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Spatial Modeling of COVID-19 Occurrence and Vaccination Rate across Counties in Ohio State from Jan. 2020 to April 2023 认领 引用
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作者 Olawale Oluwafemi Oluwaseun Ibukun +3 位作者 Yaw Kwarteng Kehinde Adebowale Yahaya Danjuma Samson Mela 《Journal of Geographic Information System》 2025年第1期80-96,共17页
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
关键词 COVID-19 Prevalence COVID-19 Vaccination Ohio Spatial Lag Model Spatial Error Model
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Spatial modeling issues in future smart cities 认领 引用 被引量：2
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作者 Gerhard SCHMITT 《Geo-Spatial Information Science》 EI 2013年第1期7-12,共6页
It is our goal to make today’s and future cities smart,sustainable and resilient.In order to achieve this,it is fundamental to understand how each city works,to formalize the knowledge gained and to apply it to a cit... It is our goal to make today’s and future cities smart,sustainable and resilient.In order to achieve this,it is fundamental to understand how each city works,to formalize the knowledge gained and to apply it to a city model as the base for simulations that can generate future scenarios with a high level of probability.The nature of this model,which must cover design,qualitative and quantitative aspects,has changed over time.In this study,we focus on the role of the spatial dimension and of geometry in a city model.Emerging from being a dominating generative force in ancient cities,spatial modeling has developed into an underlying description language for present and future cities to define functions and properties of the city in space and time.The example of the stocks and flows model applied to the city depicts where and how spatial modeling influences the design,construction and performance of the future Smart City. 展开更多
关键词 future cities spatial modeling stocks and flows city models reality-based 3D models
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Spatial modeling of solar photovoltaic power plant in Kabul,Afghanistan 认领 引用 被引量：2
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作者 NASERI Mohammad HUSSAINI Mohammad Salem +2 位作者 IQBAL Mohammad Wasim JAWADI Hussain Ali PUYA Marzia 《Journal of Mountain Science》 SCIE CSCD 2021年第12期3291-3305,共15页
Energy planning and solar plant site selections are vital strategic decisions and one of the most complex executive challenges in the interconnected procedures.It is essential to study the potential renewable energy s... Energy planning and solar plant site selections are vital strategic decisions and one of the most complex executive challenges in the interconnected procedures.It is essential to study the potential renewable energy sources in Afghanistan to select the most sustainable sites for solar power production in populated cities.This study is based on the combination of a Geographic Information System,Remote sensing,and multi-criteria decision-making technique to evaluate the optimal placement of photovoltaic solar power plants in the Kabul province,capital of Afghanistan.Two models,Analytical Hierarchy Process(AHP)and Analytical Network Process(ANP),were used to select suitable areas for establishing a solar power plant.The application of the proposed model has been made possible by integrating four constraints such as climate,environmental,topography,and economical which comprised twelve criteria:solar radiation,yearly average rainfall,land slope,aspect,land use,dust,geology and proximity to faults,main roads,Normalized difference vegetation index,urban areas river and water bodies.The findings indicate that there is no considerable difference between the results of both models since both models identified more than 20%of the total area of Kabul province in suitable classes.Outputs maps conclude that northern and southern parts of Kabul city and the eastern part of Kabul province came to the range of suitable areas.It can be concluded that Kabul province is a source of sufficient potential for producing solar electricity.The results of this study can support the plans of the Afghanistan government in solar energy production and the implementation of photovoltaic power plants. 展开更多
关键词 Photovoltaic Spatial modeling AHP ANP Kabul GIS
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Dynamic modeling of spatial variable stator vane mechanism using modified Lagrange multiplier method 认领 引用 被引量：1
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作者 Ke HE Kaiyi HUANG +2 位作者 Zhen LI Shuhui HU Zhinan ZHANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第6期251-271,共21页
The Variable Stator Vanes(VSV)system ensures the smooth operation of the highpressure compressor by adjusting the vane angles to prevent surge,and the dynamic behavior of its multistage vanes directly affects its serv... The Variable Stator Vanes(VSV)system ensures the smooth operation of the highpressure compressor by adjusting the vane angles to prevent surge,and the dynamic behavior of its multistage vanes directly affects its service performance.To investigate the dynamic behavior of the spatial VSV multi-vane mechanism,a positional constraint equation for the VSV mechanism was established,and the numerical expressions of the Jacobian matrices for different kinematic pair constraint equations were derived.The Lagrange multiplier method was modified for spatial rotation,and an ideal dynamic model of the spatial VSV multi-vane mechanism was developed.The computational results indicate that the dynamic behavior of different vanes within the same stage is similar,and the constraint moment experienced by vanes at different positions has a linear relationship with their centroid coordinates.This study expands the dynamic modeling methods for spatial mechanisms and provides a foundation for researching the frictional dynamic behavior of VSV mechanisms with clearance. 展开更多
关键词 Dynamic characteristics Dynamic models Lagrange multipliers Spatial dynamic modeling:VSV
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Integrated spatial generalized additive modeling for forest fire prediction:a case study in Fujian Province,China 认领 引用 被引量：1
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作者 Chunhui Li Zhangwen Su +4 位作者 Rongyu Ni Guangyu Wang Yiyun Ouyang Aicong Zeng Futao Guo 《Journal of Forestry Research》 SCIE EI CAS CSCD 2025年第3期208-223,共16页
The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environmen... The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environment.This study aims to build a prediction model to understand the spatial characteristics and piecewise effects of forest fire drivers.Using monthly grid data from 2006 to 2020,a modeling study analyzed fire occurrences during the September to April fire season in Fujian Province,China.We compared the fitting performance of the logistic regression model(LRM),the generalized additive logistic model(GALM),and the spatial generalized additive logistic model(SGALM).The results indicate that SGALMs had the best fitting results and the highest prediction accuracy.Meteorological factors significantly impacted forest fires in Fujian Province.Areas with high fire incidence were mainly concentrated in the northwest and southeast.SGALMs improved the fitting effect of fire prediction models by considering spatial effects and the flexible fitting ability of nonlinear interpretation.This model provides piecewise interpretations of forest wildfire occurrences,which can be valuable for relevant departments and will assist forest managers in refining prevention measures based on temporal and spatial differences. 展开更多
关键词 Forest fire prediction Logistic regression Spatial generalized additive model Spline functions Piecewise effects
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Probabilistic seismic hazard analysis for the northern segment of the North-South Seismic Belt in China based on improved spatial smoothing and fault source model integration 认领 引用
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作者 Yaohu Zhang Hua Pan +1 位作者 Meng Zhang Ying Shi 《Earthquake Science》 CAS CSCD 2026年第1期1-31,共31页
The northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic ... The northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic Seismic Hazard Analysis(PSHA)for this region is of significant importance for supporting seismic fortification in major engineering projects and formulating disaster prevention and mitigation policies.In this study,a composite seismic source model was constructed by integrating data on historical earthquakes,active faults,and paleoseismicity.Furthermore,a logic tree framework was employed to quantify epistemic uncertainties,enabling a systematic seismic hazard assessment of the region.To more accurately characterize the spatial heterogeneity of seismic activity,improvements were made to both the Circular Spatial Smoothing Model(CSSM)with a fixed radius and the Adaptive Spatial Smoothing Model(ASSM),with full consideration given to the spatiotemporal completeness of historical earthquake magnitudes.Regarding the CSSM,for scenarios involving small sample sizes in earthquake catalogs,the cross-validation method proposed in this study demonstrated higher robustness than the maximum likelihood method in determining the optimal correlation distance.Performance evaluation results indicate that while both models effectively characterize seismic activity,the ASSM exhibits superior overall predictive performance compared to the CSSM,owing to its ability to adaptively adjust the smoothing radius according to seismic density.Significant discrepancies were observed in the Peak Ground Acceleration(PGA)results calculated with a 10%probability of exceedance in 50 years across different combinations of seismic source models.The single spatially smoothed point-source model yielded a maximum PGA of approximately 0.52 g,with high-value areas concentrated near historical epicenters,thereby significantly underestimating the hazard associated with major fault zones.When combined with the simple fault-source model,the maximum PGA increased to 0.8 g,with high-value zones exhibiting a zonal distribution along faults;however,the risk remained underestimated for faults with low slip rates that are nevertheless approaching their recurrence cycles.Following the introduction of the time-dependent characteristic fault-source model,local PGA values for faults in the middle-to-late stages of their recurrence cycles increased by a factor of 2 to 7 compared to the single model.These results demonstrate that the characteristic fault-source model reasonably delineates the time-dependence of large earthquake recurrence,thereby providing a more accurate assessment of imminent seismic risks.By comprehensively applying the improved spatially smoothed pointsource model,the simple fault-source model,and the characteristic fault-source model,the following faults within the region were identified as having high seismic hazard:the Huangxianggou,Zhangxian,and Tianshui segments of the Xiqinling northern edge fault;the Maqin-Maqu segment of the Dongkunlun fault;the Longriqu fault;the Maoergai fault;the Elashan fault;the Riyueshan fault;the eastern segment of the Lenglongling fault;the Maxianshan segment of the Maxianshan northern Margin fault;and the Maomaoshan-Jinqianghe segment of the Laohushan-Maomaoshan fault.As these faults are located within seismic gaps or are approaching the recurrence periods of large earthquakes,they should be prioritized for current and future seismic monitoring as well as disaster prevention and mitigation efforts. 展开更多
关键词 northern segment of the North-South Seismic Belt fault-source characteristic earthquake spatial smoothing model
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The Interaction Mechanism Between Urban Scale Hierarchy and Urban Networks in China:An Analysis Based on A Spatial Simultaneous Equation Model 认领 引用
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作者 ZHOU Ying ZHENG Wensheng WANG Xiaofang 《Chinese Geographical Science》 SCIE CAS CSCD 2026年第1期19-33,共15页
Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefor... Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefore,this paper analyzes the spatial interaction between urban scale hierarchy and urban networks in China from 2019 to 2023,drawing on Baidu migration data and employing a spatial simultaneous equation model.The results reveal a significant positive spatial correlation between cities with higher hierarchy and those with greater network centrality.Within a static framework,we identify a positive interaction between urban scale hierarchy and urban network centrality,while their spatial cross-effects manifest as negative neighborhood interactions based on geographical distance and positive cross-scale interactions shaped by network connections.Within a dynamic framework,changes in urban scale hierarchy and urban networks are mutually reinforcing,thereby widening disparities within the urban hierarchy.Furthermore,an increase in a city’s network centrality had a dampening effect on the population growth of neighboring cities and network-connected cities.This study enhances understanding of the spatial organisation of urban systems and offers insights for coordinated regional development. 展开更多
关键词 urban scale hierarchy urban networks spatial interaction spatial spillover effect Baidu migration data spatial simultaneous equation model China
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Spatial-temporal Patterns and Influencing Factors of Green and Low-carbon Technology Innovation:Evidence from Chinese Cities 认领 引用
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作者 GAO Xin GU Weinan 《Chinese Geographical Science》 SCIE CAS CSCD 2026年第4期590-605,共16页
As a crucial motivator for China to advance the‘dual carbon'target,green and low-carbon technology innovation(GLCTI)not only provides a technological path for the realization of carbon reduction,zero carbon,and n... As a crucial motivator for China to advance the‘dual carbon'target,green and low-carbon technology innovation(GLCTI)not only provides a technological path for the realization of carbon reduction,zero carbon,and negative carbon but also plays a role in promoting the low-carbon transformation of the socio-economic development.In this case,it is necessary to explore the spatial-temporal characteristics and influencing factors of GLCTI.Green and low-carbon patent data gathered via a web crawler were utilized to indicate the level of GLCTI from 2002 to 2020.Spatial Analysis,Spatial Autocorrelation,and Spatial Durbin Model were used to investigate the spatial-temporal evolution and influencing factors of GLCTI in China.The results show that:1)whether it was the number of patents granted or the number of city participation,fossil energy carbon reduction technology had been leading GLCTI across the country;2)the cities demonstrating stronger GLCTI performance in China are primarily located in the eastern coastal regions and provincial capitals of the central and western areas,with significant spatial differentiation characteristics;3)Chinese urban GLCTI was booming in low-carbon field,and non-resource-based cities had better conditions for the development of GLCTI;4)significant spatial spillover effects and path-dependent features were observed in Chinese urban GLCTI.Talent reserve,financial investment,foreign direct investment,urban economic scale,tertiary industry-based industrial structure,and urban air quality are the key variables to enhance cities'capacity in GLCTI,while the secondary industry-based industrial structure has an inhibitory and constraining effect.The results could provide a practical reference for the development of GLCTI in China and promote the realization of the‘dual-carbon'target. 展开更多
关键词 green and low-carbon technology innovation(GLCTI) spatial-temporal pattern urban-type influencing factor Spatial Durbin Model(SDM) China
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Spatial heterogeneity in machine learning-based poverty mapping:Where do models underperform? 认领 引用
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作者 Yating Ru Elizabeth Tennant +1 位作者 David S.Matteson Christopher B.Barrett 《Geography and Sustainability》 CSCD 2026年第2期44-58,共15页
Accurately locating poor populations is increasingly urgent as global poverty reduction has stalled under the combined pressures of conflicts,climate shocks,rising food prices,pandemics,and growing inequality.Recent s... Accurately locating poor populations is increasingly urgent as global poverty reduction has stalled under the combined pressures of conflicts,climate shocks,rising food prices,pandemics,and growing inequality.Recent studies harnessing geospatial big data and machine learning(ML)have significantly advanced poverty mapping,enabling granular and timely welfare estimates in traditionally data-scarce regions.While much of the existing research has focused on overall out-of-sample predictive performance,there is a lack of understanding regarding where such models underperform and whether key spatial relationships might vary across places.This study investigates spatial heterogeneity in ML-based poverty mapping in East Africa,testing whether spatial regres sion and ML techniques produce more unbiased predictions.We find that extrapolation into unsurveyed areas suffers from biases that spatial methods do not resolve;welfare is overestimated in impoverished regions,rural areas,and single sector-focused economies,whereas it tends to be underestimated in wealthier,urbanized,and diversified economies.Even as spatial models improve overall predictive accuracy,enhancements in tradition ally underperforming areas remain marginal.This underscores the need for more representative training datasets and better remotely sensed proxies,especially for poor and rural regions,in future research related to ML-based poverty mapping.For development agencies,the findings caution against treating ML-based outputs as neutral or universally reliable,highlighting instead the need to pair technical advances with investments in inclusive data collection,integration of spatial theory,and institutional strategies that address structural data inequalities. 展开更多
关键词 Poverty mapping Machine learning Spatial models East Africa
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Spatial heterogeneity in the relationship between urbanization and urban land green use efficiency:A comparative study of the Yangtze and Yellow river basins in China 认领 引用
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作者 CHEN Qian ZHENG Liang +2 位作者 WANG Ying WU Di LI Jiangfeng 《Journal of Geographical Sciences》 SCIE CSCD 2026年第7期1590-1612,共23页
Urbanization has been widely recognized as one of the most important factors affecting urban land use,especially in densely populated and ecologically sensitive river basins.Therefore,cities must transition from high-... Urbanization has been widely recognized as one of the most important factors affecting urban land use,especially in densely populated and ecologically sensitive river basins.Therefore,cities must transition from high-pollution practices to more sustainable land resource management for socio-economic development.The objective of this study was to fill the knowledge gap regarding the impact of urbanization on urban land green use efficiency(ULGUE)and its regional variations.To achieve this,the Malmquist-Luenberger model and a spatial econometric model were employed to assess ULGUE and examine the spatial correlation between urbanization and ULGUE from 2005 to 2022.The analysis was conducted in China's prefecture-level cities in the Yangtze River Economic Belt(YREB)and Yellow River Basin(YRB)regions.The results indicated that the ULGUE of the YREB fluctuated upward,whereas that of the YRB fluctuated downward,with cities along the rivers exhibiting higher efficiency was higher.The spatial distribution characteristics of urbanization rates in the two basins demonstrated that the Heihe-Tengchong Line serves as a dividing line for urbanization levels.In addition,the spatial relationship between urbanization and ULGUE exhibited significant heterogeneity across different basins and cities of varying sizes.These findings inform decision-making for sustainable urban development in river basins. 展开更多
关键词 urbanization urban land green use efficiency Malmquist-Luenberger model spatial econometric model Yangtze River Economic Belt Yellow River Basin
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Spatial microsimulation modeling for residential energy demand of England in an uncertain future 认领 引用
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作者 Chengchao ZUO Mark BIRKIN Nicolas MALLESON 《Geo-Spatial Information Science》 EI 2014年第3期153-169,共17页
High quality infrastructure is crucial to economic success and the sustainability of society.Infrastructures for services,such as transport,energy,and water supply,also have long lead times,and therefore require effec... High quality infrastructure is crucial to economic success and the sustainability of society.Infrastructures for services,such as transport,energy,and water supply,also have long lead times,and therefore require effective long-term planning.In this paper,we report on work undertaken as part of the UK Infrastructure Transitions Research Consortium to construct long-term models of demographic change which can help to inform infrastructure planning for transport,energy,and water as well as IT and waste.A set of demographic microsimulation models(MSM),which are spatially disaggregate to the geography of UK Local Authorities,provides a high level of detail for understanding the drivers of changing patterns of demand.However,although robust forecasting models are required to support projections based on the notion of‘predict-and-provide,’the potential for behavioral adaptation is also an important consideration in this context.In this paper,we therefore establish a framework for linkage of a MSM of household composition,with behavior relating to the consumption of energy.We will investigate variations in household energy consumption within and between different household groups.An appropriate range of household types will be defined through the application of decision trees to consumption data from a detailed survey produced by the UK Department of Energy and Climate Change.From this,analysis conclusions will be drawn about the impact of changing demographics at both household and individual level,and about the potential effect of behavioral adjustments for different household groups. 展开更多
关键词 spatial modeling microsimulation energy consumption demographic modeling
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Research on the Spatial Model of the Integration of Positive Emotion into the Process of Innovation and Entrepreneurship Education 认领 引用
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作者 Md Rayhan Beg Liang-li Shen Feng Qu 《Journal of Contemporary Educational Research》 2026年第1期285-291,共7页
Based on the theory of“space production,”the space of traditional innovation and entrepreneurship education is a relatively closed classroom,presenting a one-way structure of teacher-podium-student seats,which is es... Based on the theory of“space production,”the space of traditional innovation and entrepreneurship education is a relatively closed classroom,presenting a one-way structure of teacher-podium-student seats,which is essentially the materialization of the one-way transmission relationship of educational power and the relationship between knowledge authority and passive recipient.The social relationship between teachers as the leaders of knowledge and resources and students as passive recipients solidifies the direction of knowledge transmission through spatial layout.According to the particularity of the innovation and entrepreneurship education process,spatial factors are regarded as the key variables of participating in positive emotions,which is conducive to restoring the significantly heterogeneous innovation and entrepreneurship education environment and clarifying the key factors that may affect the integration of positive emotions into innovation and entrepreneurship education.Based on the phased characteristics of innovation and entrepreneurship education,the spatial model with ideological and political knowledge and innovation and entrepreneurship education as the content is a supplement to the traditional point-line-surface positive emotion implantation model. 展开更多
关键词 Positive emotion Innovation and entrepreneurship education Spatial model
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GIS-Based Spatial Analysis and Modeling for Landslide Hazard Assessment:A Case Study in Upper Minjiang River Basin 认领 引用
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作者 FENG Wenlan ZHOU Qigang +4 位作者 ZHANG Baolei ZHOU Wancun LI Ainong ZHANG Haizhen XIAN Wei 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第4期847-852,共6页
By analyzing the topographic features of past landslides since 1980s and the main land-cover types （including change information） in landslide-prone area, modeled spatial distribution of landslide hazard in upper Mi... By analyzing the topographic features of past landslides since 1980s and the main land-cover types （including change information） in landslide-prone area, modeled spatial distribution of landslide hazard in upper Minjiang River Basin was studied based on spatial analysis of GIS in this paper. Results of GIS analysis showed that landslide occurrence in this region closely related to topographic feature. Most areas with high hazard probability were deep-sheared gorge. Most of them in investigation occurred assembly in areas with elevation lower than 3 000 m, due to fragile topographic conditions and intensive human disturbances. Land-cover type, including its change information, was likely an important environmental factor to trigger landslide. Destroy of vegetation driven by increase of population and its demands augmented the probability of landslide in steep slope. 展开更多
关键词 landslide probability spatial modeling upper Minjiang River
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Evaluation and Influence Factors of Green Innovation Efficiency in Old Industrial Area of Northeast China:New Evidence Based on Spatial Econometric Models 认领 引用 被引量：3
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作者 GUO Fuyou LI Linshan +2 位作者 ZHOU Mingxi SUN Yongsheng REN Jiamin 《Chinese Geographical Science》 SCIE CSCD 2025年第6期1315-1327,共13页
Green innovation is an important driving force for high-quality development and an important guarantee for the revitalization of the old industrial base in Northeast China.However,research on green innovation is still... Green innovation is an important driving force for high-quality development and an important guarantee for the revitalization of the old industrial base in Northeast China.However,research on green innovation is still insufficient.Using the super-efficiency epsilon-based measure Malmquist model,kernel density estimation,and spatial econometric model,this study investigated the spatiotemporal evolution characteristics and influencing factors of green innovation efficiency(GIE)in Northeast China from 2005 to 2020.The results reveal that:1)The GIE in Northeast China has obvious phased characteristics,where 2005-2011 was a period of fluctuating decline while 2012-2020 was a period of fluctuating increase,reflecting the severe resource and environmental constraints faced by the green innovation process.2)The GIE in the Northeast China has a significant spatial dependence,which has not formed a relatively stable spatial club feature.The process for improving the GIE in the Northeast China in the future is still arduous and far off.3)The interweaving and mutual influence of nonequilibrium factors have led to the diversity and complexity of the spatiotemporal pattern evolution of GIE.Overall,the level of economic development and industrial structure has a positive effect,while foreign investment and industrial agglomeration have a negative effect.The direct effects of government regulation,resource endowment,science and technology,environmental regulation,and urbanization are not significant.The research conclusion of this article can provide important reference for the revitalization of Northeast China. 展开更多
关键词 green innovation efficiency(GIE) spatial and temporal patterns influencing factors spatial econometric model Northeast China
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Modeling the Spatial Distribution of Soil Heavy Metals Using Random Forest Model—A Case Study of Nairobi and Thirirka Rivers’ Confluence 认领 引用 被引量：2
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作者 Evans Omondi Mark Boitt 《Journal of Geographic Information System》 2020年第6期597-619,共23页
Modeling the spatial distribution of soil heavy metals is important in determining the safety of contaminated soils for agricultural use. This study utilized 60 topsoil samples (0 - 30 cm), multispectral images (Senti... Modeling the spatial distribution of soil heavy metals is important in determining the safety of contaminated soils for agricultural use. This study utilized 60 topsoil samples (0 - 30 cm), multispectral images (Sentinel-2), spectral indices, and ancillary data to model the spatial distribution of heavy metals in the soils along the Nairobi River. The model was generated using the Random Forest package in R. Using R2 to assess the prediction accuracy, the Random Forest model generated satisfactory results for all the elements. It also ranked the variables in order of their importance in the overall prediction. Spectral indices were the most important variables within the rankings. From the predicted topsoil maps, there were high concentrations of Cadmium on the easterly end of the river. Cadmium is an impurity in detergents, and this section is in close proximity to the Nairobi water sewerage plant, which could be a direct source of Cadmium. Some farms had Zinc levels which were above the World Health Organization recommended limit. The Random Forest model performed satisfactorily. However, the predictions can be improved further if the spatial resolutions of the various variables are increased and through the addition of more predictor variables. 展开更多
关键词 Random Forest Sentinel 2 Heavy Metals Spectral Indices Spatial Modeling
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Spatial Evolution Characteristics and Influencing Factors of Urban Green Innovation in China 认领 引用 被引量：6
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作者 PENG Wenbin SU Xinyi TANG Yueliang 《Chinese Geographical Science》 SCIE CSCD 2025年第2期234-249,共16页
Cities are important carriers of green innovation.The foundation for accelerating China's ecological civilization construction and fostering regionally coordinated and sustainable development is quantitative analy... Cities are important carriers of green innovation.The foundation for accelerating China's ecological civilization construction and fostering regionally coordinated and sustainable development is quantitative analysis of the spatial evolution pattern and influencing factors of urban green innovation,as well as revealing the development differences between regions.This study's research object includes 284 Chinese cities that are at the prefecture level or above,excluding Xizang,Hong Kong,Macao,and Taiwan of China due to incomplete data.The spatial evolution characteristics of urban green innovation in China between 2005 and 2021 are comprehensively described using the gravity center model and boxplot analysis.The factors that affect urban green innovation are examined using the spatial Durbin model(SDM).The findings indicate that:1)over the period of the study,the gravity center of urban green innovation in China has always been distributed in the Henan-Anhui border region,showing a migration characteristic of‘initially shifting northeast,subsequently southeast',and the migration speed has gradually increased.2)Although there are also noticeable disparities in east-west,the north-south gap is the main cause of the shift in China's urban green innovation gravity center.The primary areas of urban green innovation in China are the cities with green innovation levels higher than the median.3)The main influencing factor of urban green innovation is the industrial structure level.The effect of the financial development level,the government intervention level,and the openness to the outside world degree on urban green innovation is weakened in turn.The environmental regulation degree is not truly influencing urban green innovation.The impact of various factors on green innovation across cities of different sizes,exhibiting heterogeneity.This study is conducive to broadening the academic community's comprehension of the spatial evolution characteristics of urban green innovation and offering a theoretical framework for developing policies for the all-encompassing green transformation of social and economic growth. 展开更多
关键词 urban green innovation spatial evolution spatial Durbin model China
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Effect of Spatial and Temporal Scales on Habitat Suitability Modeling:A Case Study of Ommastrephes bartramii in the Northwest Pacific Ocean 认领 引用 被引量：4
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作者 GONG Caixia CHEN Xinjun +1 位作者 GAO Feng TIAN Siquan 《Journal of Ocean University of China》 SCIE CAS 2014年第6期1043-1053,共11页
Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the ro... Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the roles of spatio-temporal scales in habitat suitability modeling,with the western stock of winter-spring cohort of neon flying squid （Ornmastrephes bartramii） in the northwest Pacific Ocean as an example.In this study,the fishery-dependent data from the Chinese Mainland Squid Jigging Technical Group and sea surface temperature （SST） from remote sensing during August to October of 2003-2008 were used.We evaluated the differences in a habitat suitability index model resulting from aggregating data with 36 different spatial scales with a combination of three latitude scales （0.5°,1 ° and 2°）,four longitude scales （0.5°,1°,2° and 4°）,and three temporal scales （week,fortnight,and month）.The coefficients of variation （CV） of the weekly,biweekly and monthly suitability index （SI） were compared to determine which temporal and spatial scales of SI model are more precise.This study shows that the optimal temporal and spatial scales with the lowest CV are month,and 0.5° latitude and 0.5° longitude for O.bartramii in the northwest Pacific Ocean.This suitability index model developed with an optimal scale can be cost-effective in improving forecasting fishing ground and requires no excessive sampling efforts.We suggest that the uncertainty associated with spatial and temporal scales used in data aggregations needs to be considered in habitat suitability modeling. 展开更多
关键词 spatial and temporal scales data aggregation habitat suitability model sea surface temperature Ommastrephes bartramii northwest Pacific Ocean
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Modeling of Spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression Model 认领 引用 被引量：4
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作者 ZHANG Haitao GUO Long +3 位作者 CHEN Jiaying FU Peihong GU Jianli LIAO Guangyu 《Chinese Geographical Science》 SCIE CSCD 2014年第2期191-204,共14页
This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 199... This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors. 展开更多
关键词 spatial lag model spatial error model geographically weighted regression model global spatial autocorrelation local spatial aurocorrelation
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Study on the Spatial Effect of Smart City Construction on Green Total Factor Productivity 认领 引用
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作者 Yu Shuang Ren Fu Yu Muhammad Ilyas 《Journal of Environmental & Earth Sciences》 CAS 2025年第1期550-561,共12页
Smart cities,a new kind of urbanization,offer a means of achieving the condition in which environmental conservation and economic growth are mutually beneficial.As a result,it is important to think about whether and h... Smart cities,a new kind of urbanization,offer a means of achieving the condition in which environmental conservation and economic growth are mutually beneficial.As a result,it is important to think about whether and how the development of smart cities might support the high-quality growth of urban economies.Based on the panel data of 163 prefecture-level cities in China from 2009–2018,the green total factor productivity(GTFP)of each prefecture-level city is measured using the SBM-GML model,and the appropriate spatial econometric model is screened by various types of tests.The spatial effect of smart city construction on GFTP is studied,and it is concluded that the pilot cities have a significant positive spatial spillover effect.The decomposition econometric model also shows that the pilot cities have a significant positive spatial spillover effect,and it also indicating that the smart city construction can also drive the surrounding cities to jointly improve the quality of economic development.Finally,the robustness of the spatial effect of smart city policy is also verified by changing the spatial measurement model and the type of spatial weight matrix,which also shows that the results of the spatial spillover effect of smart city construction are reliable. 展开更多
关键词 Smart Cities Green Total Factor Productivity Spatial Durbin Model High-Quality Development
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Impact of ionospheric irregularity on SBAS integrity:spatial threat modeling and improvement 认领 引用 被引量：2
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作者 BAO Junjie LI Rui +1 位作者 LIU Pan HUANG Zhigang 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2018年第5期908-917,共10页
The ionosphere, as the largest and least predictable error source, its behavior cannot be observed at all places simultaneously. The confidence bound, called the grid ionospheric vertical error(GIVE), can only be dete... The ionosphere, as the largest and least predictable error source, its behavior cannot be observed at all places simultaneously. The confidence bound, called the grid ionospheric vertical error(GIVE), can only be determined with the aid of a threat model which is used to restrict the expected ionospheric behavior. However, the spatial threat model at present widespread used, which is based on fit radius and relative centroid metric(RCM), is too conservative or the resulting GIVEs will be too large and will reduce the availability of satellite-based augmentation system(SBAS). In this paper, layered two-dimensional parameters, the vertical direction double RCMs, are introduced based on the spatial variability of the ionosphere. Comparing with the traditional threat model, the experimental results show that the user ionospheric vertical error(UIVE) average reduction rate reaches 16%. And the 95% protection level of conterminous United States(CONUS) is 28%, even under disturbed days, which reaches about 5% reduction rates.The results show that the system service performance has been improved better. 展开更多
关键词 ionospheric delay spatial threat model relative centroid metric(RCM) user ionospheric vertical error(UIVE)
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