中南半岛旱季VIIRS活跃火的空间特征与国别差异

热带是全球活跃火(active fire)的集中发生区,客观认识其空间特征、国别差异及其动态变化对评估区域生物质燃烧及其碳排放等具有重要意义。作为热带季风气候典型区,中南半岛旱季活跃火发生发展空间特征及其动态变化仍缺乏清晰认识。为此,论文利用可见光红外成像辐射仪(VIIRS) S-NPP 2012—2019年活跃火矢量数据,基于核密度与空间自相关评价了中南半岛及国别旱季尤其是其特征月份(2—4月)活跃火发生发展的密集程度、集聚特征及其动态变化。结果表明：① 中南半岛活跃火核密度低值区占比最大(79%),高值区最小(4%);柬埔寨、缅甸、老挝等经济落后国家的核密度均值明显高于泰国和越南;2012—2019年核密度高值区具有朝高海拔、向内陆与趋边境等分布特征,且柬埔寨东北部长居高值区。② 活跃火核密度中值区变化集中在1—4月,且多分布在低、高值区周围;高值区变化集中在2—4月,由柬埔寨东北部逐渐向缅甸东/西部、泰国西北部以及老挝北/南部转移。③ 半岛与5国活跃火核密度在旱季具有显著空间正相关性,空间集聚类型以“高—高”型和“低—低”型集聚为主,越南、柬埔寨等国局部自相关性强于泰国和老挝。     

Modeling network autocorrelation within migration flows by eigenvector spatial filtering

Although the assumption of independence among interaction flows frequently is engaged in spatial interaction modeling, in many circumstances it leads to misspecified models and incorrect inferences. An informed approach is to explicitly incorporate an assumed relationship structure among the interaction flows, and to explicitly model the network autocorrelation. This paper illustrates such an approach in the context of U.S. interstate migration flows. Behavioral assumptions, similar to those of the intervening opportunities or the competing destinations concepts, exemplify how to specify network flows that are related to particular origin–destination combinations. The stepwise incorporation of eigenvectors, which are extracted from a network link matrix, captures the network autocorrelation in a Poisson regression model specification context. Spatial autocorrelation in Poisson regression is measured by the test statistic of Jacqmin-Gadda et al. (Stat Med 16(11):1283–1297, 1997). Results show that estimated regression parameters in the spatial filtering interaction model become more intuitively interpretable.

基于自相关的我国农民人均纯收入的空间关联规则挖掘

空间自相关是空间统计的一种分析方法,能在设定显著性水平下研究相邻区域位置属性或现象之间的相关性,是分析区域变量的空间结构特征的有效手段。本文通过时2000～2004年我国农民人均纯收入进行全局和局部空间自相关分析,将区域分布划分为高收入地区、潜在高收入地区及低收入地区,揭示了全国范围内各省农民人均纯收入在整体上呈现出显著的空间正相关特性,其空间聚类趋势明显。     

Correlation Analysis of the Ultrasonic Emission from Loaded Rock Samples – the Study of Interaction of Microcracking Nucleation Centres

Summary.  The locations of fractures within loaded rock samples are distributed irregularly because of the presence of inhomogeneities in the rock sample, the existence of primary microcracks and non-uniformly distributed stress. In the case of brittle fracture, the positions of these fractures can be determined by locating the foci of ultrasonic events that occur during fracturing. In some cases, the foci cluster into clouds that are restricted spatially and are referred to as nucleation centres. The purpose of this research was to determine the mutual relationship between the various nucleation centres, by cross-correlating the time-series of ultrasonic events produced by microcracking in the individual, separate nucleation centres. An additional goal was to assess the effect of the existence of such nucleation centres on the autocorrelation parameters. This study showed that the separate nucleation centres did not appear to influence one another even during the final stages of fracturing. Until now, autocorrelation analysis of acoustic emission time sequences has been applied to evaluate the mutual influence of individual events that occur in the whole sample volume, regardless of their location. In a previous study, it was found that before the total fracturing of the sample, the autocorrelation parameters changed significantly due to the increase in mutual relationship between successive events. In this study the separated nucleation centres determined by locating the ultrasonic events were subjected to autocorrelation analysis. It was demonstrated that, in the case of critical loading of the sample, the mutual influence between the events of a given nucleation centre can be observed. The experiments have demonstrated that the autocorrelation has a precursory nature and have shown that there is a significant difference in the autocorrelation parameters calculated between the set of all recorded events, and the specific sub-set. These differences must be taken into account when applying statistical predictions, for example, in the study of rock bursts. Author’s address: Jan Vilhelm, Charles University in Prague, Albertov 6, 128 43 Prague, Czech Republic     

Spatiotemporal patterns in urbanization efficiency within the Yangtze River Economic Belt between 2005 and 2014

The question of how to generate maximum socio-economic benefits while at the same time minimizing input from urban land resources lies at the core of regional ecological civilization construction. We apply stochastic frontier analysis (SFA) in this study to municipal input-output data for the period between 2005 and 2014 to evaluate the urbanization efficiency of 110 cities within the Yangtze River Economic Belt (YREB) and then further assess the spatial association characteristics of these values. The results of this study initially reveal that the urbanization efficiency of the YREB increased from 0.34 to 0.53 between 2005 and 2014, a significant growth at a cumulative rate of 54.07%. Data show that the efficiency growth rate of cities within the upper reaches of the Yangtze River has been faster than that of their counterparts in the middle and lower reaches, and that there is also a great deal of additional potential for growth in urbanization efficiency across the whole area. Secondly, results show that urbanization efficiency conforms to a “bar-like” distribution across the whole area, gradually decreasing from the east to the west. This trend highlights great intra-provincial differences, but also striking inter-provincial variation within the upper, middle, and lower reaches of the Yangtze River. The total urbanization efficiency of cities within the lower reaches of the river has been the highest, followed successively by those within the middle and upper reaches. Finally, values for Moran’s I within this area remained higher than zero over the study period and have increased annually; this result indicates a positive spatial correlation between the urbanization efficiency of cities and annual increments in agglomeration level. Our use of the local indicators of spatial association (LISA) statistic has enabled us to quantify characteristics of “small agglomeration and large dispersion”. Thus, “high- high” (H-H) agglomeration areas can be seen to have spread outwards from around Zhejiang Province and the city of Shanghai, while areas characterized by “low-low” (L-L) patterns are mainly concentrated in the north of Anhui Province and in Sichuan Province. The framework and results of this research are of considerable significance to our understanding of both land use sustainability and balanced development.     

A spatiotemporal regression-kriging model for space-time interpolation: a case study of chlorophyll-a prediction in the coastal areas of Zhejiang,China

Spatiotemporal kriging (STK) is recognized as a fundamental space-time prediction method in geo-statistics. Spatiotemporal regression kriging (STRK), which combines space-time regression with STK of the regression residuals, is widely used in various fields, due to its ability to take into account both the external covariate information and spatiotemporal autocorrelation in the sample data. To handle the spatiotemporal non-stationary relationship in the trend component of STRK, this paper extends conventional STRK to incorporate it with an improved geographically and temporally weighted regression (I-GTWR) model. A new geo-statistical model, named geographically and temporally weighted regression spatiotemporal kriging (GTWR-STK), is proposed based on the decomposition of deterministic trend and stochastic residual components. To assess the efficacy of our method, a case study of chlorophyll-a (Chl-a) prediction in the coastal areas of Zhejiang, China, for the years 2002 to 2015 was carried out. The results show that the presented method generated reliable results that outperform the GTWR, geographically and temporally weighted regression kriging (GTWR-K) and spatiotemporal ordinary kriging (STOK) models. In addition, employing the optimal spatiotemporal distance obtained by I-GTWR calibration to fit the spatiotemporal variograms of residual mapping is confirmed to be feasible, and it considerably simplifies the residual estimation of STK interpolation.     

河南省能源消费碳排放动态变化及空间分异

在分析河南省1978—2015年能源消费碳排放总量和结构变化的基础上,利用Im PACT等式对河南省碳排放驱动因素进行了研究和对未来碳排放量进行了情景预测,并运用空间自相关分析法探讨了空间分异特征。结果表明:(1)1978—2015年,河南省碳排放量总体上呈现增加的趋势,年均增长5.11%,由煤炭和石油消费导致的碳排放比重一直稳定在95%以上。(2)弹性分析表明人均真实GDP增加1%将导致人均能源消费量增加0.48%,利用强度下降0.52%,而环境影响增加0.53%。(3)保持经济增长的同时,与2011—2015年相比,1978—2015年效率年均增长率提高5.25倍,是河南省实现循环经济建设的一种可行方案。(4)河南省2015年碳排放全局Moran’s I值为0.047,呈微弱空间正相关,各地市碳排放具有明显的二元结构特征,空间集聚特征不明显。     

Spatial relationships of multivariate data

Spatial factor analysis (SFA) is a multivariate method that determines linear combinations of variables with maximum autocorrelation at a given lag. This is achieved by deriving estimates of auto-/cross-correlations of the variables and calculating the corresponding eigenvectors of the covariance quotient matrix. A two-point spatial factor analysis model derives factors by the formation of transition matrixU comparing auto-/cross-correlations at lag 0,R ₀, with those at a specified lag d,R _d, expressed asU _d=R ₀ ^–1 R_d. The matrixU _d can be decomposed into its spectral components which represent the spatial factors. The technique has been extended to include three points of reference. Spatial factors can be derived from the relationship:

森林脑炎时空分布特征和环境影响因素分析

森林脑炎作为一种蜱传自然疫源性疾病,其空间分布与环境关系密切,探索其时空分布模式与环境因子对其影响机制对于掌握和预测森林脑炎的发病风险区域具有重要意义。本文以我国东北疫源区（黑龙江省、吉林省和内蒙古自治区）为研究区,通过统计分析和空间自相关分析探究了2005—2015年森林脑炎时空分布特征,进而运用地理探测器模型探讨森林脑炎空间分布影响因素及其指示作用。结果表明：① 研究区内森林脑炎发病率在2005—2015年有明显的增长趋势和季节性发病特征,且其发病率具有较强的空间集聚模式,主要有2个大的热点集聚区;② 从整个研究区来看,植被类型、土地利用、年均气温、土壤类型、5—8月均气温、坡度、高程和年均降雨量是森林脑炎发病率空间流行的主要环境影响因素;③ 对于所筛选的环境指示因子而言,各指示因子对森林脑炎发病风险的影响程度存在差异,即各因子的各类型（范围）内,森林脑炎发病率不同;各指示因子两两之间的相互作用对森林脑炎的发病风险具有显著增强效应。研究结果可为研究区及全国森林脑炎疫情的有效控制提供科学依据和决策支持。