基于GTWR模型的数字经济对经济高质量发展的时空效应研究

随着数字技术的不断提升,数字产业的不断发展,数字经济成为经济高质量发展的新引擎。研究数字经济与省际经济高质量发展之间的关系,对进一步促进省际经济高质量发展具有重要战略意义。论文基于2014—2020年中国30个省市的面板数据,采用熵权TOPSIS法计算数字经济和经济高质量发展的综合得分,考虑到数字经济和经济高质量发展的时空非平稳性,运用空间自相关检验和热点分析探究二者在空间上的分布规律,然后采用时空地理加权回归(GTWR)模型对数字经济与我国省际经济高质量发展之间的时空响应规律进行研究,并进一步探究数字经济影响省际经济高质量发展的具体路径。研究结果表明:1)数字经济与经济高质量发展均存在较强的空间自相关性,二者的热点区域集中于我国的中部和东部地区,冷点区域集中于西部地区,数字经济空间聚集强度不断减小,高质量发展空间聚集强度维持在较高水平;2)数字经济能够显著正向促进经济高质量发展,且影响系数存在时空异质性,空间上大致呈现出由东南沿海向西北内陆递减的态势,时间上省际的差异不断缩小趋向区域协调发展;3)数字经济对经济高质量发展各子指标的驱动力存在差异性,对创新发展的驱动力最弱。因此提出如下建议:要进一步推进西部地区数字经济的区域协调发展;要提高创新驱动力,以创新促发展;要以数字经济推动省际高质量发展。

基于GTWR模型的济南都市圈生态系统服务价值对城市扩张时空响应

【目的】随着我国城镇化发展进入到以中心城市引领都市圈、城市群的发展阶段,如何促进都市圈城镇化与生态环境协调发展成为高质量城镇发展的重要议题。生态系统服务价值对城市扩张的时空响应研究有助于把脉城市发展与生态系统服务的时空演进特征,推动城市与生态系统的协同发展,助力可持续规划以及建设策略的拟定与实施。【方法】本文以济南都市圈为研究对象,基于城镇扩展指数的计算,定量描述各城市扩张的时空特征。采用生态系统服务当量因子法,从多个角度刻画研究区生态系统服务的时空分异特征,并分析生态系统权衡与协同效应。在此基础上,运用时空地理加权回归(GTWR)模型,探究城市扩张对生态系统服务功能变化的驱动方向与驱动强度。【结果】(1)1980—2020年间济南都市圈内城市扩张显著,具有时序阶段性与区域分异性两方面特征,城市空间扩展速率与强度由高到低依次为小城市、大城市、特大城市、中等城市。(2)都市圈内整体生态服务价值量呈逐年下降趋势,黄河干流、东平湖及周边区域与鲁中山区等地区是重要的生态系统服务价值高值聚集区,都市圈内协同关系占比略低于权衡关系,其中特大城市协同关系占比最高。(3)济南都市圈内城市扩张对生态系统服务价值整体具有负面影响,随着时间的推进,影响强度有所下降。城市扩张对各亚类生态系统服务功能的影响作用具有显著差异,对供给服务与支持服务价值量变化具有负面影响,其中对供给服务变化的驱动强度不断增强,对调节服务价值量变化具有正向作用且影响力整体变化不大,对文化服务价值量变化的影响具有两面性,在不同地区的驱动方向与强度差异性较大。【结论】本研究明确了研究期限内济南都市圈中不同等级城市空间扩展的时空分异规律以及生态系统服务逐渐劣化的发展状态,所构建的GTWR模型在空间层面上量化了城市扩张对生态系统服务总量及各亚类变化量的不同驱动特征与驱动强度,研究成果可为都市圈高质量可持续发展提供决策依据。

基于GTWR模型的长江中游城市群碳排放时空异质性分析

在双碳背景下,研究中部地区城市群碳排放时空异质性特征对于促进区域低碳发展具有重要意义.该文以长江中游城市群为研究对象,选取2011—2020年的面板数据,从碳排放量、人均碳排放和地均碳排放分析碳排放的时空异质特征;运用GTWR模型探究案例区碳排放之影响因素及其时空异质性.结果表明:1)2011—2020年长江中游城市群碳排放总量不断增长,但增速趋缓.排放量由98152.00万t增长至132226.12万t,增长率由8.23%减缓至4.85%;2)长江中游城市群碳排放具有很强的空间异质性,碳排放总量、人均碳排放量和地均碳排放量总体均呈现为武汉城市圈>环长株潭城市群>环鄱阳湖城市群,其中环鄱阳湖城市群与前两者差异尤为显著;3)人口总量、经济发展水平、工业结构、能源消耗强度等因素对地区碳排放具有显著的正向促进作用,作用强度同样呈现武汉城市圈>环长株潭城市群>环鄱阳湖城市群的特征.

基于GTWR模型的安徽省气候对水稻生产力影响的时空分布规律

以气温和降水量作为气候变化的2个因素,以安徽省为研究区域,基于2001—2020年气温、降水量及水稻产量数据,使用时空地理加权回归(GTWR)模型分析气温与降水量2个因素对水稻产量的作用机制。结果表明,2001—2020年,安徽省各市水稻年平均产量在时间上出现持续波动的现象,在空间上也存在特定的集聚现象;安徽省西北部地区气温、降水量与水稻产量呈正相关关系,其中蚌埠市正相关关系最为显著;在安徽省所有城市中,淮南市和六安市的水稻产量受气温和降水量影响最为明显,而淮北市水稻产量受气温和降水量的影响相对较小,说明该地区其他因素对水稻产量具有更深影响。

基于POI-OD矩阵与GTWR分析共享单车出行特征与影响因素

共享单车的时空分布特征反映了居民的出行需求,单车出行的流动则体现了居民使用共享单车的目的性。本文基于东芝加哥中心区域11个月的共享单车出行数据的时空分布特征,结合POI分布建立POI-OD矩阵量化OD的流动特征,结合GTWR分析各类型POI与共享单车流入、流出量的相关性。结果表明:1)共享单车工作日呈现早晚高峰趋势,以短距离出行为主;2)交通运输类和餐饮类POI是主要转移热点;3)户外休闲类、餐饮类、娱乐类、工作教育类和交通运输类POI的数量对格网内共享单车的流入、流出量有积极影响,影响程度递减;住宅类和商业金融类POI的数量对格网内共享单车的流入、流出量有消极影响,住宅类影响最显著。

基于GTWR的站域建成环境对城市轨道交通客流量的时空影响

轨道交通客流量影响因素是轨道交通方面研究的一个关注点,不同站点客流量的时空非平稳性被认为与站域建成环境有关。通过构建时空地理加权(geographically and temporally weighted regression,GTWR)模型,揭示了土地多样性、密度、站点属性3个方面因素在时间和空间维度上对天津市轨道交通客流量的影响。结果表明:相较于传统的地理加权(geographically weighted regression,GWR)模型和最小二乘法(ordinary least squares,OLS)模型,GTWR具有更好的拟合优度;公交站点密度对轨道交通客流产生促进作用,尤其在工作日的早晚高峰时段和中心城区位置;市中心的商业设施在工作日晚高峰吸引更多的地铁乘客,而在近郊区它们在早高峰吸引更多的地铁乘客;人口密度促进轨道交通的客流量;充足的停车场设施数量可以吸引更多的轨道交通乘客。

A comparison of model choice strategies for logistic regression

Purpose:The purpose of this study is to develop and compare model choice strategies in context of logistic regression.Model choice means the choice of the covariates to be included in the model.Design/methodology/approach:The study is based on Monte Carlo simulations.The methods are compared in terms of three measures of accuracy:specificity and two kinds of sensitivity.A loss function combining sensitivity and specificity is introduced and used for a final comparison.Findings:The choice of method depends on how much the users emphasize sensitivity against specificity.It also depends on the sample size.For a typical logistic regression setting with a moderate sample size and a small to moderate effect size,either BIC,BICc or Lasso seems to be optimal.Research limitations:Numerical simulations cannot cover the whole range of data-generating processes occurring with real-world data.Thus,more simulations are needed.Practical implications:Researchers can refer to these results if they believe that their data-generating process is somewhat similar to some of the scenarios presented in this paper.Alternatively,they could run their own simulations and calculate the loss function.Originality/value:This is a systematic comparison of model choice algorithms and heuristics in context of logistic regression.The distinction between two types of sensitivity and a comparison based on a loss function are methodological novelties.

Performance Enhancement of XML Parsing Using Regression and Parallelism

The Extensible Markup Language(XML)files,widely used for storing and exchanging information on the web require efficient parsing mechanisms to improve the performance of the applications.With the existing Document Object Model(DOM)based parsing,the performance degrades due to sequential processing and large memory requirements,thereby requiring an efficient XML parser to mitigate these issues.In this paper,we propose a Parallel XML Tree Generator(PXTG)algorithm for accelerating the parsing of XML files and a Regression-based XML Parsing Framework(RXPF)that analyzes and predicts performance through profiling,regression,and code generation for efficient parsing.The PXTG algorithm is based on dividing the XML file into n parts and producing n trees in parallel.The profiling phase of the RXPF framework produces a dataset by measuring the performance of various parsing models including StAX,SAX,DOM,JDOM,and PXTG on different cores by using multiple file sizes.The regression phase produces the prediction model,based on which the final code for efficient parsing of XML files is produced through the code generation phase.The RXPF framework has shown a significant improvement in performance varying from 9.54%to 32.34%over other existing models used for parsing XML files.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

Geographically and Temporally Weighted Regression in Assessing Dengue Fever Spread Factors in Yunnan Border Regions

Objective This study employs the Geographically and Temporally Weighted Regression(GTWR)model to assess the impact of meteorological elements and imported cases on dengue fever outbreaks,emphasizing the spatial-temporal variability of these factors in border regions.Methods We conducted a descriptive analysis of dengue fever’s temporal-spatial distribution in Yunnan border areas.Utilizing annual data from 2013 to 2019,with each county in the Yunnan border serving as a spatial unit,we constructed a GTWR model to investigate the determinants of dengue fever and their spatio-temporal heterogeneity in this region.Results The GTWR model,proving more effective than Ordinary Least Squares(OLS)analysis,identified significant spatial and temporal heterogeneity in factors influencing dengue fever’s spread along the Yunnan border.Notably,the GTWR model revealed a substantial variation in the relationship between indigenous dengue fever incidence,meteorological variables,and imported cases across different counties.Conclusion In the Yunnan border areas,local dengue incidence is affected by temperature,humidity,precipitation,wind speed,and imported cases,with these factors’influence exhibiting notable spatial and temporal variation.

Nuclear charge radius predictions by kernel ridge regression with odd-even effects

The extended kernel ridge regression(EKRR)method with odd-even effects was adopted to improve the description of the nuclear charge radius using five commonly used nuclear models.These are:(i)the isospin-dependent A^(1∕3) formula,(ii)relativistic continuum Hartree-Bogoliubov(RCHB)theory,(iii)Hartree-Fock-Bogoliubov(HFB)model HFB25,(iv)the Weizsacker-Skyrme(WS)model WS*,and(v)HFB25*model.In the last two models,the charge radii were calculated using a five-parameter formula with the nuclear shell corrections and deformations obtained from the WS and HFB25 models,respectively.For each model,the resultant root-mean-square deviation for the 1014 nuclei with proton number Z≥8 can be significantly reduced to 0.009-0.013 fm after considering the modification with the EKRR method.The best among them was the RCHB model,with a root-mean-square deviation of 0.0092 fm.The extrapolation abilities of the KRR and EKRR methods for the neutron-rich region were examined,and it was found that after considering the odd-even effects,the extrapolation power was improved compared with that of the original KRR method.The strong odd-even staggering of nuclear charge radii of Ca and Cu isotopes and the abrupt kinks across the neutron N=126 and 82 shell closures were also calculated and could be reproduced quite well by calculations using the EKRR method.

Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.

Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

Prediction of Ground Vibration Induced by Rock Blasting Based on Optimized Support Vector Regression Models

Accurately estimating blasting vibration during rock blasting is the foundation of blasting vibration management.In this study,Tuna Swarm Optimization(TSO),Whale Optimization Algorithm(WOA),and Cuckoo Search(CS)were used to optimize two hyperparameters in support vector regression(SVR).Based on these methods,three hybrid models to predict peak particle velocity(PPV)for bench blasting were developed.Eighty-eight samples were collected to establish the PPV database,eight initial blasting parameters were chosen as input parameters for the predictionmodel,and the PPV was the output parameter.As predictive performance evaluation indicators,the coefficient of determination(R2),rootmean square error(RMSE),mean absolute error(MAE),and a10-index were selected.The normalizedmutual information value is then used to evaluate the impact of various input parameters on the PPV prediction outcomes.According to the research findings,TSO,WOA,and CS can all enhance the predictive performance of the SVR model.The TSO-SVR model provides the most accurate predictions.The performances of the optimized hybrid SVR models are superior to the unoptimized traditional prediction model.The maximum charge per delay impacts the PPV prediction value the most.

Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas:A Case Study of Songnen Plain,Northeast China

The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

Modeling of Total Dissolved Solids (TDS) and Sodium Absorption Ratio (SAR) in the Edwards-Trinity Plateau and Ogallala Aquifers in the Midland-Odessa Region Using Random Forest Regression and eXtreme Gradient Boosting

Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R2 values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large.

Country-based modelling of COVID-19 case fatality rate:A multiple regression analysis

BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale cannot be fully understood due to lack of information.AIM To identify key factors that may explain the variability in case lethality across countries.METHODS We identified 21 Potential risk factors for coronavirus disease 2019(COVID-19)case fatality rate for all the countries with available data.We examined univariate relationships of each variable with case fatality rate(CFR),and all independent variables to identify candidate variables for our final multiple model.Multiple regression analysis technique was used to assess the strength of relationship.RESULTS The mean of COVID-19 mortality was 1.52±1.72%.There was a statistically significant inverse correlation between health expenditure,and number of computed tomography scanners per 1 million with CFR,and significant direct correlation was found between literacy,and air pollution with CFR.This final model can predict approximately 97%of the changes in CFR.CONCLUSION The current study recommends some new predictors explaining affect mortality rate.Thus,it could help decision-makers develop health policies to fight COVID-19.

Optimization of Generator Based on Gaussian Process Regression Model with Conditional Likelihood Lower Bound Search

The noise that comes from finite element simulation often causes the model to fall into the local optimal solution and over fitting during optimization of generator.Thus,this paper proposes a Gaussian Process Regression(GPR)model based on Conditional Likelihood Lower Bound Search(CLLBS)to optimize the design of the generator,which can filter the noise in the data and search for global optimization by combining the Conditional Likelihood Lower Bound Search method.Taking the efficiency optimization of 15 kW Permanent Magnet Synchronous Motor as an example.Firstly,this method uses the elementary effect analysis to choose the sensitive variables,combining the evolutionary algorithm to design the super Latin cube sampling plan;Then the generator-converter system is simulated by establishing a co-simulation platform to obtain data.A Gaussian process regression model combing the method of the conditional likelihood lower bound search is established,which combined the chi-square test to optimize the accuracy of the model globally.Secondly,after the model reaches the accuracy,the Pareto frontier is obtained through the NSGA-II algorithm by considering the maximum output torque as a constraint.Last,the constrained optimization is transformed into an unconstrained optimizing problem by introducing maximum constrained improvement expectation(CEI)optimization method based on the re-interpolation model,which cross-validated the optimization results of the Gaussian process regression model.The above method increase the efficiency of generator by 0.76%and 0.5%respectively;And this method can be used for rapid modeling and multi-objective optimization of generator systems.

基于GTWR模型的疏勒河流域人均三维生态足迹动态变化及其影响因素分析

生态足迹是一种通过测量人类对区域自然资本利用程度评价该区域是否可持续发展的方法。疏勒河流域作为我国西北典型干旱区,分析其生态足迹的变化特征能够有效保障流域可持续发展。论文基于生产视角,通过土地利用数据、生物产品产量数据、社会经济数据和人口数据等,采用人均三维生态足迹模型对2000—2020年疏勒河流域人均足迹广度、人均足迹深度、人均三维生态足迹及自然资本存流量使用情况进行分析,探究流域人均三维生态足迹的动态变化特征,利用GTWR模型分析各因子的时空异质性。结果表明:2000—2020年,疏勒河流域人均足迹广度总体呈上—中—下游依次递减趋势;随着土地利用程度加深,人类对生物资源的占用速度超过其再生速度,流域人均足迹深度大于1;流域内各县人均三维生态足迹上升,生态压力增大,生态足迹重心总体自西向东北移动。在疏勒河流域,人均生态足迹主要受社会发展、民生福祉和工业发展三方面因素影响。