Quantifying the impact of earthquakes and geological factors on spatial heterogeneity of debris-flow prone areas:A case study in the Hengduan Mountains

Understanding the spatial heterogeneity of debris-flow-prone areas holds significant implications for regional risk management, particularly in seismically active regions with geological faults. Despite the significance of this knowledge, a comprehensive quantification of the influence of regional topographical and geological factors on the spatial heterogeneity of debris-flow-prone areas has been lacking. This study selected the Hengduan Mountains, an earthquake-prone region characterized by diverse surface conditions and complex landforms, as a representative study area. An improved units zoning and objective factors identification methodology was employed in earthquake and fault analysis to assess the impact of seismic activity and geological factors on spatial heterogeneity of debrisflow prone areas. Results showed that the application of GIS technology with hydrodynamic intensity and geographical units analysis can effectively analyze debris-flow prone areas. Meanwhile, earthquake and fault zones obviously increase the density of debrisflow prone catchments and make them unevenly distributed. The number of debris-flow prone areas shows a nonlinear variation with the gradual increase of geomorphic factor value. Specifically, the area with 1000 m-2500 m elevation difference, 25°-30° average slope, and 0.13-0.15 land use index is the most favorable conditions for debris-flow occurrence;The average annual rainfall from 600 to 1150 mm and landslides gradient from 16° to 35° are the main causal factors to trigger debris flow. Our study sheds light on the quantification of spatial heterogeneity in debris flow-prone areas in earthquake-prone regions, which can offer crucial support for post-debris flow risk management strategies.

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.

Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

Spatiotemporal Characteristics of Typical Ecosystem Services and Their Spatial Responses to Driving Factors in Ecologically Fragile Areas in Upper Yellow River,China

The identification of dominant driving factors for different ecosystem services(ESs)is crucial for ecological conservation and sustainable development.However,the spatial heterogeneity of the dominant driving factors affecting various ESs has not been adequately elucidated,particularly in ecologically fragile regions.This study employed the integrated valuation of ESs and trade-offs(InVEST)model to evaluate four ESs,namely,water yield(WY),soil conservation(SC),habitat quality(HQ),and carbon storage(CS),and then to identify the dominant driving factors of spatiotemporal differentiation of ES and further to characterize the spatial heterogeneity characteristics of the dominant driving factors in the eco-fragile areas of the upper Yellow River,China from 2000 to 2020.The results demonstrated that WY exhibited northeast-high and northwest-low patterns in the upper Yellow River region,while high values of SC and CS were distributed in central forested areas and a high value of HQ was distributed in vast grassland areas.The CS,WY,and SC exhibited decreasing trends over time.The most critical factors affecting WY,SC,HQ,and CS were the actual evapotranspiration,precipitation,slope,and normalized difference vegetation index,respectively.In addition,the effects of different factors on various ESs exhibited spatial heterogeneity.These results could provide spatial decision support for eco-protection and rehabilitation in ecologically fragile areas.

Impact factors identification of spatial heterogeneity of herbaceous plant diversity on five southern islands of Miaodao Archipelago in North China

Herbaceous plants are widely distributed on islands and where they exhibit spatial heterogeneity.Accurately identifying the impact factors that drive spatial heterogeneity can reveal typical island biodiversity patterns.Five southern islands in the Miaodao Archipelago,North China were studied herein.The spatial distribution of herbaceous plant diversity on these islands was analyzed,and the impact factors and their degree of impact on spatial heterogeneity were identified using CCA ordination and ANOVA.The results reveal 114 herbaceous plant species,belonging to 94 genera from 34 families in the50 plots sampled.The total species numbers on different islands were significantly positively correlated with island area,and the average a diversity was correlated with human activities,while the(3 diversity among islands was more affected by island area than mutual distances.Spatial heterogeneity within islands indicated that the diversities were generally high in areas with higher altitude,slope,total nitrogen,total carbon,and canopy density,and lower moisture content,pH,total phosphorus,total potassium,and aspect.Among the environmental factors,pH,canopy density,total K,total P,moisture content,altitude,and slope had significant gross effects,but only canopy density exhibited a significant net effect.Terrain affected diversity by restricting plantation,plantation in turn influenced soil properties and the two together affected diversity.Therefore,plantation was ultimately the fundamental driving factor for spatial heterogeneity in herbaceous plant diversity on the five islands.

Spatial-temporal Heterogeneity of Green Development Efficiency and Its Influencing Factors in Growing Metropolitan Area: A Case Study for the Xuzhou Metropolitan Area

This study analyzed the spatial-temporal heterogeneity of green development efficiency and its influencing factors in the growing Xuzhou Metropolitan Area for the period 2000–2015.The slacks-based measure(SBM)model,spatial autocorrelation,and the geographically weighted regression(GWR)model were used to conduct the analysis.The conclusions were as follows:first,the overall efficiency of green development of the Xuzhou Metropolitan Area decreased,the regional differences and spatial agglomeration shrunk and differences within the region were the main contributors to the regional differences of green development efficiency.Second,the counties with high-efficiency green development were distributed along the coast,and along the routes of the Beijing-Shanghai and the Eastern Longhai railways.A developing axis of the high-efficiency counties was the main feature of the spatial pattern for green development efficiency.Third,regarding spatial correlation and green development efficiency,the High-High type counties in the Xuzhou Metropolitan Area formed a centralized distribution corridor along the inter-provincial border areas of Henan and Jiangsu,whereas the Low-Low type counties were concentrated in the external,marginal parts of the metropolitan area.Fourth,the major factors(ranked in decreasing order of impact)influencing green development efficiency were innovation,government regulations,the economic development level,energy consumption,and industrial structure.These factors exerted their influence to varying extents;the influence of the same factor had different effects in different regions and obvious spatial differences were observed for the different regions.

Individual Grain Orientation and Heterogeneous Deformation in Cold-deformed Interstitial-Free Sheet Steel

The cold rolling deformation textural evolution of an interstitial-free （IF） steel sheet is investigated by experiment and simulation. The microstructure of the IF steel is observed by transmission electron microscopy （TEM）. The relationship between the deformation behavior of individual grain and the grain orientation are connected by Taylor factor M. The results show that the grains with higher Taylor factor are deformed slighter than those with lower ones. By considering the heterogeneous deformation, the texture simulation result can be greatly improved.

Factor input structure,environmental policies,and the export quality upgrade:a perspective of heterogeneity

In the analysis of how environmental regulation affects the comparative advantage of trade,existing literature ignores industry's inherent heterogeneity, which draws remarkably different conclusions. In view of this, the paper analyzed the mechanism of environmental regulation on the export quality of different industries from the perspective of factor input structure heterogeneity. Based on the panel data of China's manufacturing industry, the paper used the system generalized method of moments method to examine the heterogeneity influence of environmental regulation on manufacturing export quality. The study found that, first, environmental regulation affected the export quality upgrade of the manufacturing sector through offset effect and compensation effect, and the direction of the impact would mainly depend on the industry's factor input structure. Second, for industries with larger fixed-asset investment(FAI) ratio in the factor input structure, the current environmental regulation policy was not conducive to the export quality upgrading of the industries. However, there was a significant U-shaped dynamic relationship between them. As environmental regulations became stricter, when regulatory stringency went beyond the inflection point, the policy would promote the upgrading of export quality. But for industries with smaller proportion of FAI, environmental regulation exerted a favorable impact on the export quality upgrade, following a J-shaped marginal growth curve.Third, for industries with different factor input structure, their export quality had been effectively upgraded as expected by factors like human capital investment, independent R&D, technology introduction, and foreign direct investment; but raising per capita capital stock and expanding enterprise size did not produce significant direct impact on export quality upgrade. These conclusions remained robust after using different measurement methods and replacing with other variables. Therefore, this paper suggests that governments should take industry heterogeneity into consideration and formulate differentiated hierarchical environmental policies.Besides, they should strengthen the enforcement of the current environmental regulation policies. By doing so, enterprises are forced to improve their technology and product quality so that they can better cope with rising compliance costs, eliminate backward industries, and resolve excess capacity. In this way, the economic structure would be transformed and upgraded from the supply side.

Regional Economic Efficiency and Its Influencing Factors of Beijing-Tianjin-Hebei Metropolitans in China Based on a Heterogeneity Stochastic Frontier Model

Using a heterogeneity stochastic frontier model(HSFM),we empirically investigated the economic efficiency of Beijing-Tianjin-Hebei from 2003 to 2016 and its influencing factors.The key findings of the paper lie in:1)in Beijing-Tianjin-Hebei,the overall economic and technological efficiency tended to increase in a wavelike manner,economic growth slowed down,and there was an obvious imbalance in economic efficiency between the different districts,counties and cities;2)the heterogeneity stochastic frontier production functions(SFPFs)of Beijing,Tianjin and Hebei were different from each other,and investment was still an important impetus of economic growth in Beijing-Tianjin-Hebei;3)economic efficiency was positively correlated with economic agglomeration,human capital,industrial structure,infrastructure,the informatization level,and institutional factors,but negatively correlated with the government role and economic opening.The following policy suggestions are offered:1)to improve regional economic efficiency and reduce the economic gap in Beijing-Tianjin-Hebei,governments must reduce their intervention in economic activities,stimulate the potentials of labor and capital,optimize the structure of human resources,and foster new demographic incentives;2)governments must guide economic factors that are reasonable throughout Beijing-Tianjin-Hebei and strengthen infrastructure construction in underdeveloped regions,thus attaining sustainable economic development;3)governments must plan overall economic growth factors of Beijing-Tianjin-Hebei and promote reasonable economic factors(e.g.,labor,resources,and innovations)across different regions,thus attaining complementary advantages between Beijing,Tianjin,and Hebei.

A Three-dimensional Heterogeneous Mass Transfer Model for the Absorption of Gas into Multiphase System

A three-dimensional heterogeneous mass transfer model was proposed to investigate the enhancement of dispersed particles on gas absorption. The strategy to calculate local and overall enhancement factors is proposed. Instead of a global grid, the composite overlapping grid is adopted, which simplifies the setup and solution of the three-dimensional model equations. It is found that dispersed particle hold-up, particle size, liquid-solid partition coefficient of transported component, characteristic contact time, and the shortest distance between particles and gas-liquid interface have major influence on absorption enhancement factor. The particle-particle interaction on gas absorption and the lateral diffusion of transported component in liquid film were studied with the multi-particle simulation. The proposed model predicted the experimental data from the literature reasonably well.

Spatial Heterogeneity and Influencing Factors of HFRS Epidemics in Rural and Urban Areas:A Study in Guanzhong Plain of Shaanxi Province,China

Objective The Guanzhong Plain of Shaanxi Province is a severely afflicted hemorrhagic fever with renal syndrome(HFRS)epidemic area,while HFRS prevalence has decreased in most epidemic areas in China.Little information is available regarding the leading fine-scale influencing factors in this highly HFRSconcentrated area and the roles of natural environmental and socioeconomic factors.To investigate this,two regions in the Guanzhong Plain,that is,the Chang’an District and Hu County,with similar geographical environments,different levels of economic development,and high epidemic prevalence,were chosen as representative areas of the HFRS epidemic.Methods Maximum entropy models were constructed based on HFRS cases and fine-scale influencing factors,including meteorological,natural environmental,and socioeconomic factors,from 2014 to 2016.Results More than 95% of the HFRS cases in the study area were located in the northern plains,which has an altitude of less than 800 m,with topography contributed 84.1% of the impact on the spatial differentiation of the HFRS epidemic.In the northern plains,precipitation and population density jointly affected the spatial differentiation of the HFRS epidemic,with contribution rates of 60.7% and 28.0%,respectively.By comparing the influencing factors of the northern plains of Chang’an District and Hu County,we found that precipitation and the normalized difference vegetation index(NDVI)dominated the HFRS epidemic in the relatively developed Chang’an District,while land-use type,temperature,precipitation and population density dominated the HFRS epidemic in the relatively undeveloped Hu County.Conclusion Topography was the primary key factor for HFRS prevalence in the Chang’an District and Hu County,and the spatial differentiation of HFRS was dominated by precipitation and population density in the northern plains.Compared with the influencing factors of the relatively developed Chang’an District,the developing Hu County was more affected by socioeconomic factors.When formulating targeted HFRS epidemic prevention and control strategies in the targeted areas,it is crucial to consider the local economic development state and combine natural environmental factors,including the meteorological environment and vegetation coverage.

Detection of Multi-dimensional Driving Forces of Public Environmental Concern in China:Based on Spatial Heterogeneity Perspectives

Public environmental concern(PEC)is an important bottom-up force in building an environmentally sustainable society.Guided by attitude theory,this paper innovatively constructed a PEC evaluation index system,while introducing entropy weighted-TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)to realize the assessment of PEC.Exploratory spatial data analysis was used to portray the spatio-temporal evolution patterns of PEC in 362 Chinese cities at prefecture-level and above from 2011 to 2018.Furthermore,the Geodetector model was performed to identify the multi-dimensional determinants of PEC from the perspective of spatial heterogeneity.The results indicated that:1)PEC in China exhibited a fluctuating upward trend,consistent with the spatial distribution law of‘Heihe-Tengchong Line’and‘Bole-Taipei Line’;2)the driving effect of each factor varied dynamically,but in general,economic development level,population size,industrial wastewater,and education level were the dominant driving factors explaining the spatial variation of PEC;3)risk detection revealed that four factors,government environmental regulations,PM_(2.5),vegetation coverage,and natural resource endowment,had nonlinear effects on PEC;4)the interactions between factors all demonstrated an enhancement in explaining the spatial differentiation of PEC.PEC was driven by the comprehensive interaction of four-dimensional factors of economy,society,pollutant emissions,and ecology.Among them,population agglomeration accompanied by a high level of regional economy and information technology can explain the increase in PEC to the greatest extent.

Spatial Heterogeneity of Urban Happiness and Its Influencing Factors in China

Based on the data of prefecture-level and above cities, this paper examines the spatial distribution of well-being by applying the spatial econometrics. The results of the study suggest that China＇s urban well-being has a typical ＂club＂ characteristic and noticeable regional difference. As a relatively long-term and stable indicator, well-being is affected by variables of early conditions, urban per capita savings and public service, whereas inflexible indicators like urban per capita GDP,, infrastructure and urban characteristic have little influence on the spatial heterogeneity of well-being. Attention should also be paid to the regional effect of the above affecting factors. In accelerating the construction of ＂happy cities ＇＂ it is particularly important to establish a ＂tilted and flat＂ urban system.

Network autoregression model with grouped factor structures

Network autoregression and factor model are effective methods for modeling network time series data.In this study,we propose a network autoregression model with a factor structure that incorporates a latent group structure to address nodal heterogeneity within the network.An iterative algorithm is employed to minimize a least-squares objective function,allowing for simultaneous estimation of both the parameters and the group structure.To determine the unknown number of groups and factors,a PIC criterion is introduced.Additionally,statistical inference of the estimated parameters is presented.To assess the validity of the proposed estimation and inference procedures,we conduct extensive numerical studies.We also demonstrate the utility of our model using a stock dataset obtained from the Chinese A-Share stock market.

A review of the influencing factors on teleseismic traveltime tomography

Teleseismic traveltime tomography is an important tool for investigating the crust and mantle structure of the Earth.The imaging quality of teleseismic traveltime tomography is affected by many factors,such as mantle heterogeneities,source uncertainties and random noise.Many previous studies have investigated these factors separately.An integral study of these factors is absent.To provide some guidelines for teleseismic traveltime tomography,we discussed four main influencing factors:the method for measuring relative traveltime differences,the presence of mantle heterogeneities outside the imaging domain,station spacing and uncertainties in teleseismic event hypocenters.Four conclusions can be drawn based on our analysis.(1)Comparing two methods,i.e.,measuring the traveltime difference between two adjacent stations(M1)and subtracting the average traveltime of all stations from the traveltime of one station(M2),reveals that both M1 and M2 can well image the main structures;while M1 is able to achieve a slightly higher resolution than M2;M2 has the advantage of imaging long wavelength structures.In practical teleseismic traveltime tomography,better tomography results can be achieved by a two-step inversion method.(2)Global mantle heterogeneities can cause large traveltime residuals(up to about 0.55 s),which leads to evident imaging artifacts.(3)The tomographic accuracy and resolution of M1 decrease with increasing station spacing when measuring the relative traveltime difference between two adjacent stations.(4)The traveltime anomalies caused by the source uncertainties are generally less than 0.2 s,and the impact of source uncertainties is negligible.

Factors driving surface deformations in plain area of eastern Zhengzhou City,China

With the rapid socio-economic development and urban expansion,land subsidence has emerged as a major environmental issue,impeding the high-quality development of the plain area in eastern Zhengzhou City,Henan Province,China.However,effective prevention and control of land subsidence in this region have been challenging due to the lack of comprehensive surface deformations monitoring and the quantitative analysis of the factors driving these deformations.In order to accurately identify the dominant factor driving surface deformations in the study area,this study utilized the Persistent Scattered Interferometric Synthetic Aperture Radar(PS-InSAR)technique to acquire the spatio-temporal distribution of surface deformations from January 2018 to March 2020.The acquired data was verified using leveling data.Subsequently,GIS spatial analysis was employed to investigate the responses of surface deformations to the driving factors.The findings are as follows:Finally,the geographical detector model was utilized to quantify the contributions of the driving factors and reveal the mechanisms of their interactions.The findings are as follows:(1)Surface deformations in the study area are dominated by land subsidence,concentrated mainly in Zhongmu County,with a deformation rate of−12.5–−37.1 mm/a.In contrast,areas experiencing surface uplift are primarily located downtown,with deformation rates ranging from 0 mm to 8 mm;(2)Groundwater level,lithology,and urban construction exhibit strong spatial correlations with cumulative deformation amplitude;(3)Groundwater level of the second aquifer group is the primary driver of spatially stratified heterogeneity in surface deformations,with a contributive degree of 0.5328.The contributive degrees of driving factors are significantly enhanced through interactions.Groundwater level and the cohesive soil thickness in the second aquifer group show the strongest interactions in the study area.Their total contributive degree increases to 0.5722 after interactions,establishing them as the primary factors influencing surface deformation patterns in the study area.The results of this study can provide a theoretical basis and scientific support for precise prevention and control measures against land subsidence in the study area,as well as contributing to research on the underlying mechanisms.

金融要素集聚的绿色创新效应:空间关联特征与城市群异质性

区域绿色技术创新体系建设是经济绿色转型的重要基础,而金融要素空间集聚则充当着科技创新活动的重要加速器。现有文献围绕着金融要素集聚和绿色技术创新两条主线展开广泛讨论,也关注到金融要素集聚所内生的绿色创新效应,但较少基于城市群这一新时期区域协同发展的新型空间载体,系统考察两者的空间关联效应及其区域异质性特征。因此,选取国内十大代表性城市群,对其内部金融要素集聚和绿色技术创新的时空演变轨迹和空间关联异质性进行多维度诊断。研究发现:第一,金融要素集聚有助于通过各种空间效应加速城市群内部的绿色金融发展,进而正向驱动区域绿色技术创新水平的提升;第二,城市群范围内的金融要素集聚不仅会对区域绿色技术创新产生明显的本地促进效应,也会同时形成显著的空间溢出效果;第三,金融要素集聚对于绿色技术创新的空间效应还会受到城市群金融集聚模式、行政层级特征、经济发展状况等因素制约。有鉴于此,区域协同发展过程中,应围绕城市群发展定位构建差异化的金融要素集聚模式,因之打造契合于不同城市群现实约束的绿色金融服务体系和绿色技术创新体系;同时深度挖掘金融要素集聚对于绿色金融发展和绿色技术创新的空间传导效应及其异质性特征,推动金融改革和绿色发展的有效空间互动。

公众感知下重大传染病疫情冲击对社区韧性驱动因子独立、耦合作用及区域异质性分析

目的:探究公众感知下重大传染病疫情冲击对社区韧性驱动因子独立、耦合作用及区域特征,为后续精准施策提供理论依据。方法:采用网络调查法在我国东、中、西部各选一个省会城市展开调查,运用地理探测器和ESDA对社区韧性驱动因子进行探测并比较区域异质性特征。结果:政府公信力(0.322)、应急物品准备情况(0.297)是社区韧性的主导因子;任意两因子耦合对社区韧性的解释力均显著增强;各驱动因子对社区韧性的影响在空间上存在差异性,且“风险意识”“政府公信力”“家庭应急计划”是造成区域异质性的关键因子。结论:重大传染病疫情冲击下的社区韧性是多因子耦合作用的结果,多因子耦合对社区韧性的复杂影响需要多部门、多阶段、多机制因地制宜采取差异化的举措,全方位提升社区韧性。

企业异质、地区异质与多要素流动

本文将企业异质、地区异质与多要素流动统一到新经济地理学的分析框架中,发展了企业异质的新经济地理学模型。研究结果表明,当满足产业集聚的强条件时,企业分布异质性的增强强化了产业集聚的“黑洞条件”,反之,则会强化产业的分散力;区域间异质性增大导致区域均衡发展越来越困难。如果区域间的初始禀赋条件相同,则对称结构可能是一种稳定均衡结构,这时对称结构稳定对应的贸易自由度范围最宽。反之,如果区域间的初始禀赋条件不同,这时内部一般非对称结构可能成为一种稳定均衡结构,但这时对应的贸易自由度范围有所变窄;在区域异质性的条件下,内部非对称结构可能是一种稳定均衡结构,当地区同质时,该内部非对称结构就会演变为对称稳定均衡结构,并且,存在多重稳定均衡结构的现象。随着贸易自由度的提高,集聚是可能的,也是渐进的,这一过程会变得可逆。