横断山脉地区大气环流演变特征分析

应用Lamb-Jenkinson大气环流分型方法对横断山脉地区的8个经度×10个纬度范围内1948-2012年逐日平均的海平面气压场进行环流分型,由日平均海平面气压场算出6个环流指数（u、v、V、ξu、ξv、ξ）,并由此划分出27种不同的环流型。分型结果表明：横断山脉地区主要环流分型为E型、NE型、SE型、N型和C型,其频率分别为：21.4%、14.6%、13.7%、9.8%和9.5%;E和NE型环流频率逐渐增加,C型环流型频率逐渐减少。春季横断山脉地区主导环流比较繁琐;NE、N型为夏季的主要环流型,但E型环流在夏季的频率也相当大;秋季和冬季横断山脉地区的主导环流型都为E型和SE型。夏季主导环流型持续时间较长,冬季也是主导环流持续的时间较长,个别年份主导环流型持续时间超过了一个月,这主要与横断山脉地区复杂的地形有联系。     

Thematic mapping from remotely sensed data with neural networks: MLP, RBF and PNN based approaches

 Neural networks are attractive tools for the derivation of thematic maps from remotely sensed data. Most attention has focused on the multilayer perceptron (MLP) network but other network types are available and have different properties that may sometimes be more appropriate for some applications. Here a MLP, radial basis function (RBF) and probabilistic neural network (PNN) were used to classify remotely sensed data of an agricultural site. The accuracy of these classifications ranged from 86.25–91.25%. The accuracy of the PNN classification could be increased through the incorporation of prior probabilities of class membership but the accuracy of each classification could also be degraded by the presence of an untrained class. Post-classification analyses, however, could be used to identify potentially misclassified cases, including those belonging to an untrained class, to increase accuracy. The effect of the post-classification analysis on the accuracy of the classification derived from each of the three network types investigated differed and it is suggested that network type be selected carefully to meet the requirements of the application in-hand. Received: 23 March 2000 / Accepted: 9 July 2000     

Mapping benthic habitat in regions of gradational substrata: An automated approach utilising geophysical,geological, and biological relationships

The relationship between acoustic backscatter, sediment characteristics and benthic habitat is examined using high-resolution sidescan sonar data collected at the Loch Linnhe artificial reef site on the west coast of Scotland. The site is typical for the continental shelf of NW Europe, with a mix of seabed environments from muddy to coarse, stony substrata on a 10–100 m length scale. A sidescan sonar mosaic was produced and classified according to derived backscatter parameters (mean, median and standard deviation of the backscatter values) using an unsupervised classification procedure. The accuracy of the final classified map was assessed by comparison with a ground-truthing survey in which the biological habitat was derived from underwater video footage. The sidescan correctly predicted seabed surface characteristics of observed biological habitat with 78% accuracy. A second, and more challenging test of the acoustic data to correctly predict biological habitat was made by comparing it with data from 21 grab sampling stations. These stations were divided into three groups using multivariate statistical techniques based on their backscatter properties. Benthic assemblage structure was found to be significantly distinct between the high and low, and the medium and low backscatter stations. There was a low to moderate but significant correlation between the multivariate patterns of acoustic backscatter, benthic assemblage structure, and particle size distribution. The work shows that even in areas with subtle and gradational changes in substratum, the sidescan was able to predict biological community with an acceptable accuracy.     

A new insight into land use classification based on aggregated mobile phone data

Land-use classification is essential for urban planning. Urban land-use types can be differentiated either by their physical characteristics (such as reflectivity and texture) or social functions. Remote sensing techniques have been recognized as a vital method for urban land-use classification because of their ability to capture the physical characteristics of land use. Although significant progress has been achieved in remote sensing methods designed for urban land-use classification, most techniques focus on physical characteristics, whereas knowledge of social functions is not adequately used. Owing to the wide usage of mobile phones, the activities of residents, which can be retrieved from the mobile phone data, can be determined in order to indicate the social function of land use. This could bring about the opportunity to derive land-use information from mobile phone data. To verify the application of this new data source to urban land-use classification, we first construct a vector of aggregated mobile phone data to characterize land-use types. This vector is composed of two aspects: the normalized hourly call volume and the total call volume. A semi-supervised fuzzy c-means clustering approach is then applied to infer the land-use types. The method is validated using mobile phone data collected in Singapore. Land use is determined with a detection rate of 58.03%. An analysis of the land-use classification results shows that the detection rate decreases as the heterogeneity of land use increases, and increases as the density of cell phone towers increases.     

MARC: a robust method for multiple-aspect trajectory classification via space,time, and semantic embeddings

ABSTRACT

The increasing popularity of Location-Based Social Networks (LBSNs) and the semantic enrichment of mobility data in several contexts in the last years has led to the generation of large volumes of trajectory data. In contrast to GPS-based trajectories, LBSN and context-aware trajectories are more complex data, having several semantic textual dimensions besides space and time, which may reveal interesting mobility patterns. For instance, people may visit different places or perform different activities depending on the weather conditions. These new semantically rich data, known as multiple-aspect trajectories, pose new challenges in trajectory classification, which is the problem that we address in this paper. Existing methods for trajectory classification cannot deal with the complexity of heterogeneous data dimensions or the sequential aspect that characterizes movement. In this paper we propose MARC, an approach based on attribute embedding and Recurrent Neural Networks (RNNs) for classifying multiple-aspect trajectories, that tackles all trajectory properties: space, time, semantics, and sequence. We highlight that MARC exhibits good performance especially when trajectories are described by several textual/categorical attributes. Experiments performed over four publicly available datasets considering the Trajectory-User Linking (TUL) problem show that MARC outperformed all competitors, with respect to accuracy, precision, recall, and F1-score.     

新疆哈密大降水的大气环流特征分型

利用1951~2012年新疆哈密市的观测资料和NCEP再分析资料,对造成哈密市大降水(12.0 mm)的大气环流特征进行合成分析。结果发现造成哈密站大降水的大气环流分为4种类型:横槽型、低涡型、低槽(ω)型和不稳定小槽型。其中出现暴雨以上降水(24 h降水量24.0 mm)的个例集中在低槽(ω)型;横槽型个例的降水量在13.0~22.0 mm之间;低涡型个例的降水量分布相对均匀,在15.0~20.0 mm之间;不稳定小槽型个例的降水量偏少,全部在18.0 mm以下。4种类型的大气环流特征主要在对流层中高层差异明显。横槽型中低纬度环流平直,其横槽的北部是西北气流,南亚高压是青藏高压西部型;低涡型的中低纬度,在孟加拉湾有低槽,低槽的北部有低涡存在,低涡西面的脊偏强,其南亚高压是青藏高压东部型;低槽(ω)型东部的脊偏强,呈西北东南向,南亚高压是伊朗高压型;不稳定小槽型的南亚高压为伊朗高原到青藏高原东部的带状,500 hPa位势高度场上,85°E以西是平直的偏西气流,新疆东部地区有一小的短波槽。     

山东埕岛东部东营组三角洲砂体地震预测技术

山东埕岛地区东营组是胜利油田近年来的一个勘探热点地区,资源潜力巨大.其主要发育河流和三角洲沉积体系,但砂体地震反射特征复杂,横向变化快,储层识别描述难度较大.通过建立全区高分辨率层序地层格架,在等时层序格架的基础上,开展储层描述.湖相沉积与三角洲相沉积在沉积物特征和地震响应上都具有较大差异,通过对已钻井的岩心、测井和录井等资料的分析,结合地震属性和地震相等宏观描述方法,在湖相沉积的背景之下刻画出三角洲的整体展布形态.在此基础上,针对三角洲主体区地震反射较弱、砂泥岩速度差异较小的特点,利用地震波形识别技术,采用6种参数对波形进行描述,得出12种波形分类,分别对应4种主要沉积微相.最终利用神经网络方法在平面上进行沉积微相的识别和刻画,取得了较好的效果.     

面向自然资源监测监管的分类体系设计

长期以来,我国采用分类管理的方式管理不同类型的自然资源,在自然资源监测监管中造成了部分自然资源重复统计、权属不明等问题,在湿地、林地等自然资源中表现尤其明显。为了系统性地进行自然资源监测监管,需要对自然资源进行整体性地、统一性地分类,建立一个综合性的自然资源分类体系。本文遵循“山水林田湖草”是一个生命共同体的理念,从这一理论思想出发,整理单门类自然资源整体和个体之间的关系,分析国内外的分类编码体系,从法理、学理和管理不同角度研究整理自然资源分类现状,综合考虑地表覆盖层的监测监管要求,采用树状分类法和交叉分类法重新构建了三级分类的自然资源分类体系框架,为构建自然资源监测监管体系提供必要的基础支撑,为完善生态文明建设,为国民经济健康持续发展提供重要的基础保障。     

地图数据图层级批量综合功能的设计与研制

目前,已有地图综合系统使用过程中需要专业人员深度参与,耗时耗力、自动化程度较低。针对这一问题,从软件工程的角度探讨了图层级批量综合功能的研制。以1∶5万居民地图层数据的综合处理为例,分别从建筑物目标的剖分、特征参量定义与计算、综合操作决策与实施、综合控制规则建立与表达4个方面进行分析,尝试构建"特征诊断→行为决策→操作执行"的完整技术链条。结合样例数据对提出的技术设想进行可行性验证,并对后续工作进行展望。     

基于社交媒体数据的城市人群分类与活动特征分析

空间信息技术已开始进入全空间信息系统发展阶段,即将空间信息系统的范畴从传统测绘空间扩展到宇宙空间、室内空间、微观空间等可量测空间。位置大数据不仅是全空间信息系统的重要研究对象之一,而且也成为了广域全空间中了解人们生活方式以及城市动态变化的一种有效途径。本文基于社交媒体数据中的位置签到数据,提出一种不同于传统以社会经济属性为依据的城市人群分类方法。首先利用签到数据的时间序列构造矩阵模型;然后,通过分析用户签到活动的时间特征,采用K-means聚类算法和K近邻算法（K-NN）识别出具有不同时空行为特征的城市人群（静态居民、动态居民、通勤者以及访问者）;最后,本文根据得到的人群分类结果,通过分析不同类型人群的时空间行为特征,发现不同类型人群时空间行为的差异性与潜在规律性,从而为表征城市人群的组成结构及特征,研究城市时空结构提供一种新的视角。