2005-2014年长江经济带城市土地利用效率时空格局特征

如何以最小的城市土地资源投入获得最大的社会经济效益是区域生态文明建设关注的重点之一。以长江经济带110个地级市为研究对象,基于2005-2014年的市级投入产出面板数据,引入随机前沿模型（Stochastic Frontier Analysis,SFA）测度城市土地利用效率,并对其空间关联特征进行分析。结果表明：① 2005-2014年长江经济带城市土地利用效率由0.344升至0.53,累计提升率为54.07%,呈现明显的增长趋势,上游效率增长速率快于中游和下游,全域土地利用效率仍有较大上升潜力;② 城市土地利用效率呈现“条块状”分布特征,从东向西逐步递减,不仅省内、省际差异显著,上中下游的差异也较大,下游地区土地利用效率最高、中游次之、上游最低;③ 10年间城市土地利用效率Moran's I均大于0且逐年上升,说明城市土地利用效率存在空间正相关性且集聚特征逐年增强,LISA空间形态呈现“小集聚大分散”特征,H-H集聚区沿浙江、上海向外扩散,L-L集聚区集中于皖北、川北等地。研究框架和结果对区域土地资源可持续利用和统筹协调发展具有重要意义。     

球状星团成员星的空间相关性分析

以球状星团NGC (New General Catalogue) 104、NGC 5139、NGC 6121为实验样区, 选取了视差等10个恒星参数, 通过引入地学中的空间分析理论和相应的分析框架为定量描述球状星团成员星的空间分布特征提出了一种基于地学的研究范式. 通过计算全局和局部莫兰(Moran)指数得到球状星团成员星各恒星参数的空间分布特征. 研究结果表明: 球状星团NGC 104、NGC 5139、NGC 6121成员星的各恒星参数在总体上呈现出空间正相关特性, 表现出空间集聚特征, 但不同恒星参数之间存在差异; 局部空间分布也呈现聚集特征, 而不同的成员星呈现出不同的空间分布特性和趋势. 总体而言, 用地学空间相关分析系统地定量化描述球状星团成员星空间分布特征, 能够为球状星团的研究提供新的思路.     

中国区域工业全要素生产率的空间计量经济分析

全要素生产率(TFP)是一个国家或地区经济增长质量和技术进步、管理效率提高的重要标志,正确、科学测算TFP对区域经济增长和技术进步及政策研究非常重要。运用空间统计和空间计量经济学的空间自相关Moran指数、空间滞后模型和空间误差模型方法,基于2003年中国大陆31个省、直辖市和自治区的工业企业统计数据,对中国大陆省级区域工业全要素生产率进行了空间计量经济测算分析。结果发现,空间统计与空间计量经济学模型在测算我国省域工业全要素生产率中具有较好效果,利用这种方法测算的2003年中国大陆31个区域全要素生产率的实证结果比较符合工业生产率发展实际;在影响我国省域工业生产率的因素中,工业资本投入是造成工业经济增长率在东中西部地区之间和各个省域之间存在巨大差异的主要原因;劳动生产率水平偏低是制约我国省域工业生产率提高的主要瓶颈因素;2003年我国省域工业生产率增长是由资本和技术共同推动的。     

Spatial and Temporal Scale Variations of Sea Surface Temperature in the East Sea Using NOAA/AVHRR Data

Sea surface temperature fields in the East Sea are composed of various spatial structures such as eddies, fronts, filaments, turbulent-like features and other mesoscale variations associated with the oceanic circulations of the East Sea. These complex SST structures have many spatial scales and evole with time. Semi-monthly averaged SST distributions based on extensive satellite observations of SSTs from 1990 through 1995 were constructed to examine the characteristics of their spatial and temporal scale variations by using statistical methods of multi-dimensional autocorrelation functions and spectral analysis. Two-dimensional autocorrelation functions in the central part of the East Sea revealed that most of the spatial SST structures are anisotropic in the shape of ellipsoids with minor axes of about 90–290 km and major axes of 100–400 km. Two dimensional spatial scale analysis demonstrated a consistent pattern of seasonal variation that the scales appear small in winter and spring, increase gradually to summer, and then decrease again until the spring of the next year. These structures also show great spatial inhomogeneity and rapid temporal change on time scales as short as a semi-month in some cases. The slopes in spectral energy density spectra of SSTs show characteristics quite similar to horizontal and geostrophic turbulence. Temporal spectra at each latitude are demonstrated by predominant peaks of one and two cycles per year in all regions of the East Sea, implying that SSTs present very strong annual and semi-annual variations. This revised version was published online in August 2006 with corrections to the Cover Date.     

贵州乌江流域人口与经济发展协调性研究

贵州乌江流域是典型的山地流域,流域自然生态环境恶劣,人口与经济发展矛盾突出,探究流域人口与经济协调发展关系对于推动流域人口与经济可持续发展具有重要意义。选取2001~2015年贵州乌江流域各区域相关数据,运用人口-经济弹性、地理集中度、耦合指数、空间自相关和重心分析等方法对流域人口与经济协调发展关系进行探究。结果表明：① 在总量规模上,流域人口增长与经济增长协调度较高,但流域内各区域却存在差异。② 人口、经济地理集中度在空间分布上存在区域差异,流域中部区域较高且由中部向四周逐渐降低。人口、经济地理集中度在全域空间上莫兰指数值逐渐变小,表明流域人口、经济地理集中度空间自相关性在减弱,人口、经济发展的空间独立性逐渐增强。③ 人口重心、经济重心逐渐向远离流域几何重心的西南方向移动,人口重心与经济重心发生偏离,贵州乌江流域存在区域发展非均衡性。     

地表温度扰动特性及其与建设用地扩张的关系

河口地区是人口密集、经济繁荣的区域之一,同时也是生态环境脆弱区,易受到人类活动带来的负面影响,其中热环境恶化问题尤其突出。本文基于多源遥感影像数据,结合遥感技术和统计学方法,研究城市化进程中闽江河口地区20年来的地表温度变化特性;利用Moran's I指数分析研究区表面温度的空间集聚特性及其产生的尺度效应,并定量分析表面温度与不同景观类型的相关性。结果表明：① 1993-2013年,闽江河口地区建设用地面积大幅增加,经历了由慢至快,而后稳中有升的过程;城市扩张是通过大量大规模的边缘式扩张实现的,东部、西部和南部是扩张的3个主要方向。② 地表温度空间格局变化明显,次高温区和高温区显著增加,次低温区和中温区减少,低温区变化不大;高温区扩散方向与建设用地扩张方向基本一致。③ 地表温度存在明显的扰动现象,表现为靠近主城区剧烈,城郊和郊区相对和缓的特性,同时地表温度空间格局具有显著的空间聚集性。④ 建设用地扩张是地表温度增强的重要原因,林/草地和水域能有效缓解区域高温;耕地并未表现出显著的降温效果,但随着耕地比重的增加,地表温度逐渐趋于稳定。研究结果可为改善城市热环境、促进区域可持续发展提供有益参考。     

秦巴山区乡村交通环境脆弱性及影响因素——以陕西省洛南县为例

山区乡村长期处于地形地貌制约、自然灾害频发的风险胁迫之下,乡村交通系统网络化水平低、抗灾能力弱,交通环境脆弱性问题突出。以秦巴山区洛南县为例,基于人地关系脆弱性的暴露、敏感、应对能力3个维度构建了涵盖风险事件、地理特征、关键出行路径、路网结构、交通工具、家庭资本等要素的乡村交通环境脆弱性基本构成框架,并针对性建立了评估指标体系。依托ArcGIS和GeoDa软件解析了洛南县交通环境脆弱性的空间结构和空间自相关特征,运用地理加权回归模型探寻了自然、人口、社会、经济因素对交通风险应对能力的影响及空间差异。结果显示：交通环境脆弱性以县城及城郊为中心向外递增形成圈层结构,且垂直差异显著;暴露度、敏感性均与应对能力呈现显著的空间负相关性,脆弱性局部“热点”区域广泛分布于北部中山地带,且陷入了高暴露、高敏感、低应对能力的窘境。“冷点”区域多为城郊或邻近镇区的村庄,敏感性低,应对能力高;地形条件、产业分布、人口结构与受教育程度、家庭规模对交通风险应对能力有显著影响,影响性质及强度存在空间差异。     

Spatial considerations when monitoring reef fishes

Spatial dependence can obscure relationships between response and explanatory variables because of structuring within the residuals reducing variance and biasing coefficient estimates. Here, we highlight the influence of the spatial component, in the presence of spatial dependence, on abundance trends. This is illustrated using abundance data for a Critically Endangered reef fish, dageraad Chrysoblephus cristiceps, which were obtained from a long-term monitoring programme in the Tsitsikamma National Park marine protected area, South Africa. Correlograms illustrate distinct spatial structuring in the abundance data, and spatial variables were determined as more important than temporal variables when ranked according to predictive power using a random forest analysis. A generalised additive model (GAM) that did not account for spatial dependencies was compared to a generalised additive mixed model (GAMM) that incorporated a spatial residual correlation structure. Results derived from the spatially explicit GAMM differed considerably from the GAM lacking a spatial component, with the latter deemed to produce over-precise and partially biased abundance trends. The study emphasises the importance of space in accurately modelling abundance estimates, particularly temporal trends, and provides an introduction to the minimal statistical requirements necessary to address the violations associated with spatial autocorrelation.     

青海省藏传佛教寺院时空分布特征

基于青海省藏传佛教寺院数据,以县（市）作为基本研究单元,综合运用数理统计方法和空间分析方法,分析青海省藏传佛教寺院的时空演变特征,揭示藏传佛教在青海发展演变模式。结果表明：①在时间分布方面,根据藏传佛教寺院增长率的变化情况,划分为4个阶段：宋元属萌芽阶段,明属初期发展阶段,清属快速发展阶段,近代（截止1995年）属稳定发展阶段;②在空间分布方面,存在着分别以玉树和化隆为中心的“双核集聚”空间分布密集区;③空间分布存在明显的地域分异规律,东多西少,分布不平衡趋势不断增强,分布重心向东北方向迁移;④藏传佛教寺院的空间全局与局部自相关特征显著,在寺院空间自相关演变趋势中,青海省“扩散型”区域的范围逐渐扩大且呈相对独立的“团状”分布,“落后型”区域呈连续的大片“块状”分布。     

中国城市碳排放强度的空间溢出效应及驱动因素探究（英文）

In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained.(1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing.(2) The spatial autocorrelation Moran's I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable.(3) Spatial Markov chain analysis shows a Matthew effect in China's urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear "Spatial Spillover" effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa.(4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.