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modeling of spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression model

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Chinese Geographical Science 2014年第2期24卷 191-204页

作者： ZHANG Haitao GUO Long CHEN Jiaying FU Peihong GU Jianli LIAO Guangyu College of Resources and Environment Huazhong Agricultural University

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 *** 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 *** 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 *** coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical *** 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 *** 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.

关键词： spatial lag model spatial error model geographically weighted regression model global spatial autocorrelation local spatial aurocorrelation

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Analyzing Influencing Factors of Rural Poverty in Typical Poverty Areas of Hainan Province: A Case Study of Lingao County

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Chinese Geographical Science 2018年第6期28卷 1061-1076页

作者： ZHANG Jinping ZUO Feng ZHOU Yanmei ZHAI Mengxiao MEI Lin FU Yidi CHENG Yeqing College of Geography and Environmental Sciences Hainan Normal University College of International Education Hainan Normal University

Rural poverty and poverty reduction are not only the focal issues that have attracted worldwide attention, but also the vital issues on people's livelihood that has attached great importance and aimed to be solved by the central and local governments of China. Based on the survey data of 354 farming households, this paper, taking the national poverty county of Lingao County, Hainan Province for an example, examined the characteristics of rural poverty of the county. Moreover, this paper established the spatial lag model(SLM) from five dimensions, namely, status of the household head, household structure, health status, income composition and traffic accessibility, to analyze the main influencing factors of rural poverty according to the values of Moran's I and the diagnosis of spatial dependence of the OLS model. It is found that the poor farming households gathered mainly in five towns in the north and southwest of the county, and the rural poverty have the characteristics of low educational level of the heads, more minor children, high population of farming peasants, high incidence of disease and low proportion of household wage-equivalent income. The results also showed that the variables such as the number of minor children, the number of migrant worker, the number of farming peasants and the proportion of wage-equivalent income have significant effectiveness on rural poverty, while the status of the household head, health status and traffic accessibility have little influence. It is an important way to realize the goal of poverty alleviation by controlling the number of farmers' fertility, strengthening the vocational skills training of farmers, vigorously developing specialization and large-scale agriculture and increasing the employment opportunities of farmers.

关键词： rural poverty targeted poverty alleviation spatial lag model Lingao County China

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Exploring metro vibrancy and its relationship with built environment:a cross-city comparison using multi-source urban data

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Geo-spatial Information Science 2022年第2期25卷 182-196页

作者： Wei Tu Tingting Zhu Chen Zhong Xiaohu Zhang Yang Xu Qingquan Li Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and ServiceSchool of Architecture and Urban PlanningShenzhen UniversityShenzhenChina Ministry of Natural Resources(MNR)Key Laboratory for Geo-Environmental Monitoring of Great Bay Area Shenzhen UniversityShenzhenChina The Bartlett Centre for Advanced spatial Analysis(CASA) University College London(UCL)LondonUK Department of Urban Planning and Design The University of Hong KongHong KongChina Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityHong Kong SARChina

Recent urban transformations have led to critical reflections on the blighted urban infrastruc-tures and called for re-stimulating vital urban ***,the metro has been recognized as the backbone infrastructure for urban mobility and the associated economy *** date,limited research has been devoted to investigating the relationship between metro vitality and built environment in mega-cities *** paper presents a multisource urban data-driven approach to quantify the metro vibrancy and its association with the underlying built *** smart card data is processed to extract metro ridership,which denotes the vibrancy around the metro station in physical *** media check-ins are crawled to measure the vitality of metros in virtual *** physical and virtual vibrancy are integrated into a holistic metro vibrancy metric using an entropy-based weighting *** built environment characteristics,including land use,transportation and buildings are modeled as independent *** significant influences of built environ-mental factors on the metro vibrancy are unraveled using the ordinary least square regression and the spatial lag *** experiments conducted in Shenzhen,Singapore and London,this study comes up with a conclusion that spatial distributions of metro vibrancy metrics in three cities are spatially *** regression analysis suggests that in all the three cities,more affluent urban areas tend to have higher metro virbrancy,while the road density,land use and buildings tend to impact metro vibrancy in only one or two *** results demonstrate the relationship between the metro vibrancy and built environment is affected by complex urban *** findings help us to understand metro vibrancy thus make proper policy to re-stimulate the important metro infrastructure in the future.

关键词： Urban vibrancy spatial-autocorrelation spatial lag model smart card data social media

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Is the “One Province One Rate” premium policy reasonable for Chinese crop insurance? The case in Jilin Province

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Journal of Integrative Agriculture 2018年第8期17卷 1900-1911页

作者： ZHOU Xian-hua LIAO Pu WANG Ke China Institute for Actuarial Science Central University of Finance and Economics Agricultural Information Institute Chinese Academy of Agricultural Sciences

Crop insurance in China is currently adopting the premium pricing strategy of "One Province One Rate", which appears to be in line with the systematic risk characteristics within crop insurance. This research aims to investigate the theoretical rationalization of this pricing strategy and its implications using the spatial lag model and the county-level data from the 45 corn plant counties of Jilin Province, China. Results corroborate that:(1) the spatial spillover effect of the corn yield risk is significant in Jilin but decreases rapidly when the risk unit includes more than eight counties; and(2) separating Jilin Province into eight risk zones for corn insurance will significantly reduce the high cross-subsidy phenomenon arising from the "One Province One Rate" strategy and shall benefit poor peasants in the region as well. This paper not only proves the existence of a systematic risk of crop insurance but also reveals that the spatial correlation and systemic features of the crop yield risk do not create a solid foundation for the current pricing strategy of "One Province One Rate". These conclusions will undoubtedly provide important references and empirical evidence for the role of China’s crop insurance in poverty alleviation.

关键词： crop insurance premium spatial correlation spatial lag model

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Assessment of river basin habitat quality and its relationship with disturbance factors: A case study of the Tarim River Basin in Northwest China

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Journal of Arid Land 2022年第2期14卷 167-185页

作者： HE Bing CHANG Jianxia GUO Aijun WANG Yimin WANG Yan LI Zhehao State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China Xi'an University of TechnologyXi'an 710048China

The status of regional biodiversity is determined by habitat *** effective assessment of habitat quality can help balance the relationship between economic development and biodiversity ***,this study used the InVEST model to conduct a dynamic evaluation of the spatial and temporal changes in habitat quality of the Tarim River Basin in southern Xinjiang Uygur Autonomous Region of China by calc ulating the degradation degree levels for habitat types that were caused by threat factors from 1990 to 2018(represented by four periods of 1990,2000,2010 and 2018).Specifically,we used spatial autocorrelation analysis and Getis-Ord Gi*analysis to divide the study area into three heterogeneous units in terms of habitat quality:cold spot areas,hot spot areas and random *** index,population density,gross domestic product(GDP),altitude and distance from water source(DWS)were then chosen as the main disturbance *** correlation and spatial regression models were subsequently used to analyze the influences of disturbance factors on habitat *** results demonstrated that the overall level of habitat quality in the TRB was poor,showing a continuous degradation *** intensity of the negative correlation between habitat quality and Hemeroby index was proven to be strongest in cold spot areas,hot spot areas and random *** spatial lag model(SLM)was better suited to spatial regression analysis due to the spatial dependence of habitat quality and disturbance factors in heterogeneous *** analyzing the model,Hemeroby index was found to have the greatest impact on habitat quality in the studied four periods(1990,2000,2010 and2018).The research results have potential guiding significance for the formulation of reasonable management policies in the TRB as well as other river basins in arid areas.

关键词： habitat quality biodiversity InVEST model spatial heterogeneity spatial lag model human activities Tarim River Basin

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Study on Influencing Factors of Education Investment Based on spatial Econometrics Analysis

Study on Influencing Factors of Education Investment Based o...

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2012 International Conference on Earth Science and Remote Sensing(ESRS 2012)

作者： Gong Siyi Lv Kangjuan Wu Tao Shanghai University

Based on the spatial Data Analysis, this paper constructs spatial econometric model of the factor influencing the government education investment to do an empirical study to the education investment level of China’s 31 provinces. Through the study, it is found that: the education of the local government in a certain space has the spatial dependence and spillover effects between provinces. Local government expenditure and the school-age population which have contributed significantly to the local education expenditure are the core variables of the government investment in education for government investment in education. The impact of industrial structure on government investment in education is a little more complex: the initial investment in the industrial structure has little influence on education;and current data on investment of government education has some influence, but is not significant.

关键词： spatial Econometrics Analysis Education Investment spatial lag model spatial Error model

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分布未知情况下的空间滞后模型检验

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统计与精算 2011年第4期 93-98页

作者：张进峰

在扰动项分布未知的情况下，直接采用传统的空间模型检验方法是存在问题的。针对传统空间模型检验方法的不足，本文以Lee和Yu(2010)的研究为基础，采用Lee和Liu(2006)提出的最优矩条件，构造分布未知情况下空间滞后模型的稳健检验统计... 详细信息

在扰动项分布未知的情况下，直接采用传统的空间模型检验方法是存在问题的。针对传统空间模型检验方法的不足，本文以Lee和Yu(2010)的研究为基础，采用Lee和Liu(2006)提出的最优矩条件，构造分布未知情况下空间滞后模型的稳健检验统计量。这种检验方法仅需参数的一致估计量，便于计算。蒙特卡罗结果表明，在小样本情况下，本文提出的检验有良好的性质，且明显优于Saavedra(2003)提出的检验。

关键词：空间滞后模型 MLE估计 GMM估计 C(α)检验 spatial lag model MLE GMM C(α) Test

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城市化、空间溢出与技术创新

区域与城市经济

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区域与城市经济 2018年第3期

作者：鲁元平王品超朱晓盼

本文基于空间计量模型，利用中国264个地级市的面板数据，研究了城市化对技术创新的影响。结果发现：城市化对技术创新有显著的促进作用；空间自回归系数显著为正，说明创新的空间溢出效应存在：城市化过程带来的人力资本集聚、科教支... 详细信息

本文基于空间计量模型，利用中国264个地级市的面板数据，研究了城市化对技术创新的影响。结果发现：城市化对技术创新有显著的促进作用；空间自回归系数显著为正，说明创新的空间溢出效应存在：城市化过程带来的人力资本集聚、科教支出增加及创新的空间溢出效应是促进技术创新水平的重要原因。这可以解释为城市化过程带来的人力资本、教育资源及企业在城市的集聚，城市化过程的知识的溢出和要素的流动是创新的空间溢出效应产生的内在机理。

关键词：城市化技术创新空间溢出空间滞后模型 urbanization technological innovation spatial spillover spatial lag model

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服务业集聚与区域经济增长

区域与城市经济

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区域与城市经济 2012年第7期

作者：陈立泰梁乐张祖妞

采用物质资本集聚度、人力资本集聚度以及就业密度衡量服务业集聚水平，运用空间计量方法，研究服务业集聚对区域经济增长的影响，结果显示：我国区域经济增长呈现明显的空间集聚特征；服务业物质资本集聚水平对区域经济发展的促进作用... 详细信息

采用物质资本集聚度、人力资本集聚度以及就业密度衡量服务业集聚水平，运用空间计量方法，研究服务业集聚对区域经济增长的影响，结果显示：我国区域经济增长呈现明显的空间集聚特征；服务业物质资本集聚水平对区域经济发展的促进作用呈弱化之势，人力资本集聚度的区域经济增长效应也由正转向负，而服务业就业密度、经济开放水平等因素发挥着越来越重要的作用。应该注重加强相邻地区间的交流与合作，形成经济互助，协调发展；同时应提高我国服务业的集聚度，充分发挥其对区域经济增长的正向促进作用。

关键词：服务业集聚物质资本集聚度人力资本集聚度就业密度区域经济增长空间滞后模型空间误差模型 service industry agglomeration physical capital agglomeration human capital agglomeration employment density regional economic growth spatial lag model spatial error model

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铁路交通基础设施对居民家庭消费结构升级的影响

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贸易经济 2020年第5期

作者：刘玉飞常晓坤张期陈

梳理了铁路交通基础设施对消费结构转型的影响机制，并运用中国2000-2014年省级面板数据构建空间计量模型进行实证研究。研究结果显示：铁路交通基础设施主要通过增加家庭文教娱乐消费支出占比来促进居民家庭消费结构由生活型向发展型... 详细信息

梳理了铁路交通基础设施对消费结构转型的影响机制，并运用中国2000-2014年省级面板数据构建空间计量模型进行实证研究。研究结果显示：铁路交通基础设施主要通过增加家庭文教娱乐消费支出占比来促进居民家庭消费结构由生活型向发展型和享受型转变；样本期间内，铁路交通基础设施对医疗保健消费支出占比以及交通通信消费支出占比的影响不显著。由此可见，在经济转型阶段，合理布局铁路交通路线，优化铁路密度能进一步释放居民家庭的消费潜力，激发居民家庭消费结构的转型，从而实现经济的可持续增长。

关键词：铁路交通基础设施消费结构升级空间滞后模型空间误差模型 railway transportation infrastructure upgrading of consumption structure spatial lag model spatial error model

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