青藏高原城镇化格局的时空分异特征及影响因素

研究青藏高原城镇化格局的时空分异及其影响因素,有利于推动青藏高原现代人类活动时空过程的认知,对青藏高原就地就近城镇化及可持续发展具有参考意义。根据历次人口普查数据,本研究构建青藏高原县市尺度城镇化空间数据集,参考城镇化发展阶段,采用LISA空间类型划分法和空间计量回归模型,系统分析1990-2010年青藏高原内部城镇化格局的时空分异特征及影响因素。主要结论包括：① 青藏高原整体城镇化水平偏低,2017年底,青藏高原主体部分青海省和西藏自治区的常住人口城镇化水平分别为53.07%和30.8%,低于全国同期水平的58.52%,但青藏高原内不乏高水平城镇化地区,而且各地区间城镇化水平的空间差异缩小;② 青海西部柴达木盆地是高水平城镇化集聚区,羌塘地区是低水平城镇化集聚区,地级行政中心所在县市多呈现自身高、周边低的城镇化格局;③ 与内地相似,第二、三产业从业机会是推动青藏高原城镇化发展重要因素,社会公共服务资源对城镇化拉动作用开始凸显。研究结果可以为青藏高原人类活动研究和青藏高原就地就近城镇化可持续发展政策提供参考。     

中国城市环境污染监管水平的空间演化特征与影响因素

基于中国2010—2016年的地级市污染监管水平数据,采用空间自相关、位序-规模法则,空间计量模型等地学方法,分析中国地级城市污染监管水平的时空分异特征和影响因素及其空间溢出效应。结果表明：① 2010—2016年中国污染监管水平整体上呈现上升趋势且区域性和集聚性特征明显,东南部地区为稳定的高值集聚地区,中西部地区为稳定的低值集聚地区。② 中国城市污染监管水平属于次位型分布,监管规模分布的分散趋势大于集中趋势。③ 城市的人口密度、经济发展水平、第二产业占比等对城市污染监管水平有显著的正向影响,城市规模对污染监管水平存在负向的影响。④ 城市经济发展水平和城市人口密度对污染监管水平起到显著的直接效应;第二产业占比、二氧化硫排放量等起到了显著的间接效应。     

黄土边坡可靠度的随机有限元分析

为了研究黄土边坡的可靠度,在对黄土强度参数进行变异性统计、概率分布检验及垂直向自相关距离计算的基础上,将黄土强度参数黏聚力和内摩擦角作为随机变量,将其在二维边坡剖面的空间变化作为随机场,用局部平均细分法将符合一定均值和标准差的强度参数按随机场分布在边坡剖面的有限元网格上,再采用弹-理想塑性有限元计算边坡稳定系数,用MonteCarlo法计算其失效概率。结果表明:黄土黏聚力的变异系数多在0.30以上,内摩擦角的变异系数多在0.20以下,且黄土高原从西向东两个参数的变异性都增大;黏聚力和内摩擦角的概率分布类型对边坡失效概率计算结果影响显著,统计检验发现黏聚力采用对数正态分布、内摩擦角采用正态分布为宜;黄土地层的垂直向自相关距离多在0.7m以内,与计算厚度的比值多小于0.2;在大部分黄土的变异系数与自相关距离水平下,采用确定场进行可靠度分析,多数情况将极大地高估了黄土边坡的失效概率,而在极端高变异系数水平下,采用确定场进行可靠度分析,又将极大地低估其失效概率。     

新疆天山及北疆地区积雪反照率差异

新疆天山和北疆地区是我国三大稳定积雪区之一,积雪反照率的变化显著地影响其地表吸收的太阳辐射能量。2018年1~3月,在新疆天山和北疆地区进行了积雪反照率观测,发现研究区的积雪反照率存在明显的时空差异。时间上,由于受到气温变化的影响,研究区的积雪反照率整体呈现下降的趋势,而且不同时期的下降幅度有差异,1月末~3月初反照率的降低相比1月初~1月末反照率降低更加明显。空间上,由于受到污化物的影响,各区域（阿勒泰地区、塔城地区、天山北坡和伊犁河谷）的积雪反照率之间存在差异,其中天山地区（天山北坡和伊犁河谷）的积雪反照率低于北疆地区（阿勒泰地区和塔城地区）,天山北坡的反照率最低;在积雪稳定期及消融期,污化物对积雪反照率的影响最为明显。     

市域尺度货物运输碳排放时空变化及因素分析

针对货物运输导致碳排放成为温室气体主要来源之一的问题,该文综合货物运输车辆的微观温室气体排放及时空变化,从市域尺度分析货物运输碳排放的时空变化规律。利用微观排放模型计算了2000年、2005年、2010年和2015年全国286个城市货物运输碳排放的空间分布及其变化,并应用地理加权回归模型探究城市化不同层面因素对碳排放时空分布变化的影响。结果表明:货物运输碳排放具有显著的空间集聚特征,且高排放地区的集聚规律更加显著;地理加权回归模型精度明显高于普通线性回归模型,经济变量、人口变量、货运强度变量与货物运输碳排放存在显著正相关关系。该研究可为中国各市级区域制订节能减排政策提供量化的科学依据。     

Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes

Remote sensing has been proven promising in wetland mapping. However, conventional methods in a complex and heterogeneous urban landscape usually use mono temporal Landsat TM/ETM + images, which have great uncertainty due to the spectral similarity of different land covers, and pixel-based classifications may not meet the accuracy requirement. This paper proposes an approach that combines spatiotemporal fusion and object-based image analysis, using the spatial and temporal adaptive reflectance fusion model to generate a time series of Landsat 8 OLI images on critical dates of sedge swamp and paddy rice, and the time series of MODIS NDVI to calculate phenological parameters for identifying wetlands with an object-based method. The results of a case study indicate that different types of wetlands can be successfully identified, with 92.38%. The overall accuracy and 0.85 Kappa coefficient, and 85% and 90% for the user’s accuracies of sedge swamp and paddy respectively.     

基于BP网络与空间统计分析的山东人口空间分布模式预测研究

运用BP网络对山东省17地市未来人口总量进行预测,在预测数据的基础上运用空间自相关方法对未来人口的空间分布模式进行分析。研究表明:2005-2010年山东省人口密度的空间分布模式总体呈现"西南-东北"模式,存在着空间集聚现象;2006-2010年17地市局部空间关联类型基本没有发生变化,西部和南部8个地区存在着明显的"高-高"集聚;北部5个地区存在着明显的"低-低"集聚;淄博和青岛存在着两个"高-低"关联的孤立点。     

Spatial autocorrelation of West Nile virus vector mosquito abundance in a seasonally wet suburban environment

The objective of this study is to quantify and model spatial dependence in mosquito vector populations and develop predictions for unsampled locations using geostatistics. Mosquito control program trap sites are often located too far apart to detect spatial dependence but the results show that integration of spatial data over time for Cx. pipiens-restuans and according to meteorological conditions for Ae. vexans enables spatial analysis of sparse sample data. This study shows that mosquito abundance is spatially correlated and that spatial dependence differs between Cx. pipiens-restuans and Ae. vexans mosquitoes.      

Data-driven approach to characterize urban vitality: how spatiotemporal context dynamically defines Seoul’s nighttime

ABSTRACT

This study takes a data-driven approach to define urban nighttime by examining the spatiotemporal dynamics of urban vitality. Using micro-scale spatiotemporal analysis, this paper empirically provides a comprehensive, yet granular, picture of collective human behaviors in cities. Using Seoul, South Korea as a case study site, it prioritizes the spatiotemporal context in order to mitigate uncertain contextual effects inherent in such forms of data-driven analysis. Instead of leaving the data re-grouping up to researcher’s arbitrary decision, this paper employs a functional principal component analysis (FPCA) to systematically transform a set of discrete data to a continuous functional form. This paper applies FPCA on 24-hour-based dataset of pedestrian traffic in Seoul in order to make a data-driven extraction of principal components that characterize the city’s unique patterns of urban vitality. Extracting principal components allows for less statistically obvious phenomena to be measured that would have otherwise been hidden within the data. This approach proved successful in capturing nighttime vitality patterns that are eclipsed by the overwhelming trend of daytime patterns. Additionally, this paper compares differences between regions and seasons to examine what the differences can tell about the definition of nighttime.     

Temporal trends in δ18O composition of precipitation in Germany: insights from time series modelling and trend analysis

Temporal and spatial variations of stable oxygen (¹⁸O) and hydrogen (²H) isotope measurements in precipitation act as important proxies for changing hydro‐meteorological and regional and global climate patterns. Temporal trends in time series of the stable isotope composition in precipitation were rarely observed, and they are poorly understood. These might be a result of a lack of proper trend detection tools and effort for exploring trend processes. Here, we investigate temporal trends of δ¹⁸O in precipitation at 17 observation stations in Germany between 1978 and 2009. We test if significant trends in the isotope time series from different models can be observed. Mann–Kendall trend tests are applied on the isotope series, using general multiplicative seasonal autoregressive integrate moving average (ARIMA) models, which account for first and higher order serial correlations. Effects of temperature, precipitation, and geographic parameters on isotope trends are also investigated in the proposed models. To benchmark our proposed approach, the ARIMA results are compared with a trend‐free pre‐whitening procedure, the state of the art method for removing the first order autocorrelation in environmental trend studies. Moreover, we further explore whether higher order serial correlations in isotope series affects our trend results. Overall, three out of the 17 stations show significant changes when higher order autocorrelation are adjusted, and four show a significant trend when temperature and precipitation effects are considered. The significant trends in the isotope time series generally occur only at low elevation stations. Higher order autoregressive processes are shown to be important in the isotope time series analysis. Results suggest that the widely used trend analysis with only the first order autocorrelation adjustment may not adequately take account of the high order autocorrelated processes in the stable isotope series. The investigated time series analysis method including higher autocorrelation and external climate variable adjustments is shown to be a better alternative. Copyright © 2014 John Wiley & Sons, Ltd.