江西省交通区位演变与区域发展效应

选择江西省作为典型研究区域,采用历史分析与空间分析方法,基于长时间序列研究不同交通运输阶段交通区位的动态演变过程,并对其所引致的区域发展特征进行分析.结论是:在传统交通运输阶段向近代化及现代化交通运输阶段转变过程中,江西省宏观交通区位分别表现为国家南北交通控扼中枢,铁路建设的滞后区与周边发达地区间的交通过境区,可达性演变特征则由南北方向可达性的优势区逐步演化为滞后区.不同时期相对应的区域发展特征表现为,从传统运输阶段的区域商贸全面繁荣,走向近代工业化进程滞后及当前经济发展边缘化.在总结典型案例基础上,尝试从成因层,演变层,作用层,效应层和反馈层五个层面探讨交通区位与区域发展的内在作用关系.     

中国省际人口迁移的多边效应机制分析

区际人口迁移不仅与迁出地和目的地的要素特征以及距离有关,而且还受到周边迁移流的影响.基于网络自相关理论,利用"六普"省际人口迁移数据和相关统计资料,在重力模型的基础上考虑迁移流之间可能存在的几种空间依赖形式,构建中国省际迁移流的空间OD模型,初步揭示区域经济社会等因素及其空间溢出效应对省际人口迁移的影响,并就区域要素变化对整个省际人口迁移系统产生的"连锁反应"进行了模拟.结果表明:① 中国省际迁移流之间存在显著的网络自相关效应.目的地和迁出地的自相关效应皆为正,导致迁入和迁出流的空间效仿行为;迁出地和目的地周边则出现负的自相关效应,导致迁移流的空间竞争行为;② 区域经济社会等因素通过网络空间关系对周边地区产生的多边溢出效应导致迁移流在空间上集聚.其中,距离衰减效应位居各要素之首,其溢出效应进一步加剧距离的摩擦作用;对目的地而言,区域工资水平和迁移存量超过GDP的影响并产生正的溢出效应,促进周边地区吸引更多的外来人口;对迁出地而言,人口规模和迁移存量产生正的溢出效应,推动周边地区人口外迁;③ 区域要素变化潜在地对整个省际人口迁移系统产生一系列"连锁反应",震荡中心及其周边区域的迁移流波动较大.江苏省GDP增长5%的模拟结果表明,江苏迁往全国其他省份的人口数量都有不同程度地减少,而其他省份入迁人口均有所增加.相对而言,江苏周边省份的迁入或迁出流受到的波动较大,偏远省份波及较小,这是传统的重力模型所无法解释的.     

Vulnerability of Kuwait coast to sea level rise

Assessing coastal vulnerability to sea level rise (SLR) at local and regional scales is a fundamental step for designing successful long-term coastal management plans. This study was thus designed to assess Kuwait coastal vulnerability to SLR at four scenarios (.5, 1, 1.5 and 2 m). Potential inundated areas and the number of people at risk were estimated based on these SLR scenarios. A coastal vulnerability index (CVI) map of Kuwait was then computed based on the lowest scenario using eight parameters: elevation, coastal slope, geomorphology, distance to 20-m isobath, population, land use, cultural heritage and transportation. The geographic distribution of inundated areas at an SLR of .5 m revealed that the northern islands of Kuwait and coastal areas along Kuwait Bay would be highly impacted, whereas the coastal area near Shuaibah Port was the most influenced among the southern coasts. Most of the coastal area exhibited a moderate vulnerability to SLR, especially the northern islands. This study presented an initial vulnerability assessment for Kuwait coasts to SLR, which can be extended with more variables. The integrated remote sensing and geographic information system methodology demonstrated in this study can be applied in similar studies elsewhere.     

Why GPS makes distances bigger than they are

Global navigation satellite systems such as the Global Positioning System (GPS) is one of the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation errors. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS; the distance between two points recorded with a GPS is – on average – bigger than the true distance between these points. This systematic ‘overestimation of distance’ becomes relevant if the influence of interpolation error can be neglected, which in practice is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and illustrate that it functionally depends on the autocorrelation of GPS measurement error (C). We argue that C can be interpreted as a quality measure for movement data recorded with a GPS. If there is a strong autocorrelation between any two consecutive position estimates, they have very similar error. This error cancels out when average speed, distance or direction is calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine C in real-world GPS movement data sampled at high frequencies. We apply our approach to pedestrian trajectories and car trajectories. We found that the measurement error in the data was strongly spatially and temporally autocorrelated and give a quality estimate of the data. Most importantly, our findings are not limited to GPS alone. The systematic bias and its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.     

Land-surface segmentation as a method to create strata for spatial sampling and its potential for digital soil mapping

Sampling efforts are constrained by limited availability of resources. Therefore, methods to reduce the number of samples, while still achieving reasonable accuracy are needed. Land-surface segmentation (LSS) has proven a powerful technique to partition digital elevation models (DEMs) and their derivatives into relatively homogeneous areas, which can be further employed as support in soil sampling. Though topography is one of the main soil forming factors, a robust assessment of the potential of this technique to digital soil mapping (DSM) is still missing. In this study, we aimed at evaluating the potential of LSS in stratifying a landscape into relatively homogeneous areas, which can be used as strata for guiding the selection of sampling points in DSM. The experiments were carried out in two study areas where soil samples were available. Land-surface derivatives were derived from DEMs and segmented with a tool based on the multiresolution segmentation algorithm, into objects considered as homogeneous soil-landscape divisions. Thus, one sample was randomly selected within each segment from the existing sample data, based on which predictions of soil classes/sub-orders and properties, i.e. soil texture and A-horizon thickness, were made. Results were compared with predictions based on simple random sampling (SRS) and conditioned Latin hypercube (cLHS). The segmentation-based sampling (SBS) scheme performed better than SRS and cLHS schemes in predicting the A-horizon thickness, soil texture fractions and soil classes, showing a high potential of LSS in stratifying a landscape for the purposes of DSM. The novelty of this study is in the way strata are constructed, rather than in the sampling design itself. Further research is needed to demonstrate the value of a SBS design for practical use. The analyses presented here further highlight the importance of considering locally adaptive techniques in optimization of sampling schemes and predictions of soil properties.     

Unsupervised regionalization of the United States into landscape pattern types

We present a pattern-based regionalization of the conterminous US – a partitioning of the country into a number of mutually exclusive and exhaustive regions that maximizes the intra-region stationarity of land cover patterns and inter-region disparity between those patterns. The result is a discretization of the land surface into a number of landscape pattern types (LPTs) – spatial units each containing a unique quasi-stationary pattern of land cover classes. To achieve this goal, we use a recently developed method which utilizes machine vision techniques. First, the entire National Land Cover Dataset (NLCD) is partitioned into a grid of square-size blocks of cells, called motifels. The size of a motifel defines the spatial scale of a local landscape. The land cover classes of cells within a motifel form a local landscape pattern which is mathematically represented by a histogram of co-occurrence features. Using the Jensen–Shannon divergence as a dissimilarity function between patterns we group the motifels into several LPTs. The grouping procedure consists of two phases. First, the grid of motifels is partitioned spatially using a region-growing segmentation algorithm. Then, the resulting segments of this grid, each represented by its medoid, are clustered using a hierarchical algorithm with Ward’s linkage. The broad-extent maps of progressively more generalized LPTs resulting from this procedure are shown and discussed. Our delineated LPTs agree well with the perceptual patterns seen in the NLCD map.     

1971-2010年若尔盖湿地潜在蒸散量及地表湿润度的变化趋势

利用若尔盖、红原、玛曲3个气象站1971-2010年的地面气象观测资料,根据Penman-Monteith模型计算了若尔盖湿地的潜在蒸散量,发现若尔盖湿地年潜在蒸散量呈明显上升的趋势,上升趋势为9.1 mm/10a;若尔盖湿地潜在蒸散量在2001年出现了增大突变,2001-2010年平均潜在蒸散量比1971-2000年上升了28.6 mm;各季节潜在蒸散量均呈上升趋势,其中以秋季上升最明显,上升趋势为4.3 mm/10a。导致若尔盖湿地潜在蒸散量上升的主要气象因子是温度上升、相对湿度下降和降水量的减少,虽然日照时数减少和风速减小有利于潜在蒸散量的下降,但由于气温上升的趋势更明显,影响更大,所以若尔盖湿地潜在蒸散量呈明显的上升趋势。近40 a若尔盖湿地地表湿润度以-0.03/10a的趋势减小,其中2001-2010年比1981-1990年下降了0.11,下降十分明显;与此同时,年平均气温以0.41℃/10a的趋势上升,降水量以-13.5 mm/10a的趋势减少,虽然若尔盖湿地仍属于湿润区,但出现了明显的暖干化趋势。     

江南丘陵区农村居民点地域分异特征及类型划分——以长沙市为例

以长沙市2013年Landsat TM遥感影像图和长沙市的行政区划矢量图为基础,借助于ERDAS、MapGIS、ArcGIS、Fragtats、SPSS软件,运用探索性空间数据分析、景观形状指数模型及聚类分析法,从空间、规模与形态等方面探讨长沙市农村居民点地域分异特征、地域类型及调控路径。研究表明：长沙市农村居民点地域分异特征明显,在空间分布上呈现出由长沙市中部向东西两侧呈阶梯状稀疏化分布;而规模普遍偏小,规模分布自西向东呈现“波浪状”结构;在形态格局上,则表现为布局分散且形态复杂、不规则。通过采用聚类分析方法,将长沙市农村居民点划分为低密度散点型、低密度团簇型、中密度团簇型、高密度团簇型、中密度团块型、中密度条带型、高密度条带型7种,并从人口集聚、产业发展、基础设施与公共服务设施提升等方面提出了各地域类型优化调控的路径。     

A Statistical Analysis of Trends for Warm and Cold Spells in Uppsala by Means of Counts

Statistical studies of extremes are of interest in the climatic sciences, in particular trends of periods of unusually warm or cold weather, which could be labelled warm and cold spells, respectively. We study the yearly number of spells in Uppsala, Sweden which from a data‐analytic point of view truly are counts, and employ theory and methods from the field of regression models for counts. A possible trend for the period 1840–2012 was investigated. The trend for warm spells is positive and demonstrated to be larger in magnitude compared with the one for cold spells, and is found to be statistically significant. The methodology could be extended to analyse other climate indicators.     

中国经济空间格局演化与区域产业变迁——基于1952～2010年省区经济份额变动的实证分析

从区域经济份额变化出发,分析了1952~2010年中国经济空间格局的演化过程。通过有序样本聚类的方法,将省区经济份额变动划分了8个阶段,并采用偏离-份额法分析了不同阶段驱动省区经济份额上升的产业及其变迁,以及与区域产业结构和竞争优势变化的关系。研究发现,1949年以来中国区域经济布局总体上经历了沿海-内地均衡发展、沿海化非均衡发展以及东、中、西部相对均衡发展的过程,且整体上区域经济份额变动趋于减小。1979年以前,不同区域的经济份额在不同阶段变化差异较大,主要由第一和第二产业驱动。1979年以后,区域经济格局呈现明显的沿海化趋势,但驱动产业在不同阶段有所变化。2001年以来,中国区域经济格局经历了重大调整,经济份额上升的省区明显由东部向中、西部地区转移。东部省区在第二产业上仍具有明显的结构优势,但第二产业竞争优势已由东部向中、西部地区转移。东部的北京、上海、广东在第三产业上呈现更加显著的结构优势,显示出结构升级和服务化的发展态势。