中国制造业全要素生产率时空演变及影响因素

提高制造业全要素生产率是实现中国制造业高质量发展的重要途径。运用超效率SBM模型、标准差椭圆、空间自相关、地理加权回归等方法实证分析了2002—2016年中国制造业全要素生产率的时空演变特征及影响因素。结果显示：① 2002—2016年中国制造业全要素生产率总体呈波动上升趋势,由2002年的0.58上升至2016年的0.80;② 中国制造业全要素生产率的重心整体呈“西南—东北—西北”的变化格局,移动距离和速度逐渐缩小。标准差椭圆主要位于中国中东部地区,转角呈“缩小—增大—再缩小”的趋势,由偏东北—偏西南方向向正北—正南方向不断偏移,在偏东北—偏西南方向的空间分布趋于分散,在偏东南—偏西北方向的空间分布趋于极化;③ 中国制造业全要素生产率的正向空间相关关系逐渐减弱,高—高集聚区主要集中于江苏、上海等华东地区,低—低集聚区主要集中于陕西、甘肃、四川等地区;④ 中国制造业全要素生产率影响因素存在显著的空间异质性特征,金融发展是制造业全要素生产率提高的主要因素,对外开放、产业集聚和交通基础设施是制造业全要素生产率提高的重要正向影响因素,信息化水平阻碍了制造业全要素生产率的提高。     

土地经济生态位变化下的城乡空间景观格局表征——以广东省惠州市为例

土地利用变化对生物多样性和生态过程有着深刻的影响,与城乡景观格局变化密切相关。尝试将土地利用变化驱动力——土地经济生态位的理论和方法引入到景观生态学当中,为城乡空间景观格局空间表征提供新的方法。通过选取表征景观格局指标的破碎化指数、分形维数,利用Envi4.8、Arcmap10.0,Fragstasts4.1等软件,以广东省惠州市为例,在分析土地经济生态位与景观类型、景观指数之间的关系基础上,对土地经济生态位变化下的城乡空间景观格局进行表征。结果表明：不同景观土地面积数量的增减与该类型土地经济生态位升降具有一致性的规律,土地经济生态位上升,相应的景观土地面积增大,反之则减小;土地经济生态位越小,表征景观格局的景观破碎化指数、分维数指数越大,破碎化指数与分维数指数的增减与土地经济生态位升降表现出一致性的规律,土地经济生态位上升,相应的破碎化指数、分维数指数则增大,反之则减小;城乡空间景观格局变化的发生主要出现在土地经济生态位高的区域。土地经济生态位的变化是城乡景观格局变化的重要影响因子,能够很好解释城乡空间景观格局特征。     

基于时空窗口改进的时空加权回归分析——以湖北省黄石市住房价格为例

针对地理加权回归(GWR)模型不能有效处理样本数据空间自相关性这一问题,构造局部时空窗口统计量,尝试改进时空加权回归(GTWR)模型。定义多时空窗口的概念,给出其选取、计算和验证方法;计算时空窗口包含的各样本点的被解释变量平均值,与样本拟合点的被解释变量值的比值,作为新的解释变量,构建改进的时空加权回归(IGTWR)模型。以土地稀缺、多中心、资源型城市——湖北省黄石市为例,收集2007~2012年商品住宅成交价格1.93万个数据和398个楼栋样本点,选取小区等级、绿化率、楼栋总层数、容积率、距区域中心距离和销售年份6个解释变量,分别利用常规线性回归(OLS)、GWR、GTWR和IGTWR方法进行回归分析。模型结果表明：计算Moran’s I指数和分析时间序列的自相关性,能确定时空窗口的大小和数量的选取;IGTWR模型和各变量的回归统计均通过0.05的显著性水平检验,有关解释变量的系数估计值在空间分布上能合理解释;GWR拟合结果优于OLS,GTWR优于GWR,而IGTWR拟合精度最好。与GTWR模型分析相比, IGTWR模型R²从0.877提升到0.919,而AICc、残差方(RSS)和均方差(MSE)分别从6 226、49 996 201和354.427下降到6 206、32 327 472和284.969。案例研究表明：IGTWR能够表达一定时空范围的时空自相关特征,减小了估计误差,提高了回归拟合精度。     

半干旱区生态风险时空演变及其影响因素的地理探测——以宁夏盐池县为例

深入探究半干旱区景观格局变化规律,明确其生态风险时空演变特征与影响因素,对优化半干旱区景观格局、防范和治理生态风险、促进区域可持续高质量发展具有重要意义。以宁夏回族自治区盐池县为例,基于2000—2020年土地利用数据,通过景观格局指数构建生态风险模型,结合地统计分析和地理探测器模型,对其景观格局变化及生态风险时空演变与影响因素进行综合研究。结果表明：（1） 2000—2020年盐池县耕地和未利用地面积下降,其余地类均呈增加趋势,其中建设用地扩张速度最快,年均增长率为6.90%。（2） 2000—2020年盐池县景观斑块数、斑块密度、景观形状指数和香浓多样性指数上升,最大斑块指数和蔓延度指数下降,表明景观格局呈破碎化、复杂化和分散化的发展态势。（3） 2000—2020年盐池县生态风险指数由0.1465下降至0.1312,其中高、较高和中等风险区面积占比分别下降5.25%、24.21%和5.44%,而低和较低风险区面积占比分别增加14.11%和20.79%,空间上呈现出北高南低的分布格局。（4） 随着社会需求的日益增长和人类活动的加剧,自然因素对生态风险空间分异格局的影响逐渐弱化,而社会经济因素作用不断增强。     

地表温度“源—汇”景观贡献度的影响因素分析

在中国快速城市化进程中,城市热岛是严重的环境问题之一。探究城市热岛的影响因素,建立与自然因子、社会经济因子、景观形态因子的关联,对解决中国“城市病”,实现可持续发展具有重要的意义。本文首先在全国尺度上基于地表温度“源—汇”景观指数识别地表温度的“源”景观/“汇”景观,在此基础上计算“源—汇”景观贡献度及其效应,分析其时空演变规律;其次,基于空间杜宾模型分析自然因子、社会经济因子和景观形态因子对“源—汇”景观贡献度的影响,结果发现：① 2005—2015年间,可缓解热岛效应的省份冬季较夏季多,冬季“强”和“较弱”景观贡献度比值区域集中分布在中国北部和南部;夏季“强”的“汇”与“源”景观贡献度比值区域集中分布在中国北部地区。② 对于本地因子,景观形态对“源”或“汇”景观贡献度的影响较大,缓解热岛效应将附近小“汇”斑块结合来降低地表温度“汇”景观的离散度;其次,夏季适当在“源”景观里增加相对湿度、并增加其植被覆盖率,严格控制“源”景观和“汇”景观的建筑密度及建筑高度,可达到最佳降温效果。③ 邻域因子中相对湿度和建筑密度的影响更大,景观形态中增加“源”景观的离散度及其边缘密度,或增加“汇”景观集聚度,简化其形状均有利于缓解邻域的热岛效应。     

基于逆序的城市数目与累积规模关系探讨

在解读区域城市体系规模结构特征的基础上,建立了用于分析逆向排序的城市累积规模与城市数目之间关系的模型。采用全国第六次人口普查数据,利用位序—规模法则与提出的逆序累积规模模型测度了2010年中国城市体系规模结构的省际差异及其空间分布特征,并对影响区域城市体系的地理因素进行了分析。研究发现：位序—规模法则在刻画区域城市体系规模特征方面存在一定的缺陷。对于明显存在首位分布、双中心城市、或者城市数量较少的城市体系描述不够准确,导致使用位序—规模法则得到的Q值和选取的地理变量之间没有必然的联系,在进行不同区域间城市体系结构比较研究中存在一定的局限性。整体来看,逆序累积规模模型的拟合效果要优于位序—规模法则。在使用位序—规模法则拟合效果不理想的情况下,逆序累积规模模型的普适性更强。β值与区域的经济总量、城市化率、人口密度以及复种指数存在较高的统计关系,在城市化进程中,应该根据各个区域的特征及其发展态势,探索与区域发展相适应的城市体系化道路。空间自回归模型与一般回归模型的统计结果表明：中国各省区城市体系的形成与演化在空间上联系很弱,基本上处于孤立发展的封闭状态,其原因可能与中国严格的户籍管理政策及地方保护主义有关。     

旅游扶贫重点村空间可达性分布特征及影响因素研究

在国家提出乡村振兴和旅游扶贫政策的大背景下,为了发挥旅游交通在区域发展中的带动作用,为旅游扶贫重点村建设和加快区域脱贫提供理论依据,文章以湖北武陵山区258个旅游扶贫重点村为研究样本,运用栅格成本加权距离算法与地理加权回归模型(GWR)对其旅游交通可达性及影响因素在空间上影响程度及作用方向的差异进行了探究。研究发现：湖北武陵山区内旅游扶贫重点村的平均可达时间为1.82 h,区域整体可达性相对较差,出行成本较高;从自然及人文2个视角对旅游扶贫重点村可达性的影响因子进行提取,各个影响因子在区域上作用大小的排序为：距最邻近县市最短路径>高程>周边旅游资源禀赋>距河流最短距离>距周边景区最短距离>坡度,距河流最短距离及周边旅游资源禀赋与可达成本呈负相关关系,其余因素均与可达成本呈正相关关系;距周边景区最短距离与坡度值大小的单位变化对可达成本空间差异的影响不显著。     

基于DLS模型的城市土地政策生态效应研究——以深圳市为例

以快速城市化典型地区——深圳市为例,结合土地利用变化与生态质量评价模型对城市土地利用的数量控制和空间控制两类生态政策的生态效应进行了评估。结果表明：(1) 在城市化发展过程中,由于城市用地扩张,城市生境面临破碎化增加、连通性降低、生境质量下降等生态风险,生态政策的实施可以在一定程度上缓解上述生态风险。(2) 数量控制的生态政策可以提高土地利用的集约性,遏制景观破碎化和生境质量下降趋势和增强景观连通性,但可能造成城市开发强度上升,对城市用地周边生态环境产生影响。(3) 空间控制的生态政策可以维护生态用地格局,遏制景观破碎化和生境质量下降趋势,但在维护景观连通性方面作用有限。(4) 两种政策同时使用会产生协同效应,其效果优于单独使用两种政策。     

延河流域景观格局与生态水文过程分析

延河流域作为典型的黄土高原丘陵沟壑区,水土流失是最主要的生态环境问题,其生态环境治理能为整个黄土高原生态恢复重建提供典型范例,因此评价延河流域景观格局变化对水文过程的影响,对流域景观格局与水文过程的研究具有重要的科学价值和现实意义。本文通过利用SWAT模型模拟的延河流域的41个子流域的水文响应单元,建立延河流域的“源—汇”水文响应单元景观格局指数,通过计算不同地形条件下水文响应单元随着坡度变化的面积累积百分比,构建了新的“地形—水文响应单元 (Slope-HRU) 综合景观指数”。并与SWAT水文模型所模拟获得的子流域径流量、产沙量进行相关性分析,评价“地形—水文响应单元 (Slope-HRU) 景观指数”对水文过程的表征能力和适用性。结果表明：在子流域尺度上Slope-HRU景观指数和产沙状况存在明显的区域分异规律,而且R值空间变异情况基本上与产沙量的空间变化规律相反,即R值呈现由南向北,由东向西递增,而产沙量由南向北,由东向西递减。R值与年产沙量在数值上呈显著相关关系,相关系数为-0.60;再将Slope-HRU景观指数与所选取的7个常用景观格局指数相结合,构成新的综合景观指数,发现它们与子流域的产沙量耦合关系明确,均显著负相关,相关系数绝对值范围为0.43~0.63。     

一种新的客源地集中指数(tourCI)与实证分析

客源地分布的集聚特征是客源市场空间结构研究的重要内容。针对现有客源集中指数不能横向对比及难以解释驱动因素的问题,论文提出了支持引入不同影响因子参数的客源地集中指数“tourCI”计算方法,建立了基于tourCI指数的客源地空间结构解释概念框架,给出tourCI的多维度描述方式和意义解析,以测算不同影响因素下的客源地分布集聚特征。以大理古城位置微博签到提取的客源数据进行实证研究,结果表明：基于政区维度的tourCI反映出云南省内本地客源对大理古城客源分布特征产生了重要影响,省外客源分布相对均衡;基于距离维度的tourCI刻画了大理古城客源分布随目的地距离的变化规律,由近及远呈现“弱—强—弱—均衡”的指数变化模式;经济维度tourCI显示来自一线城市的游客高度集中在经济水平较高地区,来自新一线城市的游客分布相对均衡,其余城市的客源分布受经济影响较大。理论与实证研究证明了tourCI指数对客源地空间结构集聚特征及驱动因素具有较好的解析能力,有助于丰富旅游地理研究的方法体系。     

Multi-scale analysis of spatially varying relationships between agricultural landscape patterns and urbanization using geographically weighted regression

Scientific interpretation of the relationships between agricultural landscape patterns and urbanization is important for ecological planning and management. Ordinary least squares (OLS) regression is the primary statistical method in previous studies. However, this global regression lacks the ability to uncover some local-specific relationships and spatial autocorrelation in model residuals. This study employed geographically weighted regression (GWR) to examine the spatially varying relationships between several urbanization indicators (urbanization intensity index, distance to urban centers and distance to road) and changes in metrics describing agricultural landscape patterns (total area, patch density, perimeter area ratio distribution and aggregation index) at two block scales (5 km and 10 km). Results denoted that GWR was more powerful than OLS in interpreting relationships between agricultural landscape patterns and urbanization, since GWR was characterized by higher adjust R², lower Akaike Information Criterion values and reduced spatial autocorrelations in model residuals. Character and strength of the relationships identified by GWR varied spatially. In addition, GWR results were scale-dependent and scale effects were particularly significant in three aspects: kernel bandwidth of weight determination, block scale of pattern analysis, and window size of local variance analysis. Homogeneity and heterogeneity in the relationships between agricultural landscape patterns and urbanization were subject to the coupled influences of the three scale effects. We argue that the spatially varying relationships between agricultural landscape patterns and urbanization are not accidental but nearly universal. This study demonstrated that GWR has the potential to provide references for ecological planners and managers to address agricultural landscapes issues at all scales.     

Modelling spatial heterogeneity and anisotropy: child anaemia,sanitation and basic infrastructure in sub-Saharan Africa

Public interventions in support of public health and housing in developing countries could benefit from better understanding of spatial heterogeneity and anisotropy. Estimation of directional variation within geographically weighted regression (GWR) faces problems of local parameter instability, border effects and, if extended to non- spatial attributes, potential endogeneity. This study formulates a GWR model where anisotropy is filtered out based on information from directional variograms. Along with classical regressions, the approach is applied to investigate child anaemia and its associations with household characteristics, sanitation and basic infrastructure in 173 regions of sub-Saharan Africa. Based on ordinary least squares (OLS) results, anaemia prevalence rates are up to three times more responsive to child morbidity (related to malaria and other diseases) than to other covariates. GWR estimates provide similar indications, but also point to poor sanitation facilities as a cofactor of severe anaemia particularly in east and southern Africa. The anisotropy-adjusted GWR is spatially stationary in residuals, and its estimated local parameters are less collinear than GWR with no adjustment. However, similar explanatory power and lack of significant bias in parameters estimated by the latter suggest that directional variation is largely captured by modelled co-movements among the variables.     

公众参与型环境规制的时空格局及驱动因子研究——以长江经济带为例

以长江经济带公众参与型环境规制为研究对象,运用空间分析方法研究其时空格局,通过地理探测器筛选其主导驱动因子,基于GWR（Geographically Weighted Regression）模型揭示主导驱动因子空间异质性。结果表明：① 各年份公众参与型环境规制均呈显著空间正相关,但集聚程度逐年下降;② 公众参与型环境规制空间分异格局基本稳定;③ 地理探测器剔除地均固定资产投资和万人在校大学生数两个初选因子;④ 公众参与型环境规制驱动因子作用力由大到小依次为环境风险、人地压力、排放强度、信息化水平、经济水平、产业结构;⑤ 各驱动因子存在特征不同的空间异质性,为制定针对性政策提供理论依据。     

Spatial non-stationary characteristics between grass yield and its influencing factors in the Ningxia temperate grasslands based on a mixed geographically weighted regression model

Spatial models are effective in obtaining local details on grassland biomass, and their accuracy has important practical significance for the stable management of grasses and livestock. To this end, the present study utilized measured quadrat data of grass yield across different regions in the main growing season of temperate grasslands in Ningxia of China (August 2020), combined with hydrometeorology, elevation, net primary productivity (NPP), and other auxiliary data over the same period. Accordingly, non-stationary characteristics of the spatial scale, and the effects of influencing factors on grass yield were analyzed using a mixed geographically weighted regression (MGWR) model. The results showed that the model was suitable for correlation analysis. The spatial scale of ratio resident-area index (PRI) was the largest, followed by the digital elevation model, NPP, distance from gully, distance from river, average July rainfall, and daily temperature range; whereas the spatial scales of night light, distance from roads, and relative humidity (RH) were the most limited. All influencing factors maintained positive and negative effects on grass yield, save for the strictly negative effect of RH. The regression results revealed a multiscale differential spatial response regularity of different influencing factors on grass yield. Regression parameters revealed that the results of Ordinary least squares (OLS) (Adjusted R² = 0.642) and geographically weighted regression (GWR) (Adjusted R² = 0.797) models were worse than those of MGWR (Adjusted R² = 0.889) models. Based on the results of the RMSE and radius index, the simulation effect also was MGWR > GWR > OLS models. Ultimately, the MGWR model held the strongest prediction performance (R² = 0.8306). Spatially, the grass yield was high in the south and west, and low in the north and east of the study area. The results of this study provide a new technical support for rapid and accurate estimation of grassland yield to dynamically adjust grazing decision in the semi-arid loess hilly region.     

GWR模型在土壤重金属高光谱预测中的应用

目前土壤重金属高光谱反演模型大多忽视了重金属与光谱变量间相关关系的空间异质性,这与实际情况不相吻合,而地理权重回归（GWR）模型能有效地揭示变量间关系的空间异质性。本文以福州市土壤重金属Cd、Cu、Pb、Cr、Zn、Ni为对象,构建土壤重金属预测的GWR高光谱模型,并将预测结果与普通最小二乘法回归（OLS）结果进行比较分析,探讨GWR模型在土壤重金属高光谱预测中的适用性及局限性。结果表明：① GWR模型在土壤重金属高光谱预测中适用与否取决于重金属对光谱变量影响的空间异质性程度：对于Cr、Cu、Zn、Pb等对光谱变量影响空间异质性大的元素,其GWR预测精度较OLS提高明显,表现为GWR模型的调节R²较OLS模型有了明显提高,分别为OLS模型的2.69倍、2.01倍、1.87倍和1.53倍;而AIC值以及残差平方和较OLS模型却明显降低,AIC值减少量均大于3个单位,残差平方和则仅分别为OLS模型的25.33%、30.09%、47.22%和86.84%;对于Cd和Ni等对光谱变量影响空间异质性小的元素,相较于OLS模型,GWR模型的调节R²分别提高了0.015和0.007,残差平方和分别减少了5.97%和4.18%,但AIC值却分别增加了2.737和2.762,GWR预测效果改善不明显;② 光谱变换可以有效增强土壤重金属的光谱特征,其中以光谱的倒数变换效果最好,而且该变换及其微分形式可以很好地提高模型的预测效果;③ GWR模型的应用前提是变量间关系的空间非平稳性,适合在与土壤光谱变量间关系具有显著空间异质性的重金属高光谱预测中推广。     

Exploring Spatially Non-stationary Relationships in the Determinants of Mineralization in 3D Geological Space

Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies.

     

中国省域犯罪率影响因素的空间非平稳性分析

收入差距和流动人口是影响犯罪率的两个重要因素, 以往研究基于OLS模型, 在假设地域空间为均质的前提下分析其对犯罪率的影响, 但现实世界的空间单元往往难以满足“均质”的假设, 多数表现为“空间异质”。以OLS计量空间异质会造成计量结果出现偏差, 同时无法了解不同空间单元的不同影响。而地理加权回归模型通过将空间结构嵌入线性回归模型中, 很好的解决了空间异质的计量问题。利用地理加权回归模型研究2008 年中国大陆省域单元犯罪率的影响因素, 结果表明:① 犯罪率的影响因素表现出空间非平稳性, 流动人口与犯罪率显著相关, 但各个省份相关程度并不相同, 影响关系随空间位置变化而变化;② 地理加权回归模型的计量精度和拟合度比OLS模型有大幅提高     

Geographically weighted regression with parameter-specific distance metrics

Geographically weighted regression (GWR) is an important local technique to model spatially varying relationships. A single distance metric (Euclidean or non-Euclidean) is generally used to calibrate a standard GWR model. However, variations in spatial relationships within a GWR model might also vary in intensity with respect to location and direction. This assertion has led to extensions of the standard GWR model to mixed (or semiparametric) GWR and to flexible bandwidth GWR models. In this article, we present a strongly related extension in fitting a GWR model with parameter-specific distance metrics (PSDM GWR). As with mixed and flexible bandwidth GWR models, a back-fitting algorithm is used for the calibration of the PSDM GWR model. The value of this new GWR model is demonstrated using a London house price data set as a case study. The results indicate that the PSDM GWR model can clearly improve the model calibration in terms of both goodness of fit and prediction accuracy, in contrast to the model fits when only one metric is singly used. Moreover, the PSDM GWR model provides added value in understanding how a regression model’s relationships may vary at different spatial scales, according to the bandwidths and distance metrics selected. PSDM GWR deals with spatial heterogeneities in data relationships in a general way, although questions remain on its model diagnostics, distance metric specification, and computational efficiency, providing options for further research.     

东北地区旅游经济影响因素时空特征研究

从中观层面以东北地区41个市域为分析单元,选择东北振兴战略实施以来2004、2009和2015年截面数据,采用ESDA法分析旅游经济发展的空间关联特征,运用OLS和GWR模型分析旅游经济和旅游产业因子、消费因子、投资因子之间的关系,以此挖掘旅游经济影响因素的时空结构信息。结果表明：① 旅游经济发展呈显著空间正相关,相关性逐渐增大;② OLS回归结果表明,旅游产业因子对旅游经济发展的影响强度最大,在旅游产业发展中始终起基础性决定作用,其次为消费因子和投资因子,后两者差别不大;③ GWR回归结果显示,模型3 a拟合优度均比OLS有所提高,回归系数均为正值,但分布规律不同;旅游产业因子回归系数高值区经历了西南部-中南部-东北部转移,向外围圈层递减;消费因子回归系数高值区由东北部向东南部转移,向外围逐渐递减;投资因子回归系数高值区则由东北部-中南部-东北部转移,向外围逐渐递减。