部分变量误差模型的整体抗差最小二乘估计

部分变量误差模型(partial EIV model)的加权整体最小二乘(weighted total least-squares,WTLS)估计不具备抵御粗差的能力。鉴于粗差可能同时出现在观测值和系数矩阵中,本文在提出部分变量误差模型WTLS估计的两步迭代解法的基础上,运用抗差M估计的等价权方法,发展了一种整体抗差最小二乘(TRLS)估计方法,并采用一致最大功效统计量确定降权因子。针对WTLS估计两步迭代解法的特点,设计了两个不同的降权方案:第1个方案是在估计系数矩阵元素时,不对观测值降权,仅对系数矩阵降权;第2个方案是在估计系数矩阵元素时,既对系数矩阵降权,同时也对观测值降权。通过对模拟2D仿射变换和线性拟合实例进行计算和分析,结果表明第1方案优于第2方案,并且优于基于残差和验后单位权方差的抗差估计和现有的变量误差模型抗差估计。     

干涉图像第二类统计Goldstein自适应滤波方法

干涉图滤波是InSAR数据处理中的关键步骤之一,滤波结果的优劣会直接影响到相位观测的质量和最终产品精度。本文结合干涉图滤波算法的研究进展,对Goldstein频率域滤波及其经典改进算法进行了系统分析和比较,在此基础上提出了一种基于第二类统计的稳健相干性估计量的Goldstein自适应滤波方法。本文采用模拟数据和Envisat ASAR真实数据与现有方法进行了验证,试验结果表明,新的滤波方法在保持细节和抑制噪声方面优势更加明显。     

地理国情普查试点成果综合分析

城镇服务设施布局的评价与分析是地理国情普查综合统计分析城镇化发展的重要内容。针对当前城镇化发展不平衡,生活服务设施空间布局不合理的情况,该文利用克拉玛依市独山子区地理国情普查试点成果,结合专题数据,采用GIS网络分析方法,在独山子区城镇综合设施之间配置最短最优路径。耦合结果表明:老城区卫生资源分布较合理,但是教育资源和整体服务设施分布却过于集中。这种方法还可以进一步用于优化城镇功能单元布局与服务保障设施点的建设,完善居民出行、教育、日常生活和文化服务等方面。     

Delineation and assessment of Central Indian Suture through lineament extraction approach using Remote Sensing and Geographic Information System

Lineament extraction approach allowed mapping of larger number of lineaments in a classical manner in tectonic and structural studies. In the present study, various techniques were adopted to extract lineaments using Landsat ETM+ images. To remove the biasness of the images, some pre-processing techniques like stream ordering, band differencing, colour texturing were employed to enhance the edges of the structural features. Extracted lineaments and its distribution and orientation were mapped using ArcGIS Spatial analyst tool. Results of the study showed maximum number of lineaments were oriented in the ENE–SWS direction with 63° overall inclination. Accuracy assessment and validation were made with respect to visual interpretation and overlaying the lineament on Digital Topographic Model as well as with respect to an existing geological lineament map of the study site. The result of accuracy assessment indicates higher compatibility of Central Indian Suture with the geological map of the study area.     

Estimation of transit trip production using Factorial Kriging with External Drift: an aggregated data case study

Studies in transportation planning routinely use data in which location attributes are an important source of information. Thus, using spatial attributes in urban travel forecasting models seems reasonable. The main objective of this paper is to estimate transit trip production using Factorial Kriging with External Drift (FKED) through an aggregated data case study of Traffic Analysis Zones in São Paulo city, Brazil. The method consists of a sequential application of Principal Components Analysis (PCA) and Kriging with External Drift (KED). The traditional Linear Regression (LR) model was adopted with the aim of validating the proposed method. The results show that PCA summarizes and combines 23 socioeconomic variables using 4 components. The first component is introduced in KED, as secondary information, to estimate transit trip production by public transport in geographic coordinates where there is no prior knowledge of the values. Cross-validation for the FKED model presented high values of the correlation coefficient between estimated and observed values. Moreover, low error values were observed. The accuracy of the LR model was similar to FKED. However, the proposed method is able to map the transit trip production in several geographical coordinates of non-sampled values.     

Estimation of mean and extreme waves in the East China Seas

Accurately estimating the mean and extreme wave statistics and better understanding their directional and seasonal variations are of great importance in the planning and designing of ocean and coastal engineering works. Due to the lack of long-term wave measurement data, the analysis of extreme waves is often based on the numerical wave hind-casting results. In this study, the wave climate in the East China Seas (including the Bohai Sea, the Yellow Sea and the East China Sea) for the past 35 years (1979–2013) is hind-casted using a third generation wave model – WAMC4 (Cycle 4 version of WAM model). Two sets of reanalysis wind data from NCEP (National Centers for Environmental Prediction, USA) and ECMWF (European Centre for Medium-range Weather Forecasts) are used to drive the wave model to generate the long-term wave climate. The hind-casted waves are then analysed to study the mean and extreme wave statistics in the study area. The results show that the mean wave heights decrease from south to north and from sea to land in general. The extreme wave heights with return periods of 50 and 100 years in the summer and autumn seasons are significantly higher than those in the other two seasons, mainly due to the effect of typhoon events. The mean wave heights in the winter season have the highest values, mainly due to the effect of winter monsoon winds. The comparison of extreme wave statistics from both wind fields with the field measurements at several nearshore wave observation stations shows that the extreme waves generated by the ECMWF winds are better than those generated by the NCEP winds. The comparison also shows the extreme waves in deep waters are better reproduced than those in shallow waters, which is partly attributed to the limitations of the wave model used. The results presented in this paper provide useful insight into the wave climate in the area of the East China Seas, as well as the effect of wind data resolution on the simulation of long-term waves.     

西藏“一江三河”地区地下水环境背景值初步研究

在分析对比国内外研究方法的基础上,选择数理统计方法,通过最广泛的数理统计法的应用原则、数据检验、背景值取值等问题的论述,对西藏"一江三河"地区地下水水质指标TDS,Ca²+,Mg²⁺,Na⁺环境背景值进行研究,并初步探讨了它们的空间分布规律及控制因素.结果表明,地形地貌、区域水动力条件等因素对研究区地下水指标的空间分布起主要控制作用.该结果为评价西藏"一江三河"地区地下水污染状况提供科学的依据.     

Progress and Prospect of Statistics and Assessment of Large-scale Natural Disaster Damage and Losses

In June 2014, the "Statistics System for the Damage and Loss of Large-scale Natural Disasters" (SSDLLND) was issued by the Ministry of Civil Affairs and the Office of National Disaster Reduction Committee, which marked that the statistics and assessment of China's catastrophic natural disaster damage and losses formally entered a new stage of institutionalization. On the basis of analyzing the five major international disaster damage and loss assessment systems, including HAZUS-MH, ECLAC, DaLA, EMA-DLA and PDNA, the differences between the “SSDLLND” of China and five major international systems were compared from the statistics and assessment contents and indicators. Combined with the statistics and assessment practices of China’s large-scale disaster damage and losses and the characteristics of international systems in recent years, the future development of the SSDLLND were proposed in three aspects: Enriching and improving the framework of damage and loss statistics content, stepwise improvement of disaster impact assessment methods (such as the ecological capital loss assessment, tourism industry loss assessment due to the disasters, etc.), and improving indicators and parameters of loss statistics. The study has an important practical significance for improving the statistics and assessment system of the damage and loss of major natural disasters and better serving the post-disaster recovery and reconstruction decision-making.     

碳市场与电力市场机制影响下发电机组成本分析与竞争力研究

碳市场和电力市场将共同发挥市场对资源优化配置的决定性作用,对共同市场主体发电企业带来经营和发展挑战。在考虑碳成本的基础上,采用发电机组经济性影响模型,定量分析了碳市场不同发展阶段对发电机组发电成本的影响,定量评价不同能源结构投资收益经济性,以及对集中竞价市场出清顺序的影响。结果表明,随着有偿配额比例和碳价增加,碳成本占发电成本比例逐步攀升,燃煤机组碳成本占发电成本比例最高将达29%,燃气机组此比例达6%;在设定碳市场高比例配额有偿分配和高碳价情况下,煤电和气电单位发电收益逐步降低甚至出现亏损,远低于可再生能源,推动投资逐步向清洁高效火电机组和可再生能源倾斜;碳成本将成为竞价市场需考虑的重要因素,并影响发电机组出清顺序,进一步巩固高效率、低排放机组在集中竞价市场中的竞争力。     

A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods

Rapid flood mapping is critical for local authorities and emergency responders to identify areas in need of immediate attention. However, traditional data collection practices such as remote sensing and field surveying often fail to offer timely information during or right after a flooding event. Social media such as Twitter have emerged as a new data source for disaster management and flood mapping. Using the 2015 South Carolina floods as the study case, this paper introduces a novel approach to mapping the flood in near real time by leveraging Twitter data in geospatial processes. Specifically, in this study, we first analyzed the spatiotemporal patterns of flood-related tweets using quantitative methods to better understand how Twitter activity is related to flood phenomena. Then, a kernel-based flood mapping model was developed to map the flooding possibility for the study area based on the water height points derived from tweets and stream gauges. The identified patterns of Twitter activity were used to assign the weights of flood model parameters. The feasibility and accuracy of the model was evaluated by comparing the model output with official inundation maps. Results show that the proposed approach could provide a consistent and comparable estimation of the flood situation in near real time, which is essential for improving the situational awareness during a flooding event to support decision-making.