Scrub typhus is a significant public health issue with a wide distribution and is influenced by various determinants.However,in order to effectively eradicate scrub typhus,it is crucial to identify the specific factor...Scrub typhus is a significant public health issue with a wide distribution and is influenced by various determinants.However,in order to effectively eradicate scrub typhus,it is crucial to identify the specific factors that contribute to its incidence at a detailed level.Therefore,the objective of our study is to identify these influencing factors,examine the spatial variations in incidence,and analyze the interplay of two factors on scrub typhus incidence,so as to provide valuable experience for the prevention and treatment of scrub typhus in Gannan and to alleviate the economic burden of the local population.This study employed spatial autocorrelation analyses to examine the dependent variable and ordinary least squares model residuals.Additionally,spatial regression modelling and geographical detector were used to analyze the factors influencing the annual mean 14-year incidence of scrub typhus in the streets/townships of Gannan region from 2008 to 2021.The results of spatial1 autocorrelation analyses indicated the presence of spatial correlation.Among the global spatial regression models,the spatial lag model was found to be the best fitting model(log likelihood ratio?319.3029,AIC?666.6059).The results from the SLM analysis indicated that DEM,mean temperature,and mean wind speed were the primary factors influencing the occurrence of scrub typhus.For the local spatial regression models,the multiscale geographically weighted regression was determined to be the best fitting model(adjusted R2?0.443,AICc?726.489).Further analysis using the MGWR model revealed that DEM had a greater impact in Xinfeng and Longnan,while the southern region was found to be more susceptible to scrub typhus due to mean wind speed.The geographical detector results revealed that the incidence of scrub typhus was primarily influenced by annual average normalized difference vegetation index.Additionally,the interaction between GDP and the percentage of grassland area had a significant impact on the incidence of scrub typhus(q?0.357).This study illustrated the individual and interactive effects of natural environmental factors and socio-economic factors on the incidence of scrub typhus;and elucidated the specific factors affecting the incidence of scrub typhus in various streets/townships.The findings of this study can be used to develop effective interventions for the prevention and control of scrub typhus.展开更多
This study evaluates the distribution of COVID-19 cases and mass vaccination campaigns from January 2020 to April 2023. There are over 235,000 COVID-19 cases and over 733,000 vaccinations across the 159 counties in th...This study evaluates the distribution of COVID-19 cases and mass vaccination campaigns from January 2020 to April 2023. There are over 235,000 COVID-19 cases and over 733,000 vaccinations across the 159 counties in the state of Georgia. Data on COVID-19 was acquired from usafact.org while the vaccination records were obtained from COVID-19 vaccination tracker. The spatial patterns across the counties were analyzed using spatial statistical techniques which include both global and local spatial autocorrelation. The study further evaluates the effect of vaccination and selected socio-economic predictors on COVID-19 cases across the study area. The result of hotspot analysis reveals that the epicenters of COVID-19 are distributed across Cobb, Fulton, Gwinnett, and DeKalb counties. It was also affirmed that the vaccination records followed the same pattern as COVID-19 cases’ epicenters. The result of the spatial error model performed well and accounted for a considerable percentage of the regression with an adjusted R squared of 0.68, Akaike Information Criterion (AIC) 387.682 and Breusch-Pagan of 9.8091. ESDA was employed to select the main explanatory variables. The selected variables include vaccination, population density, percentage of people that do not have health insurance, black race, Hispanic and these variables accounted for 68% of the number of COVID-19 cases in the state of Georgia during the study period. The study concludes that both COVID-19 cases and vaccinated individuals have spatial peculiarities across counties in Georgia state. Lastly, socio-economic variables and vaccination are very important to reduce the vulnerability of individuals to COVID-19 disease.展开更多
Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect ...Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R^2=0.55 vs.R^2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.展开更多
In the face of the unrelenting challenge posed by earthquakes-a natural hazard of unpredictable nature with a legacy of significant loss of life,destruction of infrastructure,and profound economic and social impacts-t...In the face of the unrelenting challenge posed by earthquakes-a natural hazard of unpredictable nature with a legacy of significant loss of life,destruction of infrastructure,and profound economic and social impacts-the scientific community has pursued advancements in earthquake early warning systems(EEWSs).These systems are vital for pre-emptive actions and decision-making that can save lives and safeguard critical infrastructure.This study proposes and validates a domain-informed deep learning-based EEWS called the hybrid earthquake early warning framework for estimating response spectra(HEWFERS),which represents a significant leap forward in the capabilities to predict ground shaking intensity in real-time,aligning with the United Nations’disaster risk reduction goals.HEWFERS ingeniously integrates a domain-informed variational autoencoder for physics-based latent variable(LV)extraction,a feed-forward neural network for on-site prediction,and Gaussian process regression for spatial prediction.Adopting explainable artificial intelligence-based Shapley explanations further elucidates the predictive mechanisms,ensuring stakeholder-informed decisions.By conducting an extensive analysis of the proposed framework under a large database of approximately 14000 recorded ground motions,this study offers insights into the potential of integrating machine learning with seismology to revolutionize earthquake preparedness and response,thus paving the way for a safer and more resilient future.展开更多
Based on energy consumption data of each region in China from 1997 to 2009 and using ArcGIS9.3 and GeoDA9.5 as technical support, this paper made a preliminary study on the changing trend of spatial pattern at regiona...Based on energy consumption data of each region in China from 1997 to 2009 and using ArcGIS9.3 and GeoDA9.5 as technical support, this paper made a preliminary study on the changing trend of spatial pattern at regional level of carbon emissions from energy consumption, spatial autocorrelation analysis of carbon emissions, spatial regression analysis between carbon emissions and their influencing factors. The analyzed results are shown as follows. （1） Carbon emissions from energy consumption increased more than 148% from 1997 to 2009 but the spatial pattern of high and low emission regions did not change greatly. （2） The global spatial autocorrelation of carbon emissions from energy consumption in- creased from 1997 to 2009, the spatial autocorrelation analysis showed that there exists a ＂polarization＂ phenomenon, the centre of ＂High-High＂ agglomeration did not change greatly but expanded currently, the centre of ＂Low-Low＂ agglomeration also did not change greatly but narrowed currently. （3） The spatial regression analysis showed that carbon emissions from energy consumption has a close relationship with GDP and population, R-squared rate of the spatial regression between carbon emissions and GDP is higher than that between carbon emissions and population. The contribution of population to carbon emissions increased but the contribution of GDP decreased from 1997 to 2009. The carbon emissions spillover effect was aggravated from 1997 to 2009 due to both the increase of GDP and population, so GDP and population were the two main factors which had strengthened the spatial autocorrelation of carbon emissions.展开更多
Quantifying the aggregation patterns of urban population, economic activities, and land use are essential for understanding compact development, but little is known about the difference among the distribution characte...Quantifying the aggregation patterns of urban population, economic activities, and land use are essential for understanding compact development, but little is known about the difference among the distribution characteristics and how the built environment influences urban aggre-gation. In this study, five elements are collected in Wuhan, China, namely population density, floor area ratio, business POIs, road network and built-up area as the representative of urban population, economic activities and land use. An inverse S-shape function is employed to fit the elements’ macro distribution. An aggregation degree index is proposed to measure the aggregation level of urban elements. The kernel density estimation is used to identify the aggregation patterns. The spatial regression model is used to identify the built environment factors influencing the spatial distribution of urban elements. Results indicates that all urban elements decay outward from the city center in an inverse S-shape manner. The business Pointof- Interest (POI) density and population density are highly aggregated;floor area ratio and road density are moderately aggregated, whereas the built-up density is poorly aggregated. Three types of spatial aggregation patterns are identified: a point-shaped pattern, an axial pattern and a planar pattern. The spatial regression modeling shows that the built environment is associated with the distribution of the urban population, economic activities and land use. Destination accessibility factors, transit accessibility factors and land use diversity factors shape the distribution of the business POI density, floor area ratio and road density. Design factors are positively associated with population density, floor area ratio and built-up density. Future planning should consider the varying spatial concentration of urban population, economic activities and land use as well as their relationships with built environment attributes. Results of this study will provide a systematic understanding of aggregation of urban land use, popula-tion, and economic activities in megacities as well as some suggestions for planning and compact development.展开更多
Objective We aimed to investigate and interpret the associations between socioeconomic factors and the prevalence, awareness, treatment, and control of hypertension at the provincial level in China.Methods A nationall...Objective We aimed to investigate and interpret the associations between socioeconomic factors and the prevalence, awareness, treatment, and control of hypertension at the provincial level in China.Methods A nationally and provincially representative sample of 179,059 adults from the China Chronic Disease and Nutrition Surveillance study in 2015–2016 was used to estimate hypertension burden. The spatial Durbin error model was fitted to investigate socioeconomic factors associated with hypertension indicators.Results Overall, it was estimated that 29.20% of the participants were hypertensive nationwide,among whom, 34.32% were aware of their condition, 27.69% had received antihypertensive treatment,and 7.81% had controlled their condition. Per capita gross domestic product(GDP) was associated with hypertension prevalence(coefficient:-2.95, 95% CI:-5.46,-0.45) and control(coefficient: 6.35, 95% CI:1.36, 11.34) among adjacent provinces and was also associated with awareness(coefficient: 2.93, 95%CI: 1.12, 4.74) and treatment(coefficient: 2.67, 95% CI: 1.21, 4.14) in local province. Beds of internal medicine(coefficient: 2.66, 95% CI: 1.08, 4.23) was associated with control in local province. Old dependency ratio(coefficient:-3.58, 95% CI:-5.35,-1.81) was associated with treatment among adjacent provinces and with control(coefficient:-1.69, 95% CI:-2.42,-0.96) in local province.Conclusion Hypertension indicators were not only directly influenced by socioeconomic factors of local area but also indirectly affected by characteristics of geographical neighbors. Population-level strategies should involve optimizing supportive socioeconomic environment by integrating clinical care and public health services to decrease hypertension burden.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
Developing countries must consider the influence of anthropogenic dynamics on changes in rangeland habitats. This study explores happened degradation in 178 rangeland management plans for Northeast Iran in three main ...Developing countries must consider the influence of anthropogenic dynamics on changes in rangeland habitats. This study explores happened degradation in 178 rangeland management plans for Northeast Iran in three main steps:(1) conducting a trend analysis of rangeland degradation and anthropogenic dynamics in 1986–2000 and 2000–2015,(2) visualizing the effects of anthropogenic drivers on rangeland degradation using bivariate local spatial autocorrelation(BiLISA), and(3) quantifying spatial dependence between anthropogenic driving forces and rangeland degradation using spatial regression approaches. The results show that 0.77% and 0.56% of rangelands are degraded annually during the first and second periods. The BiLISA results indicate that dry-farming, irrigated farming and construction areas were significant drivers in both periods and grazing intensity was a significant driver in the second period. The spatial lag(SL) model(wi=0.3943, Ei=1.4139) with two drivers of dry-farming and irrigated farming in the first period and the spatial error(SE) model(wi=0.4853, Ei=1.515) with livestock density, dry-farming and irrigated farming in the second period showed robust performance in quantifying the driving forces of rangeland degradation. To conclude, the BiLISA maps and spatial models indicate a serious intensification of the anthropogenic impacts of ongoing conditions on the rangelands of northeast Iran in the future.展开更多
Understanding the urban-rural development mechanism is critical for implementing rural revival and new-type urbanization.However,it remains a challenge to quantify the urban-rural integrated development level(URIDL)an...Understanding the urban-rural development mechanism is critical for implementing rural revival and new-type urbanization.However,it remains a challenge to quantify the urban-rural integrated development level(URIDL)and its impact factors.Hence,we constructed an assessment system for the URIDL from spatial,economic,social,life,and ecological integration.The spatial autocorrelation and Spearman rank correlation coefficients were used to assess the spatiotemporal variation of the URIDL and the trade-off synergistic relationship among the subsystems at the provincial scale in China using socio-economic statistical data from 2000 to 2020.A spatial panel quantile regression model was used to analyze the driving mechanism.The results showed that the URIDL of China increased by 0.19%from 2000 to 2020,and a high-high(H-H)spatial agglomeration pattern occurred in the Yangtze River Delta and the Beijing-Tianjin-Hebei regions.Spatial integration significantly contributed to the other subsystems,whereas economic integration had a significant negative impact on the other subsystems in the eastern coastal and southwestern regions.Per capita Gross Domestic Product(GDP)improved the URIDL,whereas other factors,such as fiscal revenue decentralization,had inhibiting effects.Notably,the impact of factors on URIDL varies across different quantiles.Finally,we proposed policy recommendations for differentiated improvement of URIDL based on its evolution and regional development level during the research period.展开更多
Spatiotemporal pattern analysis provides a new dimension for data interpretation due to new trends in computer vision and big data analysis. The main aim of this study was to explore the recent advances in geospatial ...Spatiotemporal pattern analysis provides a new dimension for data interpretation due to new trends in computer vision and big data analysis. The main aim of this study was to explore the recent advances in geospatial technologies to examine the spatiotemporal pattern of COVID-19 at the Public Health Unit (PHU) level in Ontario, Canada. The spatial autocorrelation results showed that the incidence rate (no. of confirmed cases per 100,000 population–IR/100K) was clustered at the PHU level and found a tendency of clustering high values. Some PHUs in Southern Ontario were identified as hot spots, while Northern PHUs were cold spots. The space-time cube showed an overall trend with a 99% confidence level. Considerable spatial variability in incidence intensity at different times suggested that risk factors were unevenly distributed in space and time. The study also created a regression model that explains the correlation between IR/100K values and potential socioeconomic factors.展开更多
Soil carbon to nitrogen(C/N) ratio is one of the most important variables reflecting soil quality and ecological function,and an indicator for assessing carbon and nitrogen nutrition balance of soils.Its variation ref...Soil carbon to nitrogen(C/N) ratio is one of the most important variables reflecting soil quality and ecological function,and an indicator for assessing carbon and nitrogen nutrition balance of soils.Its variation reflects the carbon and nitrogen cycling of soils.In order to explore the spatial variability of soil C/N ratio and its controlling factors of the Ili River valley in Xinjiang Uygur Autonomous Region,Northwest China,the traditional statistical methods,including correlation analysis,geostatistic alanalys and multiple regression analysis were used.The statistical results showed that the soil C/N ratio varied from 7.00 to 23.11,with a mean value of 10.92,and the coefficient of variation was 31.3%.Correlation analysis showed that longitude,altitude,precipitation,soil water,organic carbon,and total nitrogen were positively correlated with the soil C/N ratio(P < 0.01),whereas negative correlations were found between the soil C/N ratio and latitude,temperature,soil bulk density and soil p H.Ordinary Cokriging interpolation showed that r and ME were 0.73 and 0.57,respectively,indicating that the prediction accuracy was high.The spatial autocorrelation of the soil C/N ratio was 6.4 km,and the nugget effect of the soil C/N ratio was 10% with a patchy distribution,in which the area with high value(12.00–20.41) accounted for 22.6% of the total area.Land uses changed the soil C/N ratio with the order of cultivated land > grass land > forest land > garden.Multiple regression analysis showed that geographical and climatic factors,and soil physical and chemical properties could independently explain 26.8%and 55.4% of the spatial features of soil C/N ratio,while human activities could independently explain 5.4% of the spatial features only.The spatial distribution of soil C/N ratio in the study has important reference value for managing soil carbon and nitrogen,and for improving ecological function to similar regions.展开更多
There are urgent calls for new approaches to map the global urban conditions of complexity,diffuseness,diversity,and connectivity.However,existing methods mostly focus on mapping urbanized areas as bio physical entiti...There are urgent calls for new approaches to map the global urban conditions of complexity,diffuseness,diversity,and connectivity.However,existing methods mostly focus on mapping urbanized areas as bio physical entities.Here,based on the continuum of urbanity framework,we developed an approach for cross-scale urbanity map-ping from town to city and urban megaregion with different spatial resolutions using the Google Earth Engine.This approach was developed based on multi-source remote sensing data,Points of Interest-Open Street Map(POIs-OSM)big data,and the random forest regression model.This approach is scale-independent and revealed significant spatial variations in urbanity,underscoring differences in urbanization patterns across megaregions and between urban and rural areas.Urbanity was observed transcending traditional urban boundaries,diffusing into rural settlements within non-urban locales.The finding of urbanity in rural communities far from urban areas challenges the gradient theory of urban-rural development and distribution.By mapping livelihoods,lifestyles,and connectivity simultaneously,urbanity maps present a more comprehensive characterization of the complex-ity,diffuseness,diversity,and connectivity of urbanized areas than that by land cover or population density alone.It helps enhance the understanding of urbanization beyond biophysical form.This approach can provide a multifaceted understanding of urbanization,and thereby insights on urban and regional sustainability.展开更多
The spatial relationships between traffic accessibility and supply and demand(S&D)of ecosystem services(ESs)are essential for the formulation of ecological compensation policies and ESs regulation.In this study,an...The spatial relationships between traffic accessibility and supply and demand(S&D)of ecosystem services(ESs)are essential for the formulation of ecological compensation policies and ESs regulation.In this study,an ESs matrix and coupling analysis method were used to assess ESs S&D based on land-use data for 2000,2010,and 2020,and spatial regression models were used to analyze the correlated impacts of traffic accessibility.The results showed that the ESs supply and balance index in the middle reaches of the Yangtze River urban agglomeration(MRYRUA)continuously decreased,while the demand index increased from 2000 to 2020.The Gini coefficients of these indices continued to increase but did not exceed the warning value(0.4).The coupling degree of ESs S&D continued to increase,and its spatial distribution patterns were similar to that of the ESs demand index,with significantly higher values in the plains than in the montane areas,contrasting with those of the ESs supply index.The results of global bivariate Moran’s I analysis showed a significant spatial dependence between traffic accessibility and the degree of coupling between ESs S&D;the spatial regression results showed that an increase in traffic accessibility promoted the coupling degree.The present results provide a new perspective on the relationship between traffic accessibility and the coupling degree of ESs S&D,representing a case study for similar future research in other regions,and a reference for policy creation based on the matching between ESs S&D in the MRYRUA.展开更多
Existing quantitative migration studies are mainly at the inter-region or inter-province level for lacking of detailed geo-referenced migration data.Meanwhile,few of them integrate explorative spatial data analysis an...Existing quantitative migration studies are mainly at the inter-region or inter-province level for lacking of detailed geo-referenced migration data.Meanwhile,few of them integrate explorative spatial data analysis and spatial regression model into migration analysis.Based on aggregated registered floating population data from 2005 to 2008,the phenomena that population floating to Yiwu City in Zhejiang Province is analyzed at the provincial and county levels.The spatial layout of Yiwu's pull forces is proved as a V-shaped pattern excluding Sichuan Province based on map visualization method.Using the migration ratio in 2007 as an explanatory variable,two models are compared using ordinary least square,spatial error model and spatial lag model methods for county-level data in Jiangxi and Anhui provinces.The model with migration stock provides an improved fitting over the model without migration stock according to the model fitting results.The floating population flocking into Yiwu City from Jiangxi is determined mostly by migration stock while the determinant factors are migration stock and distance to Yiwu City for Anhui.The distance-decay effect is true for migration flow from Anhui to Yiwu City while the distance rule is not confirmed in Jiangxi with the best fitting model.The correlation between per capita net income of rural labor forces and migration ratio is not significant in Jiangxi and significant but at the 0.1 level only in Anhui.Further analysis shows that the distance,income and man-land ratio are important factors to explain population floating at earlier stage.However,as the dynamic population floating process evolves,the determinant factor would be migration stock.展开更多
In recent years,the police intervention strategy“Hot spots policing”has been effective in combating crimes.However,as cities are under the intense pressure of increasing crime and scarce police resources,police patr...In recent years,the police intervention strategy“Hot spots policing”has been effective in combating crimes.However,as cities are under the intense pressure of increasing crime and scarce police resources,police patrols are expected to target more accurately at finer geographic units rather than ballpark“hot spot”areas.This study aims to develop an algorithm using geographic information to detect crime patterns at street level,the so-called“hot street”,to further assist the Criminal Investigation Department(CID)in capturing crime change and transitive moments efficiently.The algorithm applies Kernel Density Estimation(KDE)technique onto street networks,rather than traditional areal units,in one case study borough in London;it then maps the detected crime“hot streets”by crime type.It was found that the algorithm could successfully generate“hot street”maps for Law Enforcement Agencies(LEAs),enabling more effective allocation of police patrolling;and bear enough resilience itself for the Strategic Crime Analysis(SCA)team’s sustainable utilization,by either updating the inputs with latest data or modifying the model parameters(i.e.the kernel function,and the range of spillover).Moreover,this study explores contextual characteristics of crime“hot streets”by applying various regression models,in recognition of the best fitted Geographically Weighted Regression(GWR)model,encompassing eight significant contextual factors with their varied effects on crimes at different streets.Having discussed the impact of lockdown on crime rates,it was apparent that the land-use driven mobility change during lockdown was a fundamental reason for changes in crime.Overall,these research findings have provided evidence and practical suggestions for crime prevention to local governors and policy practitioners,through more optimal urban planning(e.g.Low Traffic Neighborhoods),proactive policing(e.g.in the listed top 10“Hot Streets”of crime),publicizing of laws and regulations,and installations of security infrastructures(e.g.CCTV cameras and traffic signals).展开更多
Quality of life(QOL) is a hotspot issue that has attracted increasing attention from the Chinese Government and scholars, it is also a vital issue that should be addressed during the cause of ′establishing overall we...Quality of life(QOL) is a hotspot issue that has attracted increasing attention from the Chinese Government and scholars, it is also a vital issue that should be addressed during the cause of ′establishing overall well-off society′. Northeast China is one of the most import old industrial bases in China, however, the industrial structure of heavy chemical industry and the development mode of ′production first, living last′ have leaded to series of social problems, which have also become a serious bottleneck to social stability and economic sustainable development. Through applying the methods of BP neural network, exploratory spatial data analysis(ESDA) and spatial regression model, this paper examines the space-time dynamics of QOL of the residents in Northeast China. We first investigate the indexes of QOL of the residents and then use ESDA methods to visualize its space-time relationship. We have found a spatial agglomeration of QOL of the residents in middle-southern Liaoning Province, central Jilin Province and Harbin-Qiqihar-Daqing area of Heilongjiang Province. Two third of the counties are low-low spatial correlation, and the correlative type of about 60% of the prefecture level areas keeps stable, indicating QOL of the residents in Northeast China shows a certain character of path dependence or spatial locked. We have also found that economic strength and development levels of service industry have positive and obvious effect on QOL of the residents, while the effect of such indexes as the social service level and the proportion of the tertiary industries are less.展开更多
Earthquakes pose significant risks globally,necessitating effective seismic risk mitigation strategies like earthquake early warning(EEW)systems.However,developing and optimizing such systems requires thoroughly under...Earthquakes pose significant risks globally,necessitating effective seismic risk mitigation strategies like earthquake early warning(EEW)systems.However,developing and optimizing such systems requires thoroughly understanding their internal procedures and coverage limitations.This study examines a deep-learning-based on-site EEW framework known as ROSERS(Real-time On-Site Estimation of Response Spectra)proposed by the authors,which constructs response spectra from early recorded ground motion waveforms at a target site.This study has three primary goals:(1)evaluating the effectiveness and applicability of ROSERS to subduction seismic sources;(2)providing a detailed interpretation of the trained deep neural network(DNN)and surrogate latent variables(LVs)implemented in ROSERS;and(3)analyzing the spatial efficacy of the framework to assess the coverage area of on-site EEW stations.ROSERS is retrained and tested on a dataset of around 11,000 unprocessed Japanese subduction ground motions.Goodness-of-fit testing shows that the ROSERS framework achieves good performance on this database,especially given the peculiarities of the subduction seismic environment.The trained DNN and LVs are then interpreted using game theory-based Shapley additive explanations to establish cause-effect relationships.Finally,the study explores the coverage area of ROSERS by training a novel spatial regression model that estimates the LVs using geographically weighted random forest and determining the radius of similarity.The results indicate that on-site predictions can be considered reliable within a 2–9 km radius,varying based on the magnitude and distance from the earthquake source.This information can assist end-users in strategically placing sensors,minimizing blind spots,and reducing errors from regional extrapolation.展开更多
Data collected on the surface of the earth often has spatial interaction. In this paper, a non-isotropic mixing spatial data process is introduced, and under such a spatial structure a nonparametric kernel method is s...Data collected on the surface of the earth often has spatial interaction. In this paper, a non-isotropic mixing spatial data process is introduced, and under such a spatial structure a nonparametric kernel method is suggested to estimate a spatial conditional regression. Under mild regularities, sufficient conditions are derived to ensure the weak consistency as well as the convergence rates for the kernel estimator. Of interest are the following: (1) All the conditions imposed on the mixing coefficient and the bandwidth are simple; (2) Differently from the time series setting, the bandwidth is found to be dependent on the dimension of the site in space as well; (3) For weak consistency, the mixing coefficient is allowed to be unsummable and the tendency of sample size to infinity may be in different manners along different direction in space; (4) However, to have an optimal convergence rate, faster decreasing rates of mixing coefficient and the tendency of sample size to infinity along each direction are required.展开更多
Background: Although visceral leishmaniasis(VL),a disease caused by parasites,is controlled in most provinces in China,it is still a serious public health problem and remains fundamentally uncontrolled in some northwe...Background: Although visceral leishmaniasis(VL),a disease caused by parasites,is controlled in most provinces in China,it is still a serious public health problem and remains fundamentally uncontrolled in some northwest provinces and autonomous regions.The objective of this study is to explore the spatial and temporal characteristics of VL in Sichuan Province,Gansu Province and Xinjiang Uygur Autonomous Region in China from 2004 to 2018 and to identify the risk areas for VL transmission.Methods:: Spatiotemporal models were applied to explore the spatio-temporal distribution characteristics of VL and the association between VL and meteorological factors in western China from 2004 to 2018.Geographic information of patients from the National Diseases Reporting Information System operated by the Chinese Center for Disease Control and Prevention was defined according to the address code from the surveillance data.Results: During our study period,nearly 90%of cases occurred in some counties in three western regions(Sichuan Province,Gansu Province and Xinjiang Uygur Autonomous Region),and a significant spatial clustering pattern was observed.With our spatiotemporal model,the transmission risk,autoregressive risk and epidemic risk of these counties during our study period were also well predicted.The number of VL cases in three regions of western China concentrated on a few of counties.VL in Kashi Prefecture,Xinjiang Uygur Autonomous Region is still serious prevalent,and integrated control measures must be taken in different endemic areas.Conclusions: The number of VL cases in three regions of western China concentrated on a few of counties.VL in Kashi Prefecture,Xinjiang Uygur Autonomous Region is still serious prevalent,and integrated control measures must be taken in different endemic areas.Our findings will strengthen the VL control programme in China.展开更多
基金provided by the Science and Technology Program of Jiangxi Provincial Health andWellness Commission(Grant No.SKJP220226866).
文摘Scrub typhus is a significant public health issue with a wide distribution and is influenced by various determinants.However,in order to effectively eradicate scrub typhus,it is crucial to identify the specific factors that contribute to its incidence at a detailed level.Therefore,the objective of our study is to identify these influencing factors,examine the spatial variations in incidence,and analyze the interplay of two factors on scrub typhus incidence,so as to provide valuable experience for the prevention and treatment of scrub typhus in Gannan and to alleviate the economic burden of the local population.This study employed spatial autocorrelation analyses to examine the dependent variable and ordinary least squares model residuals.Additionally,spatial regression modelling and geographical detector were used to analyze the factors influencing the annual mean 14-year incidence of scrub typhus in the streets/townships of Gannan region from 2008 to 2021.The results of spatial1 autocorrelation analyses indicated the presence of spatial correlation.Among the global spatial regression models,the spatial lag model was found to be the best fitting model(log likelihood ratio?319.3029,AIC?666.6059).The results from the SLM analysis indicated that DEM,mean temperature,and mean wind speed were the primary factors influencing the occurrence of scrub typhus.For the local spatial regression models,the multiscale geographically weighted regression was determined to be the best fitting model(adjusted R2?0.443,AICc?726.489).Further analysis using the MGWR model revealed that DEM had a greater impact in Xinfeng and Longnan,while the southern region was found to be more susceptible to scrub typhus due to mean wind speed.The geographical detector results revealed that the incidence of scrub typhus was primarily influenced by annual average normalized difference vegetation index.Additionally,the interaction between GDP and the percentage of grassland area had a significant impact on the incidence of scrub typhus(q?0.357).This study illustrated the individual and interactive effects of natural environmental factors and socio-economic factors on the incidence of scrub typhus;and elucidated the specific factors affecting the incidence of scrub typhus in various streets/townships.The findings of this study can be used to develop effective interventions for the prevention and control of scrub typhus.
文摘This study evaluates the distribution of COVID-19 cases and mass vaccination campaigns from January 2020 to April 2023. There are over 235,000 COVID-19 cases and over 733,000 vaccinations across the 159 counties in the state of Georgia. Data on COVID-19 was acquired from usafact.org while the vaccination records were obtained from COVID-19 vaccination tracker. The spatial patterns across the counties were analyzed using spatial statistical techniques which include both global and local spatial autocorrelation. The study further evaluates the effect of vaccination and selected socio-economic predictors on COVID-19 cases across the study area. The result of hotspot analysis reveals that the epicenters of COVID-19 are distributed across Cobb, Fulton, Gwinnett, and DeKalb counties. It was also affirmed that the vaccination records followed the same pattern as COVID-19 cases’ epicenters. The result of the spatial error model performed well and accounted for a considerable percentage of the regression with an adjusted R squared of 0.68, Akaike Information Criterion (AIC) 387.682 and Breusch-Pagan of 9.8091. ESDA was employed to select the main explanatory variables. The selected variables include vaccination, population density, percentage of people that do not have health insurance, black race, Hispanic and these variables accounted for 68% of the number of COVID-19 cases in the state of Georgia during the study period. The study concludes that both COVID-19 cases and vaccinated individuals have spatial peculiarities across counties in Georgia state. Lastly, socio-economic variables and vaccination are very important to reduce the vulnerability of individuals to COVID-19 disease.
基金supported by Projects of International Cooperation and Exchanges NSFC (grant: 41361140361)the Special fund project of Chinese Academy of Sciences (grant: Y371164001)the key deployment project of Chinese Academy of Sciences (Grant No. KZZD-EW-12-2, KZZD-EW12-3)
文摘Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R^2=0.55 vs.R^2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.
基金the financial support from the Chilean National Research and Development Agency(Agencia Nacional de Investigación y Desarrollo,ANID)through Fondo Nacional de Desarrollo Científico y Tecnológico(FONDECYT)Regular 1240503Fondo de Valorización de la Investigación(FOVI)230030 projectsthe financial support from the ANID through FONDECYT Reg-ular 1240501.
文摘In the face of the unrelenting challenge posed by earthquakes-a natural hazard of unpredictable nature with a legacy of significant loss of life,destruction of infrastructure,and profound economic and social impacts-the scientific community has pursued advancements in earthquake early warning systems(EEWSs).These systems are vital for pre-emptive actions and decision-making that can save lives and safeguard critical infrastructure.This study proposes and validates a domain-informed deep learning-based EEWS called the hybrid earthquake early warning framework for estimating response spectra(HEWFERS),which represents a significant leap forward in the capabilities to predict ground shaking intensity in real-time,aligning with the United Nations’disaster risk reduction goals.HEWFERS ingeniously integrates a domain-informed variational autoencoder for physics-based latent variable(LV)extraction,a feed-forward neural network for on-site prediction,and Gaussian process regression for spatial prediction.Adopting explainable artificial intelligence-based Shapley explanations further elucidates the predictive mechanisms,ensuring stakeholder-informed decisions.By conducting an extensive analysis of the proposed framework under a large database of approximately 14000 recorded ground motions,this study offers insights into the potential of integrating machine learning with seismology to revolutionize earthquake preparedness and response,thus paving the way for a safer and more resilient future.
基金Foundation： National Social Science Foundation of China, No.10ZD＆M030 Non-profit Industry Financial Program of Ministry of Land and Resources of China, No.200811033+2 种基金 A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions National Natural Science Foundation of China, No.40801063 No.40971104
文摘Based on energy consumption data of each region in China from 1997 to 2009 and using ArcGIS9.3 and GeoDA9.5 as technical support, this paper made a preliminary study on the changing trend of spatial pattern at regional level of carbon emissions from energy consumption, spatial autocorrelation analysis of carbon emissions, spatial regression analysis between carbon emissions and their influencing factors. The analyzed results are shown as follows. （1） Carbon emissions from energy consumption increased more than 148% from 1997 to 2009 but the spatial pattern of high and low emission regions did not change greatly. （2） The global spatial autocorrelation of carbon emissions from energy consumption in- creased from 1997 to 2009, the spatial autocorrelation analysis showed that there exists a ＂polarization＂ phenomenon, the centre of ＂High-High＂ agglomeration did not change greatly but expanded currently, the centre of ＂Low-Low＂ agglomeration also did not change greatly but narrowed currently. （3） The spatial regression analysis showed that carbon emissions from energy consumption has a close relationship with GDP and population, R-squared rate of the spatial regression between carbon emissions and GDP is higher than that between carbon emissions and population. The contribution of population to carbon emissions increased but the contribution of GDP decreased from 1997 to 2009. The carbon emissions spillover effect was aggravated from 1997 to 2009 due to both the increase of GDP and population, so GDP and population were the two main factors which had strengthened the spatial autocorrelation of carbon emissions.
基金The research was funded by the National Natural Science Foundation of China(grant number 41971368).
文摘Quantifying the aggregation patterns of urban population, economic activities, and land use are essential for understanding compact development, but little is known about the difference among the distribution characteristics and how the built environment influences urban aggre-gation. In this study, five elements are collected in Wuhan, China, namely population density, floor area ratio, business POIs, road network and built-up area as the representative of urban population, economic activities and land use. An inverse S-shape function is employed to fit the elements’ macro distribution. An aggregation degree index is proposed to measure the aggregation level of urban elements. The kernel density estimation is used to identify the aggregation patterns. The spatial regression model is used to identify the built environment factors influencing the spatial distribution of urban elements. Results indicates that all urban elements decay outward from the city center in an inverse S-shape manner. The business Pointof- Interest (POI) density and population density are highly aggregated;floor area ratio and road density are moderately aggregated, whereas the built-up density is poorly aggregated. Three types of spatial aggregation patterns are identified: a point-shaped pattern, an axial pattern and a planar pattern. The spatial regression modeling shows that the built environment is associated with the distribution of the urban population, economic activities and land use. Destination accessibility factors, transit accessibility factors and land use diversity factors shape the distribution of the business POI density, floor area ratio and road density. Design factors are positively associated with population density, floor area ratio and built-up density. Future planning should consider the varying spatial concentration of urban population, economic activities and land use as well as their relationships with built environment attributes. Results of this study will provide a systematic understanding of aggregation of urban land use, popula-tion, and economic activities in megacities as well as some suggestions for planning and compact development.
基金supported by National Key Research&Development Program of Ministry of Science and Technology of People’s Republic of China[2018YFC1311703,2018YFC1311706]。
文摘Objective We aimed to investigate and interpret the associations between socioeconomic factors and the prevalence, awareness, treatment, and control of hypertension at the provincial level in China.Methods A nationally and provincially representative sample of 179,059 adults from the China Chronic Disease and Nutrition Surveillance study in 2015–2016 was used to estimate hypertension burden. The spatial Durbin error model was fitted to investigate socioeconomic factors associated with hypertension indicators.Results Overall, it was estimated that 29.20% of the participants were hypertensive nationwide,among whom, 34.32% were aware of their condition, 27.69% had received antihypertensive treatment,and 7.81% had controlled their condition. Per capita gross domestic product(GDP) was associated with hypertension prevalence(coefficient:-2.95, 95% CI:-5.46,-0.45) and control(coefficient: 6.35, 95% CI:1.36, 11.34) among adjacent provinces and was also associated with awareness(coefficient: 2.93, 95%CI: 1.12, 4.74) and treatment(coefficient: 2.67, 95% CI: 1.21, 4.14) in local province. Beds of internal medicine(coefficient: 2.66, 95% CI: 1.08, 4.23) was associated with control in local province. Old dependency ratio(coefficient:-3.58, 95% CI:-5.35,-1.81) was associated with treatment among adjacent provinces and with control(coefficient:-1.69, 95% CI:-2.42,-0.96) in local province.Conclusion Hypertension indicators were not only directly influenced by socioeconomic factors of local area but also indirectly affected by characteristics of geographical neighbors. Population-level strategies should involve optimizing supportive socioeconomic environment by integrating clinical care and public health services to decrease hypertension burden.
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.
文摘Developing countries must consider the influence of anthropogenic dynamics on changes in rangeland habitats. This study explores happened degradation in 178 rangeland management plans for Northeast Iran in three main steps:(1) conducting a trend analysis of rangeland degradation and anthropogenic dynamics in 1986–2000 and 2000–2015,(2) visualizing the effects of anthropogenic drivers on rangeland degradation using bivariate local spatial autocorrelation(BiLISA), and(3) quantifying spatial dependence between anthropogenic driving forces and rangeland degradation using spatial regression approaches. The results show that 0.77% and 0.56% of rangelands are degraded annually during the first and second periods. The BiLISA results indicate that dry-farming, irrigated farming and construction areas were significant drivers in both periods and grazing intensity was a significant driver in the second period. The spatial lag(SL) model(wi=0.3943, Ei=1.4139) with two drivers of dry-farming and irrigated farming in the first period and the spatial error(SE) model(wi=0.4853, Ei=1.515) with livestock density, dry-farming and irrigated farming in the second period showed robust performance in quantifying the driving forces of rangeland degradation. To conclude, the BiLISA maps and spatial models indicate a serious intensification of the anthropogenic impacts of ongoing conditions on the rangelands of northeast Iran in the future.
基金Under the auspices of National Key Research and Development Program(No.2023YFC3804001)National Natural Science Foundation of China(No.42201440)。
文摘Understanding the urban-rural development mechanism is critical for implementing rural revival and new-type urbanization.However,it remains a challenge to quantify the urban-rural integrated development level(URIDL)and its impact factors.Hence,we constructed an assessment system for the URIDL from spatial,economic,social,life,and ecological integration.The spatial autocorrelation and Spearman rank correlation coefficients were used to assess the spatiotemporal variation of the URIDL and the trade-off synergistic relationship among the subsystems at the provincial scale in China using socio-economic statistical data from 2000 to 2020.A spatial panel quantile regression model was used to analyze the driving mechanism.The results showed that the URIDL of China increased by 0.19%from 2000 to 2020,and a high-high(H-H)spatial agglomeration pattern occurred in the Yangtze River Delta and the Beijing-Tianjin-Hebei regions.Spatial integration significantly contributed to the other subsystems,whereas economic integration had a significant negative impact on the other subsystems in the eastern coastal and southwestern regions.Per capita Gross Domestic Product(GDP)improved the URIDL,whereas other factors,such as fiscal revenue decentralization,had inhibiting effects.Notably,the impact of factors on URIDL varies across different quantiles.Finally,we proposed policy recommendations for differentiated improvement of URIDL based on its evolution and regional development level during the research period.
文摘Spatiotemporal pattern analysis provides a new dimension for data interpretation due to new trends in computer vision and big data analysis. The main aim of this study was to explore the recent advances in geospatial technologies to examine the spatiotemporal pattern of COVID-19 at the Public Health Unit (PHU) level in Ontario, Canada. The spatial autocorrelation results showed that the incidence rate (no. of confirmed cases per 100,000 population–IR/100K) was clustered at the PHU level and found a tendency of clustering high values. Some PHUs in Southern Ontario were identified as hot spots, while Northern PHUs were cold spots. The space-time cube showed an overall trend with a 99% confidence level. Considerable spatial variability in incidence intensity at different times suggested that risk factors were unevenly distributed in space and time. The study also created a regression model that explains the correlation between IR/100K values and potential socioeconomic factors.
基金Under the auspices of National Science and Technology Support Program of China(No.2014BAC15B03)the West Light Funds of Chinese Academy of Sciences(No.YB201302)
文摘Soil carbon to nitrogen(C/N) ratio is one of the most important variables reflecting soil quality and ecological function,and an indicator for assessing carbon and nitrogen nutrition balance of soils.Its variation reflects the carbon and nitrogen cycling of soils.In order to explore the spatial variability of soil C/N ratio and its controlling factors of the Ili River valley in Xinjiang Uygur Autonomous Region,Northwest China,the traditional statistical methods,including correlation analysis,geostatistic alanalys and multiple regression analysis were used.The statistical results showed that the soil C/N ratio varied from 7.00 to 23.11,with a mean value of 10.92,and the coefficient of variation was 31.3%.Correlation analysis showed that longitude,altitude,precipitation,soil water,organic carbon,and total nitrogen were positively correlated with the soil C/N ratio(P < 0.01),whereas negative correlations were found between the soil C/N ratio and latitude,temperature,soil bulk density and soil p H.Ordinary Cokriging interpolation showed that r and ME were 0.73 and 0.57,respectively,indicating that the prediction accuracy was high.The spatial autocorrelation of the soil C/N ratio was 6.4 km,and the nugget effect of the soil C/N ratio was 10% with a patchy distribution,in which the area with high value(12.00–20.41) accounted for 22.6% of the total area.Land uses changed the soil C/N ratio with the order of cultivated land > grass land > forest land > garden.Multiple regression analysis showed that geographical and climatic factors,and soil physical and chemical properties could independently explain 26.8%and 55.4% of the spatial features of soil C/N ratio,while human activities could independently explain 5.4% of the spatial features only.The spatial distribution of soil C/N ratio in the study has important reference value for managing soil carbon and nitrogen,and for improving ecological function to similar regions.
基金support from the National Natural Science Fund for Distinguished Young Scholars(Grant No.42225104)the National Natural Science Foundation(Grant No.U21A2010).
文摘There are urgent calls for new approaches to map the global urban conditions of complexity,diffuseness,diversity,and connectivity.However,existing methods mostly focus on mapping urbanized areas as bio physical entities.Here,based on the continuum of urbanity framework,we developed an approach for cross-scale urbanity map-ping from town to city and urban megaregion with different spatial resolutions using the Google Earth Engine.This approach was developed based on multi-source remote sensing data,Points of Interest-Open Street Map(POIs-OSM)big data,and the random forest regression model.This approach is scale-independent and revealed significant spatial variations in urbanity,underscoring differences in urbanization patterns across megaregions and between urban and rural areas.Urbanity was observed transcending traditional urban boundaries,diffusing into rural settlements within non-urban locales.The finding of urbanity in rural communities far from urban areas challenges the gradient theory of urban-rural development and distribution.By mapping livelihoods,lifestyles,and connectivity simultaneously,urbanity maps present a more comprehensive characterization of the complex-ity,diffuseness,diversity,and connectivity of urbanized areas than that by land cover or population density alone.It helps enhance the understanding of urbanization beyond biophysical form.This approach can provide a multifaceted understanding of urbanization,and thereby insights on urban and regional sustainability.
基金National Natural Science Foundation of China,No.42001187,No.41701629。
文摘The spatial relationships between traffic accessibility and supply and demand(S&D)of ecosystem services(ESs)are essential for the formulation of ecological compensation policies and ESs regulation.In this study,an ESs matrix and coupling analysis method were used to assess ESs S&D based on land-use data for 2000,2010,and 2020,and spatial regression models were used to analyze the correlated impacts of traffic accessibility.The results showed that the ESs supply and balance index in the middle reaches of the Yangtze River urban agglomeration(MRYRUA)continuously decreased,while the demand index increased from 2000 to 2020.The Gini coefficients of these indices continued to increase but did not exceed the warning value(0.4).The coupling degree of ESs S&D continued to increase,and its spatial distribution patterns were similar to that of the ESs demand index,with significantly higher values in the plains than in the montane areas,contrasting with those of the ESs supply index.The results of global bivariate Moran’s I analysis showed a significant spatial dependence between traffic accessibility and the degree of coupling between ESs S&D;the spatial regression results showed that an increase in traffic accessibility promoted the coupling degree.The present results provide a new perspective on the relationship between traffic accessibility and the coupling degree of ESs S&D,representing a case study for similar future research in other regions,and a reference for policy creation based on the matching between ESs S&D in the MRYRUA.
基金Under the auspices of National Natural Science Foundation of China(No.41001314)Youth Science Funds of State Key Laboratory of Resources and Environmental Information System,Chinese Academy of Sciences(No.KA11040101)National Key Technology R&D Program of China(No.2012BAI32B07)
文摘Existing quantitative migration studies are mainly at the inter-region or inter-province level for lacking of detailed geo-referenced migration data.Meanwhile,few of them integrate explorative spatial data analysis and spatial regression model into migration analysis.Based on aggregated registered floating population data from 2005 to 2008,the phenomena that population floating to Yiwu City in Zhejiang Province is analyzed at the provincial and county levels.The spatial layout of Yiwu's pull forces is proved as a V-shaped pattern excluding Sichuan Province based on map visualization method.Using the migration ratio in 2007 as an explanatory variable,two models are compared using ordinary least square,spatial error model and spatial lag model methods for county-level data in Jiangxi and Anhui provinces.The model with migration stock provides an improved fitting over the model without migration stock according to the model fitting results.The floating population flocking into Yiwu City from Jiangxi is determined mostly by migration stock while the determinant factors are migration stock and distance to Yiwu City for Anhui.The distance-decay effect is true for migration flow from Anhui to Yiwu City while the distance rule is not confirmed in Jiangxi with the best fitting model.The correlation between per capita net income of rural labor forces and migration ratio is not significant in Jiangxi and significant but at the 0.1 level only in Anhui.Further analysis shows that the distance,income and man-land ratio are important factors to explain population floating at earlier stage.However,as the dynamic population floating process evolves,the determinant factor would be migration stock.
基金partly supported by King’s Global Engagement Partnership Fund[2020-2021#PF2021_Mar_005].
文摘In recent years,the police intervention strategy“Hot spots policing”has been effective in combating crimes.However,as cities are under the intense pressure of increasing crime and scarce police resources,police patrols are expected to target more accurately at finer geographic units rather than ballpark“hot spot”areas.This study aims to develop an algorithm using geographic information to detect crime patterns at street level,the so-called“hot street”,to further assist the Criminal Investigation Department(CID)in capturing crime change and transitive moments efficiently.The algorithm applies Kernel Density Estimation(KDE)technique onto street networks,rather than traditional areal units,in one case study borough in London;it then maps the detected crime“hot streets”by crime type.It was found that the algorithm could successfully generate“hot street”maps for Law Enforcement Agencies(LEAs),enabling more effective allocation of police patrolling;and bear enough resilience itself for the Strategic Crime Analysis(SCA)team’s sustainable utilization,by either updating the inputs with latest data or modifying the model parameters(i.e.the kernel function,and the range of spillover).Moreover,this study explores contextual characteristics of crime“hot streets”by applying various regression models,in recognition of the best fitted Geographically Weighted Regression(GWR)model,encompassing eight significant contextual factors with their varied effects on crimes at different streets.Having discussed the impact of lockdown on crime rates,it was apparent that the land-use driven mobility change during lockdown was a fundamental reason for changes in crime.Overall,these research findings have provided evidence and practical suggestions for crime prevention to local governors and policy practitioners,through more optimal urban planning(e.g.Low Traffic Neighborhoods),proactive policing(e.g.in the listed top 10“Hot Streets”of crime),publicizing of laws and regulations,and installations of security infrastructures(e.g.CCTV cameras and traffic signals).
基金Under the auspices of Key Research Program of Chinese Academic of Science(No.KZZD-EW-06-03,KSZD-EW-Z-021-03)Advantage Discipline Project of Hainan Normal University(No.305010048)+2 种基金Key Discipline Project of Hainan(No.3050107048)National Natural Science Foundation of China(No.41201160,41329001)Natural Science Foundation of Hainan Province(No.414189)
文摘Quality of life(QOL) is a hotspot issue that has attracted increasing attention from the Chinese Government and scholars, it is also a vital issue that should be addressed during the cause of ′establishing overall well-off society′. Northeast China is one of the most import old industrial bases in China, however, the industrial structure of heavy chemical industry and the development mode of ′production first, living last′ have leaded to series of social problems, which have also become a serious bottleneck to social stability and economic sustainable development. Through applying the methods of BP neural network, exploratory spatial data analysis(ESDA) and spatial regression model, this paper examines the space-time dynamics of QOL of the residents in Northeast China. We first investigate the indexes of QOL of the residents and then use ESDA methods to visualize its space-time relationship. We have found a spatial agglomeration of QOL of the residents in middle-southern Liaoning Province, central Jilin Province and Harbin-Qiqihar-Daqing area of Heilongjiang Province. Two third of the counties are low-low spatial correlation, and the correlative type of about 60% of the prefecture level areas keeps stable, indicating QOL of the residents in Northeast China shows a certain character of path dependence or spatial locked. We have also found that economic strength and development levels of service industry have positive and obvious effect on QOL of the residents, while the effect of such indexes as the social service level and the proportion of the tertiary industries are less.
文摘Earthquakes pose significant risks globally,necessitating effective seismic risk mitigation strategies like earthquake early warning(EEW)systems.However,developing and optimizing such systems requires thoroughly understanding their internal procedures and coverage limitations.This study examines a deep-learning-based on-site EEW framework known as ROSERS(Real-time On-Site Estimation of Response Spectra)proposed by the authors,which constructs response spectra from early recorded ground motion waveforms at a target site.This study has three primary goals:(1)evaluating the effectiveness and applicability of ROSERS to subduction seismic sources;(2)providing a detailed interpretation of the trained deep neural network(DNN)and surrogate latent variables(LVs)implemented in ROSERS;and(3)analyzing the spatial efficacy of the framework to assess the coverage area of on-site EEW stations.ROSERS is retrained and tested on a dataset of around 11,000 unprocessed Japanese subduction ground motions.Goodness-of-fit testing shows that the ROSERS framework achieves good performance on this database,especially given the peculiarities of the subduction seismic environment.The trained DNN and LVs are then interpreted using game theory-based Shapley additive explanations to establish cause-effect relationships.Finally,the study explores the coverage area of ROSERS by training a novel spatial regression model that estimates the LVs using geographically weighted random forest and determining the radius of similarity.The results indicate that on-site predictions can be considered reliable within a 2–9 km radius,varying based on the magnitude and distance from the earthquake source.This information can assist end-users in strategically placing sensors,minimizing blind spots,and reducing errors from regional extrapolation.
基金the National Natural Science Foundation of China (198010:38)National 863 Project.
文摘Data collected on the surface of the earth often has spatial interaction. In this paper, a non-isotropic mixing spatial data process is introduced, and under such a spatial structure a nonparametric kernel method is suggested to estimate a spatial conditional regression. Under mild regularities, sufficient conditions are derived to ensure the weak consistency as well as the convergence rates for the kernel estimator. Of interest are the following: (1) All the conditions imposed on the mixing coefficient and the bandwidth are simple; (2) Differently from the time series setting, the bandwidth is found to be dependent on the dimension of the site in space as well; (3) For weak consistency, the mixing coefficient is allowed to be unsummable and the tendency of sample size to infinity may be in different manners along different direction in space; (4) However, to have an optimal convergence rate, faster decreasing rates of mixing coefficient and the tendency of sample size to infinity along each direction are required.
基金This work was financially supported by grants from the China Mega-Project on Infectious Disease Prevention(No.2018ZX10713001).
文摘Background: Although visceral leishmaniasis(VL),a disease caused by parasites,is controlled in most provinces in China,it is still a serious public health problem and remains fundamentally uncontrolled in some northwest provinces and autonomous regions.The objective of this study is to explore the spatial and temporal characteristics of VL in Sichuan Province,Gansu Province and Xinjiang Uygur Autonomous Region in China from 2004 to 2018 and to identify the risk areas for VL transmission.Methods:: Spatiotemporal models were applied to explore the spatio-temporal distribution characteristics of VL and the association between VL and meteorological factors in western China from 2004 to 2018.Geographic information of patients from the National Diseases Reporting Information System operated by the Chinese Center for Disease Control and Prevention was defined according to the address code from the surveillance data.Results: During our study period,nearly 90%of cases occurred in some counties in three western regions(Sichuan Province,Gansu Province and Xinjiang Uygur Autonomous Region),and a significant spatial clustering pattern was observed.With our spatiotemporal model,the transmission risk,autoregressive risk and epidemic risk of these counties during our study period were also well predicted.The number of VL cases in three regions of western China concentrated on a few of counties.VL in Kashi Prefecture,Xinjiang Uygur Autonomous Region is still serious prevalent,and integrated control measures must be taken in different endemic areas.Conclusions: The number of VL cases in three regions of western China concentrated on a few of counties.VL in Kashi Prefecture,Xinjiang Uygur Autonomous Region is still serious prevalent,and integrated control measures must be taken in different endemic areas.Our findings will strengthen the VL control programme in China.
