不同比例尺下DEM的地形特征空间自相关性差异——以川渝地区和长三角区为例

采用局部Moran’s I模型对川渝地区和长三角区1∶250 000和1∶1 000 000两种尺度DEM以及全国1∶1 000 000 DEM地形信息进行了空间自相关的计算分析与比较,研究发现两种尺度下,高程自相关存在一致性,而坡度自相关差异性较大。1∶250 000地形自相关特性多呈随机分布,反映地势较平稳,具有均...     

利用空间自相关的中国内陆甲型H1N1流感疫情分析

为了获得对中国大陆地区2009年爆发的甲型H1N1流感疫情的时空分布的认识,以中国省级甲流疫情的逐月时空数据（时间序列为2009年5月至2010年3月）为研究对象,采用空间自相关方法对其进行了分析。结果发现,甲流传播的集聚性是由弱到强变化的,疫情大致经历了三个阶段：早期的随机分布、中期的扩散以及后期的集中控制;广东、北京、浙江是疫情最为严重的地区,但高高疫情相邻的省份不多;统计学上显著的疫情爆发热点集中在中东部的浙江、江苏、上海、福建等地,聚集重心并未随着时间发生转移。     

Shrubs proliferated within a six-year exclosure in a temperate grassland—Spatiotemporal relationships between vegetation and soil variables

Overgrazing has been considered one of the maj or causes that trigger shrub encroachment of grassland. Proliferation of shrubs in grassland is recognized as an important indicator of grassland degradation and desertification. In China, various conservation measures, including enclosures to reduce livestock grazing, have been taken to reverse the trend of grassland desertification, yet shrubs have been reported to increase in the grasslands over the past decades. In late 2007, we set up a 400-m-by-50-m exclosure in a long-term overgrazed temperate grassland in Inner Mongolia, with the ob- jective to quantify the spatiotemporal relationship between vegetation dynamics, soil variables, and grazing exclusion. Soil moisture was continuously monitored within the exclosure, and cover and aboveground biomass of the shrubs were measured inside the exclosure in 2007, 2009, 2010, 2012, and 2013, and outside the exclosure in 2012 and 2013. We found the average shrub cover and biomass significantly increased in the six years by 103 % and 120%, respectively. The result supported the hypothesis that releasing grazing pressure following long-term overgrazing tends to trigger shrub invasion into grassland. Our results, limited to a single gradient, suggest that any conservation measures with quick release of overgrazing pressure by enclosure or other similar means might do just the opposite to accelerate shrub en- croachment in grassland. The changes in vegetation cover and biomass were regressed on the temporal average of the soil moisture content by means of the generalized least square technique to quantify the effect of the spatial autocor- relation. The result indicates that the grass cover and biomass significantly increased with the top, but decreased with the bottom layer soil moisture. The shrub cover and biomass, on the other hand, decreased with the top, but increased with bottom soil moisture, although the regression coefficients for the shrubs were not statistically significant. Hence this study supports the two-layered soil model which assumes grasses and shrubs use belowground resources in dif- ferent depths.     

城市蔓延综合指数的评价与分析——以武汉市为例

城市蔓延是城市边界向外扩张、城市用地更为分散的现象,往往是人口增长、经济收入提高、交通便捷、土地利用变化等引起的。采用集成3S技术综合测度城市蔓延的程度,将城市蔓延的综合指数分解为人口指数(Ips)、社会经济指数(Ies)、交通指数(Its)和土地利用指数(Ils),并融合遥感、地理信息系统和空间分析技术进行多因素多因子评价,对武汉市1984—2013年城市蔓延程度进行度量。研究结果表明:Ips总体呈直线上升,Ies、Its和Ils总体呈指数型上升,Ils和Its近5年的增幅较大;路网密度空间自相关指数Moran’s I值最低,分布最为分散,Its权重值最高,建设用地比例权重值最低;城市蔓延度综合指数在2004年前的增长速度相对缓慢,之后呈显著上升趋势。     

利用接收机钟差实时滤波提高GNSS精度

建立接收机钟差的高斯马尔可夫模型,通过自相关函数估计接收机钟差的时间常数,利用Allan方差确定模型谱密度参数,将滤波后的接收机钟差返回补偿至GNSS观测方程,重新进行导航解算。实验结果表明,对接收机钟差进行滤波,可有效提高钟差估计精度,提高GNSS的精度。     

Temporal and spatial variability of annual and seasonal rainfall over Ethiopia

Abstract

Characterization of the seasonal and inter-annual spatial and temporal variability of rainfall in a changing climate is vital to assess climate-induced changes and suggest adequate future water resources management strategies. Trends in annual, seasonal and maximum 30-day extreme rainfall over Ethiopia are investigated using 0.5° latitude?×?0.5° longitude gridded monthly precipitation data. The spatial coherence of annual rainfall among contiguous rainfall grid points is also assessed for possible spatial similarity across the country. The correlation between temporally coinciding North Atlantic Multidecadal Oscillation (AMO) index and annual rainfall variability is examined to understand the underlying coherence. In total 381 precipitation grid points covering the whole of Ethiopia with five decades (1951–2000) of precipitation data are analysed using the Mann-Kendall test and Moran spatial autocorrelation method. Summer (July–September) seasonal and annual rainfall data exhibit significant decreasing trends in northern, northwestern and western parts of the country, whereas a few grid points in eastern areas show increasing annual rainfall trends. Most other parts of the country exhibit statistically insignificant trends. Regions with high annual and seasonal rainfall distribution exhibit high temporal and spatial correlation indices. Finally, the country is sub-divided into four zones based on annual rainfall similarity. The association of the AMO index with annual rainfall is modestly good for northern and northeastern parts of the country; however, it is weak over the southern region.

Editor Z.W. Kundzewicz; Associate editor S. Uhlenbrook

Citation Wagesho, N., Goel, N.K., and Jain, M.K. 2013. Temporal and spatial variability of annual and seasonal rainfall over Ethiopia. Hydrological Sciences Journal, 58 (2), 354–373.     

The distribution of Spearman’s rho trend statistic for persistent hydrologic data

Abstract

Spearman’s rho, a distribution-free statistic, has been suggested in the literature for testing the significance of trend in time series data. Although the use of the test based on Spearman’s rho (also known as the Daniels test) is less widespread than that based on Kendall’s tau (the Mann-Kendall test), the two tests have been shown in the literature to be equivalent for time series with independent observations. The distribution of the Mann-Kendall trend statistic for persistent data has been previously addressed in the literature. In this paper, the distribution of Spearman’s rho as a trend test statistic for persistent data is studied. Following the same procedures used for Kendall’s tau in earlier work, an exact expression for the variance of Spearman’s rho for persistent data with multivariate Gaussian dependence is derived, and a method for calculating the exact full distribution of rho for small sample sizes is also outlined. Approximations for moderate and large sample sizes are also discussed. A case study of testing the significance of trends in a group of world river flow station data using both Kendall’s tau and Spearman’s rho is presented. Both the theoretical results and those of the case study confirm the equivalence of trend testing based on Spearman’s rho and Kendall’s tau for persistent hydrologic data.

Editor Z. W. Kundzewicz; Associate editor S. Grimaldi     

Detecting negative spatial autocorrelation in georeferenced random variables

Negative spatial autocorrelation refers to a geographic distribution of values, or a map pattern, in which the neighbors of locations with large values have small values, the neighbors of locations with intermediate values have intermediate values, and the neighbors of locations with small values have large values. Little is known about negative spatial autocorrelation and its consequences in statistical inference in general, and regression-based inference in particular, with spatial researchers to date concentrating mostly on understanding the much more frequently encountered case of positive spatial autocorrelation. What are the spatial contexts within which negative spatial autocorrelation should be readily found? What are its inferential consequences for regression models? This paper presents selected empirical examples of negative spatial autocorrelation, adding to the slowly growing literature about this phenomenon.     

A local scale-sensitive indicator of spatial autocorrelation for assessing high- and low-value clusters in multiscale datasets

Georeferenced user-generated datasets like those extracted from Twitter are increasingly gaining the interest of spatial analysts. Such datasets oftentimes reflect a wide array of real-world phenomena. However, each of these phenomena takes place at a certain spatial scale. Therefore, user-generated datasets are of multiscale nature. Such datasets cannot be properly dealt with using the most common analysis methods, because these are typically designed for single-scale datasets where all observations are expected to reflect one single phenomenon (e.g., crime incidents). In this paper, we focus on the popular local G statistics. We propose a modified scale-sensitive version of a local G statistic. Furthermore, our approach comprises an alternative neighbourhood definition that enables to extract certain scales of interest. We compared our method with the original one on a real-world Twitter dataset. Our experiments show that our approach is able to better detect spatial autocorrelation at specific scales, as opposed to the original method. Based on the findings of our research, we identified a number of scale-related issues that our approach is able to overcome. Thus, we demonstrate the multiscale suitability of the proposed solution.     

Trapped in Jobless Household Areas: the spatio-temporal dynamics of children in jobless households in metropolitan Australia

Child disadvantage is an important issue in Australia today and is one of the key policy target areas of the Federal government. Child disadvantage not only affects child wellbeing in the present time but can also inhibit their future achievements. As well as individual- and family-level effects, there is growing evidence that child wellbeing and achievement is highly affected by the environment in which children grow up, and what resources and role models are available to them in their surrounding areas. In this paper, we examine the spatial clustering of children in jobless households through the computation of the Global and Local Moran's statistics, for the Sydney and Melbourne metropolitan regions, and observe how these clusters have changed between the 2001 and 2006 census years. We find that there are significant spatial clusters of children in jobless households in both periods, suggesting that ‘place’ is a driver of this particular phenomenon. Further, we observe an expansion of ‘hot spots’, in both Sydney and Melbourne, suggesting that areas with high proportions of children in jobless households have both persisted and increased despite the sizeable economic growth in Australia over the same period.