Modeling of Spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression Model

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.

Comparison of Uniform and Kernel Gaussian Weight Matrix in Generalized Spatial Panel Data Model

Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover effect of correlation between locations. Value of ρ or λ will influence the goodness of fit model, so it is important to make parameter estimation. The effect of another location is covered by making contiguity matrix until it gets spatial weighted matrix (W). There are some types of W—uniform W, binary W, kernel Gaussian W and some W from real case of economics condition or transportation condition from locations. This study is aimed to compare uniform W and kernel Gaussian W in spatial panel data model using RMSE value. The result of analysis showed that uniform weight had RMSE value less than kernel Gaussian model. Uniform W had stabil value for all the combinations.

A geographical similarity-based sampling method of non-fire point data for spatial prediction of forest fires

Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires.Non-fire point data are important training data for constructing a model,and their quality significantly impacts the prediction performance of the model.However,non-fire point data obtained using existing sampling methods generally suffer from low representativeness.Therefore,this study proposes a non-fire point data sampling method based on geographical similarity to improve the quality of non-fire point samples.The method is based on the idea that the less similar the geographical environment between a sample point and an already occurred fire point,the greater the confidence in being a non-fire point sample.Yunnan Province,China,with a high frequency of forest fires,was used as the study area.We compared the prediction performance of traditional sampling methods and the proposed method using three commonly used forest fire risk prediction models:logistic regression(LR),support vector machine(SVM),and random forest(RF).The results show that the modeling and prediction accuracies of the forest fire prediction models established based on the proposed sampling method are significantly improved compared with those of the traditional sampling method.Specifically,in 2010,the modeling and prediction accuracies improved by 19.1%and 32.8%,respectively,and in 2020,they improved by 13.1%and 24.3%,respectively.Therefore,we believe that collecting non-fire point samples based on the principle of geographical similarity is an effective way to improve the quality of forest fire samples,and thus enhance the prediction of forest fire risk.

Panel data models with cross-sectional dependence: a selective review

In this review, we highlight some recent methodological and theoretical develop- ments in estimation and testing of large panel data models with cross-sectional dependence. The paper begins with a discussion of issues of cross-sectional dependence, and introduces the concepts of weak and strong cross-sectional dependence. Then, the main attention is primarily paid to spatial and factor approaches for modeling cross-sectional dependence for both linear and nonlinear （nonparametric and semiparametric） panel data models. Finally, we conclude with some speculations on future research directions.

Effect of FDI on China's environmental pollution: Evidence based on spatial panel data

It is clearly stated in the 19th people's congress that we should make the environmental protection as our national policy. Therefore, it is of great importance to study this issue. This article is going to consider 30 provinces of China as the cross-section, and utilize the data sample from 2006 to 2015 of these cross-sections to formulate a Spatial Panel Data Durbin Model to analyze the effect of FDI. By using these data, this article creates a comprehensive environmental pollution index with the help of entropy. The result indicates that the effect of FDI on environment has a non-linear and spatial spillover characteristic. Before reaching the critical value, FDI has a negative effect on environment; however, with the accumulation of FDI, it will create a significant positive effect on the environment.

Comparison of Geographically Weighted Regression of Benthic Substrate Modeling Accuracy on Large and Small Wadeable Streams

Aquatic habitat assessments encompass large and small wadeable streams which vary from many meters wide to ephemeral. Differences in stream sizes within or across watersheds, however, may lead to incompatibility of data at varying spatial scales. Specifically, issues caused by moving between scales on large and small streams are not typically addressed by many forms of statistical analysis, making the comparison of large (>30 m wetted width) and small stream (<10 m wetted width) habitat assessments difficult. Geographically weighted regression (GWR) may provide avenues for efficiency and needed insight into stream habitat data by addressing issues caused by moving between scales. This study examined the ability of GWR to consistently model stream substrate on both large and small wadeable streams at an equivalent resolution. We performed GWR on two groups of 60 randomly selected substrate patches from large and small streams and used depth measurements to model substrate. Our large and small stream substrate models responded equally well to GWR. Results showed no statistically significant difference between GWR R2 values of large and small stream streams. Results also provided a much needed method for comparison of large and small wadeable streams. Our results have merit for aquatic resource managers, because they demonstrate ability to spatially model and compare substrate on large and small streams. Using depth to guide substrate modeling by geographically weighted regression has a variety of applications which may help manage, monitor stream health, and interpret substrate change over time.

Employment effect of China's environmental regulation: Evidence based on spatial panel data

This article considers 30 provinces of China as the cross-section subjects, and utilizes the data sample from 2009 to 2015 of these cross-sections to formulate a Spatial Panel Data Durbin Model to analyze the effect of environmental regulation on employment. The result indicates that environmental regulation has negative effect on employment with the consideration of spatial spillover effect, and this adverse effect is not significant mathematically. With the enhance of environmental regulation, the negative impact on employment will decrease accordingly, even may eventually promote job growth, which means there may be a non-linear relationship between them. Specifically, the direct effect of environmental regulation on employment indicates that it is beneficial for job growth whereas the indirect effect illustrate that it is detrimental for employment.

教育支出的产出效应研究——基于空间Panel Data与菲德模型的数量分析

考虑到我国省际经济增长的空间溢出性和空间相关性,将空间Panel Data模型和菲德模型结合起来研究教育支出的产出效应,这种方法能在考虑空间相关性的基础上研究教育支出的部门溢出,从而对教育支出的产出效应得到更准确的估计。研究结果显示省际经济增长之间存在显著的空间相关性,而空间Panel Data模型的实证结果也表明,如果不考虑空间相关性会夸大教育支出的部门溢出。

房价影响因素的空间非一致性与差异化调控手段——基于Panel Data模型的实证研究

房地产价格受多种宏观经济因素的综合影响,不同城市的房价决定因素可能存在差异。文章将全国城市划分为四种级别,并选择17个一、二、三线样本城市,以货币供应量、CPI、GDP、城镇居民家庭人均可支配收入和社会固定资产投资额为解释变量,选取2002-2012年的季度数据,构建Panel Data模型,研究房价影响因素的空间非一致性,研究结果证明了空间非一致性的存在。基于此,对一、二、三线城市分别提出了差异性调控手段建议。

基于空间Panel data固定效应模型的人口增长影响分析

运用空间面板数据分析方法考查了城镇化水平和人均GDP对人口增长的问题。首先介绍了面板数据在分析中的优越性,并阐述了面板数据模型的空间相关性检验。在此基础上,通过选用省级2003—2012年的Panel观测数据,对变量的面板数据进行平稳性检验,考查了变量的空间相关性。最后结合空间面板数据,运用固定效应法,估计了中国各省城镇化水平、人均GDP对人口增长的影响。经过实证检验,表明各省人口增长存在空间相关性,并概括了主要研究结论。

Utilization of Open Source Spatial Data for Landslide Susceptibility Mapping at Chittagong District of Bangladesh—An Appraisal for Disaster Risk Reduction and Mitigation Approach

Since creation of spatial data is a costly and time consuming process, researchers, in this domain, in most of the cases rely on open source spatial attributes for their specific purpose. Likewise, the present research aims at mapping landslide susceptibility at the metropolitan area of Chittagong district of Bangladesh utilizing obtainable open source spatial data from various web portals. In this regard, we targeted a study region where rainfall induced landslides reportedly causes causalities as well as property damage each year. In this study, however, we employed multi-criteria evaluation (MCE) technique i.e., heuristic, a knowledge driven approach based on expert opinions from various discipline for landslide susceptibility mapping combining nine causative factors—geomorphology, geology, land use/land cover (LULC), slope, aspect, plan curvature, drainage distance, relative relief and vegetation in geographic information system (GIS) environment. The final susceptibility map was devised into five hazard classes viz., very low, low, moderate, high, and very high, representing 22 km2 (13%), 90 km2 (53%);24 km2 (15%);22 km2 (13%) and 10 km2 (6%) areas respectively. This particular study might be beneficial to the local authorities and other stake-holders, concerned in disaster risk reduction and mitigation activities. Moreover this study can also be advantageous for risk sensitive land use planning in the study area.

Temporal and spatial responses of ecological resilience to climate change and human activities in the economic belt on the northern slope of the Tianshan Mountains, China

In the Anthropocene era,human activities have become increasingly complex and diversified.The natural ecosystems need higher ecological resilience to ensure regional sustainable development due to rapid urbanization and industrialization as well as other intensified human activities,especially in arid and semi-arid areas.In the study,we chose the economic belt on the northern slope of the Tianshan Mountains(EBNSTM)in Xinjiang Uygur Autonomous Region of China as a case study.By collecting geographic data and statistical data from 2010 and 2020,we constructed an ecological resilience assessment model based on the ecosystem habitat quality(EHQ),ecosystem landscape stability(ELS),and ecosystem service value(ESV).Further,we analyzed the temporal and spatial variation characteristics of ecological resilience in the EBNSTM from 2010 to 2020 by spatial autocorrelation analysis,and explored its responses to climate change and human activities using the geographically weighted regression(GWR)model.The results showed that the ecological resilience of the EBNSTM was at a low level and increased from 0.2732 to 0.2773 during 2010–2020.The spatial autocorrelation analysis of ecological resilience exhibited a spatial heterogeneity characteristic of"high in the western region and low in the eastern region",and the spatial clustering trend was enhanced during the study period.Desert,Gobi and rapidly urbanized areas showed low level of ecological resilience,and oasis and mountain areas exhibited high level of ecological resilience.Climate factors had an important impact on ecological resilience.Specifically,average annual temperature and annual precipitation were the key climate factors that improved ecological resilience,while average annual evapotranspiration was the main factor that blocked ecological resilience.Among the human activity factors,the distance from the main road showed a negative correlation with ecological resilience.Both night light index and PM2.5 concentration were negatively correlated with ecological resilience in the areas with better ecological conditions,whereas in the areas with poorer ecological conditions,the correlations were positive.The research findings could provide a scientific reference for protecting the ecological environment and promoting the harmony and stability of the human-land relationship in arid and semi-arid areas.

考虑空间异质性的短距离上学方式选择机理

短距离出行是学生日常出行的重要组成部分,合理引导短距离上学群体交通方式选择对于促进学生健康发展和缓解高峰期间道路交通拥堵具有重要意义.本研究以中国南京市为例,利用贝叶斯分类法识别出南京市学生的短距离出行阈值为2.7 km,在此基础上,分别构建多项logit(multinomial logit,MNL)模型与地理加权多项logit(geographically weighted multinominal logit,GWMNL)模型,探讨个人属性、出行特征、家庭特征及建成环境对短距离学生群体上学方式选择的影响及其空间异质性.结果表明,相比MNL模型,GWMNL模型具有更好的拟合度和解释能力,说明学生上学交通方式选择行为因居住位置不同存在明显差异.与居住在学校附近的低龄学生相比,核心区与近郊区较短出行距离的高龄学生更倾向于乘坐小汽车上学,而远郊区(六合区和江宁区)的高龄学生更倾向于选择积极出行(步行和自行车)和电动自行车上学.小汽车数量对学生选择积极出行、电动自行车及公共交通上学均有消极影响,且这种消极影响从市中心向外围区域逐渐增强.最近公交站距离对居住在核心区与近郊区的学生选择公共交通具有明显促进作用,而对部分远郊地区的学生选择公共交通具有抑制作用,且在南京市的北部和东部地区尤为明显.研究结果可为不同区域学生群体短距离积极出行引导策略的制定提供参考.

服务业产业多样化对城市经济韧性的影响——来自地级市夜间灯光数据的证据

本文利用夜间灯光栅格数据,测算了2008—2019年我国281个地级市的经济韧性,并研究了服务业产业多样化类型对城市经济韧性的影响。研究发现:服务业产业无关多样化程度越高的城市经济韧性相对越强,而相关多样化对城市经济韧性具有部分负向作用。多种检验表明上述结果稳健。不同城市规模和区域一体化程度的产业多样化与城市经济韧性关系具有一定区别,但总体上同质性大于异质性。利用中介效应检验进行作用机制分析后发现,服务业无关多样化可通过提高服务业就业水平和产出水平提高城市的经济韧性。因此,城市经济规划建设中应将提升经济韧性作为重要考虑因素,结合服务业产业多样化特征因地制宜,根据城市规模适度提高城市产业多样化。

湖南省城镇土地利用多样性对人口密度的影响研究

城镇人口密度降低已经成为制约部分城镇发展的重要因素之一。选取湖南省为案例区,在定量刻画湖南省城镇人口密度与土地利用多样性格局基础上,运用地理加权回归模型探索两者的关系,并构建压力-状态-响应模型探索湖南省城镇土地利用多样性对人口密度的影响路径。结果表明:①湖南省城镇人口密度、城镇建设用地土地利用多样性均存在明显的空间正相关性,但聚集趋势相反;②单一化的城镇建设用地利用方式既不利于湖南省城镇土地利用多样性的提升,也不利于湖南省城镇人口密度的提升,工业布局可以引导湖南省城镇人口密度的提升,注重经济发展、提高城镇公共服务能力是提高城镇土地利用多样性进而提高城镇人口密度的有效方法;③湖南省二、三产业增加值与城镇人口密度呈现弱负相关且呈现“南高北低”的格局;④湖南省城镇居民可支配收入与城镇人口密度的相关性呈现出“西北高、东南低”的格局,并在长沙市中心城区一带出现正相关与负相关的分界。最后,从优化城镇职能体系对应对历史遗留问题、促进人口密度有序提升方面提出了政策建议。

铁路网络联系对城市土地绿色利用效率的影响研究——以长三角地区为例

研究目的:探究铁路网络联系对城市土地绿色利用效率的影响及空间溢出效应,为依托铁路交通发展促进土地绿色利用效率提升提供科学依据。研究方法:超效率SBM模型,社会网络分析方法,空间面板模型。研究结果:(1)长三角城市铁路联系强度和土地绿色利用效率均呈现显著的增长趋势,土地绿色利用效率存在着较为明显的区域差异。(2)铁路联系强度的提高促进了本城市土地绿色利用效率的提升,与综合铁路联系相比,高铁联系对本城市土地绿色利用效率的提升作用更加明显。(3)铁路联系的加强促进了本城市产业结构合理化水平的提升和创新产出的增长,进而对土地绿色利用效率产生影响。高铁联系在促进本城市产业结构合理化水平提升和创新产出增长方面的作用更加明显。(4)城市对外铁路联系强度的提高产生了负向的空间溢出效应,抑制了邻近城市土地绿色利用效率的提升。研究结论:应充分发挥铁路建设在优化产业结构、促进创新方面的作用,依托铁路网络加强区域内经济技术合作、发挥各城市比较优势,以推动区域土地绿色利用效率的整体性提升。

扩容兼区划调整过程中城市群经济韧性引力演化及影响因素研究--以长三角城市群为例

为了分析城市群在扩容兼区划调整过程中经济韧性的格局演化及影响因素,以长三角城市群为研究对象,利用熵权法、引力模型和空间面部杜宾模型对其进行分析。结果表明:扩容过程实现了城市群经济韧性的整体提升,扩容中经历的巢湖区划调整有利于促进合肥都市圈的发展;长三角城市群经济韧性引力整体实现迅速地升级,超强引力范围不断扩大,高强度引力大部分分布在长三角城市群东部地区;长三角城市群经济韧性存在显著的正向溢出效应,医疗条件近期对城市群经济韧性的影响强度更大,而就业保障和物流规模目前则是阻碍了长三角城市群经济韧性的提升。

四川省县域数字乡村发展水平的地域特征与影响因素

以北京大学新农村发展研究院发布的2020年县域数字乡村指数作为测度指标,采用空间自相关分析、半变异函数、地理加权回归、地理探测器等方法,研究四川省172个县域数字乡村发展水平的空间分异特征及影响因素。结果表明:(1)四川省县域数字乡村发展水平整体偏低,空间分异格局明显,呈现出以成都平原为核心高值区、自东向西递减的趋势。(2)数字乡村发展水平的空间关联性较强,显著高-高区主要分布在成都平原及其周围,如德阳、眉山的县域;显著低-低区主要在川西北及攀西地区分布,如阿坝、甘孜、凉山的县域。(3)数字基础设施指数和治理数字化指数的地域分异受空间相关性影响的比重大,经济数字化指数和生活数字化指数的地域分异主要受随机成分影响。(4)从回归系数平均数的绝对值看,影响四川省县域数字乡村发展水平的因素由大到小为农村金融、收入水平、互联网发展水平、人口受教育水平、政府支持和交通条件。并且这些因素不是独立、直接作用于数字乡村,而是各因素两两交互作用。

耕地“非粮化”影响因素空间效应研究——以珠三角为例

为科学管控耕地非粮化,本研究以珠三角县级行政区为研究单元,在揭示耕地非粮化空间分异特征基础上,采用空间杜宾模型和地理加权回归模型探究耕地非粮化及其空间效应。结果表明:珠三角2019年各县平均非粮化率为47.8%,高于全国平均水平。从非粮化率来看,耕地非粮化集聚于珠三角周边县域及部分中部县域,以低-低和高-高集聚为主;从非粮化面积来看,耕地非粮化集聚于研究区东北部,以高-高集聚为主。珠三角非粮化存在空间依赖性。从直接效应看,第一产业GDP占比、到市中心的距离与非粮化呈负相关,劳均耕地面积、有效耕地灌溉面积与非粮化呈正相关;从溢出效应看,人均GDP与非粮化呈正相关。第一产业GDP占比和有效耕地灌溉面积对非粮化的影响均呈现中部高、周边低的空间异质性特征。研究表明,经济发展水平较高区域更易产生“非粮化”,非粮化治理应当因地制宜、分级整治,坚决落实“非粮化”管理政策,提高种粮收益和粮食综合生产力,促进粮农降本增效。

基于空间面板数据模型的拱坝变形缺失数据处理

变形监测信息缺失会对拱坝安全性态的分析造成困难甚至引起误判,需要采用科学合理的方法对缺失数据进行处理,从而获取完整可靠的监测数据。传统的缺失值处理方法仅考虑了拱坝测点的局部空间关联性,而拱坝变形整体性较强,在进行缺失值插补时有必要考虑拱坝所有测点间的时空关联性。鉴于此,从拱坝的整体性和测点的空间相关性出发,首先引入空间权重矩阵,证明拱坝变形具有显著的空间正自相关性;在此基础上,基于空间面板数据模型提出一种考虑拱坝整体时空关联性的变形缺失数据处理方法;最后结合某拱坝工程实例,验证了所构建模型的有效性。工程实例表明:该方法插补残差值低于SL 601—2013《混凝土坝安全监测技术规范》所规定的误差限值,具有较高的插补精度,可对变形监测缺失数据进行有效处理。