of cropland phenology in China. A smoothing algorithm based on an asymmetric Gaussian function was first performed on NDVI dataset to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination. Subsequent processing for identifying cropping systems and extracting phenological parameters, the starting date of growing season （SGS） and the ending date of growing season （EGS） was based on the smoothed NVDI time-series data. The results showed that the cropping systems in China became complex as moving from north to south of China. Under these cropping systems, the SGS and EGS for the first growing season varied largely over space, and those regions with multiple cropping systems generally presented a significant advanced SGS and EGS than the regions with single cropping patterns. On the contrary, the phenological events of the second growing season including both the SGS and EGS showed little difference between regions. The spatial patterns of cropping systems and phenology in Chinese cropland were highly related to the geophysical environmental factors. Several anthropogenic factors, such as crop variety, cultivation levels, irrigation, and fertilizers, could profoundly influence crop phenological status. How to discriminate the impacts of biophysical forces and anthropogenic drivers on phenological events of cultivation remains a great challenge for further studies.
文摘In order to examine the effect of the Zagros Mountains on precipitation, first, the annual and Seasonal rainfall indices （rain days frequency, rain amount, daily rainfall intensity, and heavy rains） from 43 stations in 1995 - 2004 between the 30° N to 35° N parallels over the mountain range were analyzed. Second, the effect of the Zagros Mountains was studied through the computation of the spatial correlations between the precipitation parameters and the topographic indices （station site elevation, station mean elevation within a radius of 2.5 km, mean elevation of 9 blocks along each of the eight Cartesian directions, and the elevation differences of these 9 blocks from the station mean elevation）. The results showed that in the cold season the maximal rainfall occurs on the upper range of west slope, while in warm season it spreads over the study area. The correlations between precipitation and elevation indices were positive on the north of the stations and negative on the south of the stations, that is, the higher elevations of the stations to the north force the uplifting of the moist air masses and increase rainfall at the stations, while the lower elevations to their south lead the movement of the moist air masses to the stations. This is due to the fact that these stations or slopes are exposed to the moist air masses coming from the Mediterranean Sea and the Persian Gulf. The heavy rain days and the summer sporadic rain events do not show significant correlations with the topographic indices. The findings indicate that the Zagros Mountains intensify the cold period frontal rains especially over the west slope and block the moist air masses from entering the interior parts of the country. Moreover, these mountains play a secondary role in creating rain days. But they are very important in the production of precipitation in the area. Therefore, their absence will decrease the amount of rainfall to their west and, in return, expand the dry climates of their west and east.
文摘Although big data are widely used in various fields,its application is still rare in the study of mining subsidence prediction(MSP)caused by underground mining.Traditional research in MSP has the problem of oversimplifying geological mining conditions,ignoring the fluctuation of rock layers with space.In the context of geospatial big data,a data-intensive FLAC3D(Fast Lagrangian Analysis of a Continua in 3 Dimensions)model is proposed in this paper based on borehole logs.In the modeling process,we developed a method to handle geospatial big data and were able to make full use of borehole logs.The effectiveness of the proposed method was verified by comparing the results of the traditional method,proposed method,and field observation.The findings show that the proposed method has obvious advantages over the traditional prediction results.The relative error of the maximum surface subsidence predicted by the proposed method decreased by 93.7%and the standard deviation of the prediction results(which was 70 points)decreased by 39.4%,on average.The data-intensive modeling method is of great significance for improving the accuracy of mining subsidence predictions.
基金Under the auspices of the National Social Science Foundation of China(No.16BJY039)
文摘The urban heat island(UHI) effect has significant effects on the quality of life and public health. Numerous studies have addressed the relationship between UHI and the increase in urban impervious surface area(ISA), but few of them have considered the impact of the spatial configuration of ISA on UHI. Land surface temperature(LST) may be affected not only by urban land cover, but also by neighboring land cover. The aim of this research was to investigate the effects of the abundance and spatial association of ISAs on LST. Taking Harbin City, China as an example, the impact of ISA spatial association on LST measurements was examined. The abundance of ISAs and the LST measurements were derived from Landsat Thematic Mapper(TM) imagery of 2000 and 2010, and the spatial association patterns of ISAs were calculated using the local Moran’s I index. The impacts of ISA abundance and spatial association on LST were examined using correlation analysis. The results suggested that LST has significant positive associations with both ISA abundance and the Moran’s I index of ISAs, indicating that both the abundance and spatial clustering of ISAs contribute to elevated values of LST. It was also found that LST is positively associated with clustering of high-ISA-percentage areas(i.e.,>50%) and negatively associated with clustering of low-ISA-percentage areas(i.e.,<25%). The results suggest that, in addition to the abundance of ISAs,their spatial association has a significant effect on UHIs.
基金funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(grant agreement No 949670)from ESRC under JPI Urban Europe/NSFC(grant No.ES/T000287/1).
文摘The disruptive effects of the COVID-19 pandemic has rapidly shifted how individuals navigate in cities.Governments are concerned that travel behavior will shift toward a car-driven and homeworking future,shifting demand away from public transport use.These concerns place the recovery of public transport in a possible crisis.A resilience perspective may aid the discussion around recovery-particularly one that deviates from pre-pandemic behavior.This paper presents an empirical study of London’s public transport demand and introduces a perspective of spatial resilience to the existing body of research on post-pandemic public transport demand.This study defines spatial resilience as the rate of recovery in public transport demand within census boundaries over a period after lockdown restrictions were lifted.The relationship between spatial resilience and urban socioeconomic factors was investigated by a global spatial regression model and a localized perspective through Geographically Weighted Regression(GWR)model.In this case study of London,the analysis focuses on the period after the first COVID-19 lockdown restrictions were lifted(June 2020)and before the new restrictions in mid-September 2020.The analysis shows that outer London generally recovered faster than inner London.Factors of income,car ownership and density of public transport infrastructure were found to have the greatest influence on spatial patterns in resilience.Furthermore,influential relationships vary locally,inviting future research to examine the drivers of this spatial heterogeneity.Thus,this research recommends transport policymakers capture the influences of homeworking,ensure funding for a minimum level of service,and advocate for a polycentric recovery post-pandemic.
基金supported by the National Natural Science Foundation of China (U1361214)the National Key Research and Development Program of China (2016YFC0501107)
文摘Mining operations can usually lead to environmental deteriorations. Underground mining activities could cause an extensive decrease in groundwater level and thus a dramatic variation in soil moisture content（SMC）. In this study, the spatial and temporal variations of SMC from 2001 to 2015 at two spatial scales（i.e., the Shendong coal mining area and the Daliuta Coal Mine） were analyzed using an improved thermal inertia model with a long-term series of Landsat TM/OLI（TM=Thematic Mapper and OLI=Operational Land Imager） data. Our results show that at large spatial scale（the Shendong coal mining area）, underground mining activities had insignificant negative impacts on SMC and that at small spatial scale（the Daliuta Coal Mine）, underground mining activities had significant negative impacts on SMC. Trend analysis of SMC demonstrated that areas with decreasing trend of SMC were mainly distributed in the mined area, indicating that underground mining is a primary cause for the drying trend in the mining region in this arid environment.
基金Sponsored by Fundamental Research Funds for the Central Universities(GK201303006)National Natural Science Foundation of China(41171139)Key Projects of Humanities and Social Sciences Research Base of Guangdong Colleges and Universities(09JDXM84001)
文摘Formation and evolution of rural settlement patterns in Lianzhou City,Guangdong Province were analyzed from the perspective of space and time,on the basis of its gazetteer and relevant historical data.The results show that Lianzhou was first founded in the sixth year of Yuanding Period of the Western Han Dynasty,and its development could be roughly classified into 6 stages according to the construction of south–north traffic lines and regional development progress,and it witnessed the fastest development in the Ming and Qing Dynasty.In terms of spatial distribution,rural settlements in the local area show spatial continuity,Lianzhou Town is the core of rural settlement growth in the city,and towns with the most concentrated rural settlements in all stages are located in central-west and northeast parts of the city,and those with lower density of rural settlements are mostly located in minority regions in the north and mountainous areas in the east.On the basis of the above facts,the paper studies the influence of natural geological conditions,immigrant,traffic,economic development and ethnic composition on the establishment and development of rural settlements in Lianzhou City.
基金National Natural Science Foundation of China,No.41971233。
文摘Urban construction land has relatively high human activity and high carbon emissions.Research on urban construction land prediction under carbon peak and neutrality goals(hereafter“dual carbon”goals)is important for territorial spatial planning.This study analyzed quantitative relationships between carbon emissions and urban construction land,and then modified the construction land demand prediction model.Thereafter,an integrated model for urban construction land demand prediction and spatial pattern simulation under“dual carbon”goals was developed,where urban construction land suitability was modified based on carbon source and sink capacity of different land-use types.Using Guangzhou as a case study,the integrated model was validated and applied to simulate the spatiotemporal dynamics of its urban construction land during 2030–2060 under baseline development and“dual carbon”goals scenarios.The simulation results showed that Guangzhou’s urban construction land expanded rapidly until 2030,with the spatial pattern not showing an intensive development trend.Guangzhou’s urban construction land expansion slowed during 2030–2060,with an average annual growth rate of 0.2%,and a centralized spatial pattern trend.Under the“dual carbon”goal scenario,Guangzhou’s urban construction land evolved into a polycentric development pattern in 2030.Compared with the baseline development scenario,urban construction land expansion in Guangzhou during 2030–2060 is slower,with an average annual growth rate of only 0.1%,and the polycentric development pattern of urban construction land was more prominent.Furthermore,land maintenance and growth,that is,a carbon sink,is more obvious under the“dual carbon”goals scenario,with the forest land area nearly 10.6%higher than that under the baseline development scenario.The study of urban construction land demand prediction and spatial pattern simulation under“dual carbon”goals provides a scientific decision-making support tool for territorial spatial planning,aiding in quantifying territorial spatial planning.
文摘The high precision population forecasting and spatial distribution modeling are very important for the theory and application of population sociology,city planning and Geo-Informatics.However,the two problems need to be solved for providing the high precision population information.One is how to improve the population forecasting precision of small area(e.g.,street scale);another is how to improve the spatial resolution of urban population distribution model.To solve the two problems,some new methods are proposed in this contribution.(1)To improve the precision of small area population forecasting,a new method is developed based on the fade factor and the slide window.(2)To improve the spatial resolution of urban population distribution model,a new method is proposed based on the land classification,public facility information and the artificial intelligence technology.For validation of the proposed methods,the real population data of 15 streets in Xicheng district,Beijing,China from 2010 to 2016,the remote sensing images and the public facility data are collected and used.A number of experiments are performed.The results show that the spatial resolution of proposed model reaches 30m*30m and the forecasting precision is better than 5%using the proposed method to forecast the population of 15 streets in Xicheng district in the next four years.
基金Centre of Geographical Studies,No.UIDB/00295/2020,No.UIDP/00295/2020FCT–Portuguese Foundation for Science and Technology,I.P.,No.SFRH/BD/96632/2013,No.CEEIND/00268/2017Project Be Safe Slide,No.PTDC/GES-AMB/30052/2017。
文摘Water depths and flow velocities decisively influence the damage caused by flash floods.Geographic Information System(GIS)is a powerful and useful tool,allowing the spatial analysis of results obtained by hydraulic modelling,namely from the HEC-RAS/HEC-GeoRAS software.The GIS spatial analysis performed in this study seeks to explain and quantify the spatial relationships between the stream channel features and flow components during flash flood events.Despite these relationships are generically known,there are few studies exploring this subject in different geographic contexts.A 1D hydraulic model was applied in a small watershed in Portugal,providing good results in the definition of floodable areas,water depths and longitudinal velocities.No direct relationship was found between water depths and velocities in the floodable areas;however,negative strong correlations were found between the two flow components along the stream centerlines.Bed slope,channel and flood width,and roughness prove to be highly relevant on the longitudinal variations of water depths and velocities and on the location of maximum values.Increasing peak discharges and return periods(R;)can change the relationships between water depths and velocities at the same location.Results can be improved with more accurate elevation data for stream channels and floodplains.
基金Project(2012CB957702) supported by the National Basic Research Program of ChinaProjects(41590854,41431070,41274024,41321063) supported by the National Natural Science Foundation of ChinaProject(Y205771077) supported by the Hundred Talents Program of the Chinese Academy of Sciences
文摘The scattering-model-based(SMB)speckle filtering for polarimetric SAR(Pol SAR)data is reasonably effective in preserving dominant scattering mechanisms.However,the efficiency strongly depends on the accuracies of both the decomposition and classification of the scattering properties.In addition,a relatively weak speckle reduction particularly in distributed media was reported in the related literatures.In this work,an improved SMB filtering strategy is proposed considering the aforementioned deficiencies.First,the orientation angle compensation is incorporated into the SMB filtering process to remedy the overestimation of the volume scattering contribution in the Freeman-Durden decomposition.In addition,an algorithm to select the homogenous pixels is developed based on the spatial majority rule for adaptive speckle reduction.We demonstrate the superiority of the proposed methods in terms of scattering property preservation and speckle noise reduction using L-band Pol SAR data sets of San Francisco that were acquired by the NASA/JPL airborne SAR(AIRSAR)system.
基金supported by the National Natural Science Foundation of China(41831284).
文摘The Guanzhong Plain urban agglomeration is a response to the Belt and Road Initiative in Northwest China that aims to promote regional development.The direct impact of high-speed railway construction is to shorten the spatial-temporal distance among regions,improve the accessibility of regional transportation,and promote socioeconomic linkages.From the perspective of accessibility,this study analyzes the impact of high-speed railway construction on the spatial pattems and county-level economic relationships of the Guanzhong Plain urban agglom-eration.The results show that the construction of high-speed railway significantly improves regional accessibility,increases the potential for urban economic development,and gradually narrows the gaps in economic potential among cities.The construction of high-speed railway has increased the intensity of extenal economic relations among numerous counties in the Guanzhong Plain urban agglomeration,and most of the areas with increased connections are located in the direction of routes extension.The development of the internal economic network of the Guanzhong Plain urban agglomeration is unbalanced,and a complex network is gradually emerging with a few large cities at the core,but the construction of high-speed railway is changing the struicture of the economic network.In general,a certain degree of intrinsic coupling exists between regional accessibility change and the evolution of economic relations caused by high-speed railway,reflecting the requirements of the regional overall development strategy.
