Spatial downscaling of climate variables using three statistical methods in Central Iran

Spatial downscaling methods are widely used for the production of bioclimatic variables(e.g. temperature and precipitation) in studies related to species ecological niche and drainage basin management and planning. This study applied three different statistical methods, i.e. the moving window regression(MWR), nonparametric multiplicative regression(NPMR), and generalized linear model(GLM), to downscale the annual mean temperature(Bio1) and annual precipitation(Bio12) in central Iran from coarse scale(1 km × 1 km) to fine scale(250 m ×250 m). Elevation, aspect, distance from sea and normalized difference vegetation index(NDVI) were used as covariates to create downscaled bioclimatic variables. Model assessment was performed by comparing model outcomes with observational data from weather stations. Coefficients of determination(R2), bias, and root-mean-square error(RMSE) were used to evaluate models and covariates. The elevation could effectively justify the changes in bioclimatic factors related to temperature and precipitation. Allthree models could downscale the mean annual temperature data with similar R2, RMSE, and bias values. The MWR had the best performance and highest accuracy in downscaling annual precipitation(R2=0.70; RMSE=123.44). In general, the two nonparametric models, i.e. MWR and NPMR, can be reliably used for the downscaling of bioclimatic variables which have wide applications in species distribution modeling.

Empirical-Statistical Methodology and Methods for Modeling and Forecasting of Climate Variability of Different Temporal Scales

Main problem of modern climatology is to assess the present as well as future climate change, For this aim two approaches are used: physic-mathematic modeling on the basis of GCMs and palaeoclimatic analogues. The third approach is based on the empirical-statistical methodology and is developed in this paper. This approach allows to decide two main problems: to give a real assessment of climate changes by observed data for climate monitoring and extrapolation of obtained climate tendencies to the nearest future (10-15 years) and give the empirical basis for further development of physic-mathematical models. The basic theory and methodology of empirical-statistic approach have been developed as well as a common model for description of space-time climate variations taking into account the processes of different time scales. The way of decreasing of the present and future uncertainty is suggested as the extraction of long-term climate changes components in the particular time series and spatial generalization of the same climate tendencies in the obtained homogeneous regions. Algorithm and methods for realization of empirical-statistic methodology have been developed along with methods for generalization of intraannual fluctuations, methods for extraction of homogeneous components of different time scales (interannual, decadal, century), methodology and methods for spatial generalization and modeling, methods for extrapolation on the basis of two main kinds of time models: stochastic and deterministic-stochastic. Some applications of developed methodology and methods are given for the longest time series of temperature and precipitation over the world and for spatial generalization over the European area.

A Statistical Approach for Predicting Grassland Degradation in Disturbance-Driven Landscapes

Maintaining a land base that supports safe and realistic training operations is a significant challenge for military land managers which can be informed by frequent monitoring of land condition in relation to management practices. This study explores the relationship between fire and trends in tallgrass prairie vegetation at military and non-military sites in the Kansas Flint Hills. The response variable was the long-term linear trend (2001-2010) of surface greenness measured by MODIS NDVI using BFAST time series trend analysis. Explanatory variables included fire regime (frequency and seasonality) and spatial strata based on existing management unit boundaries. Several non-spatial generalized linear models (GLM) were computed to explain trends by fire regime and/or stratification. Spatialized versions of the GLMs were also constructed. For non-spatial models at the military site, fire regime explained little (4%) of the observed surface greenness trend compared to strata alone (7% - 26%). The non-spatial and spatial models for the non-military site performed better for each explanatory variable and combination tested with fire regime. Existing stratifications contained much of the spatial structure in model residuals. Fire had only a marginal effect on surface greenness trends at the military site despite the use of burning as a grassland management tool. Interestingly, fire explained more of the trend at the non-military site and models including strata improved explanatory power. Analysis of spatial model predictors based on management unit stratification suggested ways to reduce the number of strata while achieving similar performance and may benefit managers of other public areas lacking sound data regarding land usage.

Estimating inter-regional trade flows in China： A sector-specific statistical model

China has huge differences among its regions in terms of socio-economic development, industrial structure, natural resource endowments, and technological advancement. These differences have created complicated linkages between regions in China. In this study, building upon gravity model and location quotient techniques, we develop a sector-specific model to estimate inter-provincial trade flows, which is the base for making a multi-regional input-output table. In the model, we distinguish sectors with less intra-sector input from those with larger intra-sector input, and assume that the former sectors tend to compete among regions while the latter tend to cooperate among regions. Then we apply this new method of inter-regional trade estimation to three sectors： food and tobacco, metal smelting and proc- essing, and electrical equipment. The results show that selection of bandwidth has a significant impact on the assessment of inter-regional trade. Trade flows are more scattered with the increase of bandwidths. As a result, bandwidth reflects the spatial concentration of geo- graphical activities, which should be distinguishable for different industries. We conclude that the sector-specific spatial model can increase the credibility of estimates of inter-regional trade flows.

脑卒中住院患者性别、年龄及时空分布特征:基于宁夏地区19万例患者数据

背景脑卒中是血液无法流入大脑而导致脑组织受损的一种急性脑血管疾病,其致残率、致死率以及复发率较高,宁夏地区相关研究多为脑卒中临床相关分析,缺乏对近年宁夏地区脑卒中住院情况的变化趋势分析。目的分析宁夏地区脑卒中住院患者疾病流行特征及分布情况,为制定脑卒中综合防治措施提供依据。方法收集宁夏地区共55家中医及西医医院的病案首页数据,筛选出2013—2019年出院且主要诊断为脑卒中[国际疾病分类(ICD)-10编码为I60~I63]的数据进行分析。应用SPSS 24.0软件采用χ^(2)检验对性别及年龄进行差异性分析,应用地理信息系统采用全局空间自相关分析方法对宁夏地区2013—2019年脑卒中住院率数据的分布进行分析。结果最终纳入190634例脑卒中患者数据。2013—2019年宁夏地区脑卒中住院率呈逐年上升趋势(住院率分别为24.302/10000、30.045/10000、34.949/10000、39.397/10000、44.049/10000、47.617/10000、52.944/10000,χ_(趋势)^(2)=5.982,P=0.014),且在每年的寒冷季节住院率较高,每年3月份有1个明显的高峰,之后稍有下降。2013—2019年宁夏地区脑卒中住院患者中,缺血性脑卒中167194例,出血性脑卒中23440例;缺血性脑卒中和出血性脑卒中患者不同性别、年龄比较,差异有统计学意义(χ^(2)=473.533、7518.543,P<0.05);且从数据上看,缺血性脑卒中和出血性脑卒中男性、≥60岁住院患者所占比例均超过50%。全局空间自相关分析结果显示,2013年、2018年和2019年脑卒中住院率存在空间聚集性(P<0.05)。空间分布图显示,宁夏地区住院的脑卒中患者主要集中在宁夏中北部。结论2013—2019年宁夏地区脑卒中患者住院率表现为不断上升且存在不同程度的聚集性,住院率较高的地区集中在宁夏中北部。

基于时空变源分布式模型的流域洪水复盘分析方法——以海河流域北三河系“23·7”较大洪水为例

海河流域北三河系覆盖北京、天津、河北三省(直辖市),其防洪安全事关当地社会经济发展。为有效应对气候变化影响下极端暴雨频发所带来的洪水防御新挑战,提出了一种时空双维度流域性洪水复盘新思路,以“23·7”北三河系较大洪水为例,通过构建时空变源分布式水文模型结合暴雨时空统计分析对全河系洪水的发生、发展过程进行了模拟。模拟结果的确定性系数均大于0.7,洪峰相对误差均小于5%,所建模型可以较好地表征此次洪水的产流机制变化。分析发现,此次洪水过程中北运河流域产流以蓄满产流机制为主,潮白河流域产流则蓄满及混合产流两种机制均有体现,特别是靠近降雨集中区的山区平原过渡地带表现出明显的混合产流机制。建议在未来进一步完善流域防洪体系,重点提升该区域的水雨情监测能力。所提出的时空双维度下的流域性洪水复盘分析方法可为今后其他流域级洪水模拟复盘提供技术参考。

基于统计和自适应ParNet的产学研绩效评价

针对现有产学研绩效评价体系及方法中存在的评价指标覆盖范围单一、评价样本特征表达不充分、评价模型自优化能力待提高的问题,提出主客观产学研综合绩效智能评价的评价体系及方法。首先,围绕三方合作主体,挖掘产学研合作过程中影响绩效的要素及这些要素之间的联系,自主构建主客观产学研绩效三级评价体系;其次,通过将收集到的离散序列评价样本映射至极坐标空间、马尔可夫转移矩阵等不同高维空间域,增强离散样本特征表征;然后,通过基于精英反向翻筋斗觅食的混沌优化策略设计,提高深度模型冗余压缩和超参数的全局寻优效率,构建轻量压缩及高维超参数的自适应寻优的ParNet(AParNet)分类模型;最后,将模型应用于产学研绩效评价中,实现高性能的绩效智能评价。实验结果表明,所提方法很好地贴合了离散序列非线性分类应用,同时模型中加入优化策略后,在减少计算量的同时提高了分类性能,具体体现在:与ParNet相比,AParNet中的参数量减少了10.8%,较好地实现了模型的压缩,且它在产学研绩效评价中的分类准确率可达到98.6%。在产学研绩效智能评价应用中,该方法提高了评价模型的自适应能力,能够实现准确、高效的产学研绩效评价。

物资集中招标采购成本控制方法研究

集中招标采购提高了大型企业集团的议价能力,有助于降低企业采购成本。文章基于多价值链视角挖掘采购成本的关键性影响因素,以某大型企业集团钢铁类物资集中招标采购为例,通过空间统计分析和构建空间面板模型,分析关键性影响因素对企业集团下属各项目单位采购成本的差异性,并提出该采购模式下的成本控制策略,有助于其他企业集团结合下属各项目单位的区域特性统筹资源调度,进行成本管控。

Delineation of spring capture zones in southern Great Basin,USA based on modeling results and geochemical data

The protection zones or capture zones of springs in desert environments can be hard to identify,but they are critical to spring protection.Most springs fed by regional aquifers are susceptible to contamination and groundwater development.The U.S.Environmental Protection Agency has established hydrogeologic mapping methods to delineate protection zones for springs.However,it is often difficult to determine a regional aquifer system's flow pattern with this technique alone,and the use of these methods is not conducive to efficient groundwater management.Particle tracking analysis using a well-conceptualized and calibrated numerical model for the three-dimensional groundwater flow domain feeding a given group of springs can help facilitate the identification of spring capture zone boundaries.Building upon this basis,a multifaceted approach was developed to define clear boundaries of the capture zones for the springs in the Furnace Creek,Ash Meadows,and the Muddy River areas in the southern Great Basin,USA.Capture zones were first delineated from inverse particle tracking and Hydrologic Unit 12 watersheds.Afterwards,they were adjusted based on water budgets,geology,and hydrologically significant faults.Finally,a geochemical analysis of the groundwater chemistry and isotopic data was conducted to verify the extent of each spring capture zone.This multifaceted approach adds confidence to the new delineations.

Model Builder建模在土地勘测定界面积计算和汇总中的应用

Model Builder可将空间数据、地理处理及统计分析工具以流程图的形式组合起来,快速轻松地实现各种地理数据处理与应用。为此,文章提出一种利用Model Builder组合"相交、汇总统计数据"等工具完成勘测定界面积汇总的方法。实践证明,该方法能减轻人工编辑工作量,减少错误,提高工作效率。

基于广义协同高斯过程模型的结构不确定性量化解析方法

结构不确定性量化是定量参数不确定性传递到结构响应的不确定性大小。传统的蒙特卡洛法需要进行大量的数值计算,耗时较高,难以应用于大型复杂结构的不确定性量化。代理模型方法是基于少量训练样本建立的近似数学模型,可代替原始物理模型进行不确定性量化以提高计算效率。针对高精度样本计算成本高而低精度样本计算精度低的问题,该文提出了整合高、低精度训练样本的广义协同高斯过程模型。基于该模型框架推导了结构响应均值和方差的解析表达式,实现了结构不确定性的量化解析。采用三个空间结构算例来验证结构不确定性量化解析方法的准确性,并与传统的蒙特卡洛法、协同高斯过程模型和高斯过程模型的计算结果对比,结果表明所提方法在计算精度和效率方面均具有优势。

基于北京市区实测数据的雷暴风风场时空统计特性研究

雷暴风引发的结构破坏、财产损失和人员伤害屡有发生,其风场特性的认识和研究是结构抗雷暴风设计的基础。该文基于北京市区325 m高气象塔实测数据识别的70个雷暴风事件,采用经典雷暴风模型对其平均风及脉动风时空特性等进行了统计分析,研究结果表明:雷暴风频发于夏季,其峰值风速主要出现在北京时间的下午及傍晚时分且较易发生于西北方向;在峰值风速发生时段,平均风速剖面均值呈现轻微的鼻形特征;雷暴风湍流强度和阵风因子剖面与大尺度稳态强风特性存在较大差异,且雷暴风统计结果均大于大尺度稳态强风的对应值;北京市结构抗雷暴风设计中可考虑应用Von Karman谱计算雷暴风荷载。该研究结果可为雷暴风风场统一模型的建立和结构抗雷暴风设计提供参考。

黄河中下游沿黄城市水资源承载力贴近度空间结构及作用机理

采用贴近度测度黄河中下游31个沿黄城市水资源承载力,分析承载力贴近度的空间特征和空间作用机理。结果表明:黄河中下游流域城市水资源承载力整体偏低,尤其是下游流域沿黄城市的水资源供需矛盾尖锐;从空间结构特征全局看,31个沿黄城市水资源承载力整体呈现空间自相关特征,从局部看,大部分城市无分异特征,但中游流域出现热点区域,下游流域尤其是河南省出现低洼区域;通过沿黄城市水资源承载力贴近度空间作用机理分析,中游流域沿黄城市水资源承载力受到驱动力(D)、压力(P)、状态(S)、影响(I)、管理(M)5个因素的影响,且城市之间存在空间虹吸效应,水资源表现出空间竞争,而不是空间协同发展。下游流域沿黄城市水资源承载力只受到驱动力(D)、状态(S)、影响(I)3个因素的作用。研究成果可为沿黄城市加强水资源的管理、优化城市发展模式提供参考。

The Spatial Statistics Analysis of Housing Market Bubbles

With the incorporation of spatial statistic method, this paper constructs a state-space model of housing market bubbles, discussing the spatial pattern of housing market bubbles in China,and identifying the dynamic evolution process. The results show that: The bubbles of housing market walked along a path from low level to high level and then downsized to a low level during the period of 2009 and 2014, and the highest level stayed at 2011. From overall, the level of housing market bubbles had shown significant spatial autocorrelation and spatial agglomeration. In detail, the direction of North-South in China showed the inverted U shape, i.e., Central region was with high bubbles, and two ends contained low bubbles; from East-West direction, the East had high bubbles and the West contained comparatively low bubbles. Local spatial test indicates that there were some approximate spatial features in housing market bubbles among the adjacent regions. Observed from the level of housing market bubbles, China contained 3 plates: The first was the plate with low bubble level,including 3 provinces in North-East China(provinces of Jilin, Heilongjiang and Liaoning were included,but Dalian in Liaoning province was excluded; the second was the Central and West plate(the provinces of Yunnan, Guizhou, Sichuan, Guangdong, Guangxi, Hunan, Hubei, Gansu, Fujian, Jiangxi and Hainan were included in this plate), which was also featured with low bubble; and the third was Central East plate(provinces or provincial regions of Beijing, Tianjin, Hebei, Jiangsu, Zhejiang, Shanghai, Shandong,Anhui, Shanxi, Shaanxi and Inner Mongolia were included), which was characterized as high bubble region.

基于空间分析的保护生物学研究

保护生物学家和生态学家早就认识到只有准确地辨识保护对象的空间位置、范围、及其相邻的关系 (例如边缘 )和连接度 ,以及依存的地形和气候等生境条件 ,才能发现生物种群和生境在空间的扩散与收缩、增长与灭绝的动态 ,揭示分布的格局 ,从而系统、全面地了解保护对象和生境的存在状态、破碎程度和变化趋势 ,进行有效的自然保护。得益于新兴的空间分析技术 ,保护生物学自 2 0世纪 90年代以来取得了很大的进步。基于空间分析的保护生物学研究是最近 10年左右大力发展的新保护生物学的重要基础。该文结合作者的研究工作 ,综述了基于空间分析的保护生物学项目 ,探讨了保护生物学发展历史、主要研究方法与应用、以及今后的可能发展趋势。在生物多样性的丰度和分布的空间解绎部分 ,通过综述世界保护监测中心的图解全球生物多样性的工作 ,如国家尺度的生物多样性水平、植物多样性的分布中心和维管束植物科的多样性等的空间分布 ,介绍了Dobson等图示美国主要濒危植物、鸟类、鱼类和软体动物等 4个主要类群在县 (County)为基本空间单位上分布的空间格局 ,讨论了生物多样性空间解绎的意义。在第二部分用世界资源研究所的全球森林监测 (Globalforestwatch)项目 ,美国的国家保护缺失区分析 (GAPanalysis)项目 。

利用交叉信息熵模拟东北地区水稻种植面积空间分布

作物时空分布变化是农业研究的重要内容。近30a来,东北地区水稻种植面积显著增加,为探讨东北地区水稻时空变化特征,进一步丰富和完善作物空间分布信息获取方法,研究作物空间分布对包括气候变化在内的多种影响因素的响应关系,该研究综合80年代以来的作物面积与产量统计数据、耕地数据、农业灌溉数据以及作物生长适宜性分布等多源数据,利用基于交叉信息熵原理的作物空间分配模型(spatial production allocation model,SPAM)构建了针对中国作物分布特点的SPAM-China模型,模拟了中国东北地区1980-2008年像元尺度上水稻空间分布信息。结果表明,模拟结果能较好地反映出东北地区水稻主要种植区域,近30a东北地区水稻种植时空变化特征显著,水稻种植区域向北向东扩展,种植重心北移了约1.76个纬度,中北部地区水稻种植面积增加且趋势明显,南部地区变化趋势不显著。

空间分析中空间自相关性的诊断

目的大部分流行病学资料具有空间属性,即空间相关性和空间异质性,本研究拟从空间自相关统计量、变异函数图、空间自回归模型等3方面探讨具有空间属性资料的空间自相关性分析方法。方法分别阐述空间自相关统计量、变异函数图和空间自回归模型的原理和计算方法,对其各自的特点进行比较,并以2009年广东省手足口病的发病为例阐述软件实现的过程以及对参数和统计量的解释。结果基于广义Pearson相关系数的空间自相关统计量可以定量地分析疾病在空间上的聚集状态以及相关程度;变异函数可以对疾病发生的空间变异进行分解,并解释由空间自相关部分引起的变异大小;空间自回归模型则可以扣除间间自相关性的影响后分析影响因素的作用大小。结论空间自相关统计量、变异函数图、空间自回归模型是定性和定量地分析空间自相关性的方法,三者结合可以很充分地分析疾病的空间自相关性。

山东省人口密度分布模式的GIS空间分析

利用2010年山东省第六次人口普查数据,借助GIS技术,运用空间统计分析方法,对山东省17地市人口空间分布模式进行分析。采用四分位数计算方法和Moran’s I和Local Moran’s I指数研究方法分析可知,2010年山东省17地市人口密度的空间分布模式存在空间集聚现象,总体上以济南、淄博为中心大致划分为高密度区、较高密度区和较低密度区3个空间条带形区域,没有出现高值和低值异常的空间区域;山东省17地市人口密度呈正的空间自相关,空间分布并非处于完全的随机状态,表现出相似值之间的空间聚集:西部和南部5个地区存在着明显的"高-高"集聚;北部4个地区存在着明显的"低-低"集聚;存在着莱芜和日照两个"低-高"关联及潍坊市"高-高"关联的孤立点。

空间分析研究进展

空间分析从地理空间的视角描述和分析问题,是地理学日益受关注的研究手段与方法,已引起其他相关学科的重视。该文阐述了空间分析的概念、内容、分类与定义,沿着1960s以来空间分析的发展脉络,分为3个阶段:1960s对空间数据的分析和空间自相关的测度,1970s-1980s空间统计学的迅速发展时期,1990s到本世纪初,空间分析与GIS和智能计算相互结合产生巨大的应用潜力,计算密集型的特点明显。结合各阶段典型的、具有代表性的文献资料,阐述不同时期的主要特点与成就,最后总结了空间分析的发展趋势和相关问题。

喀斯特石漠化小流域景观的空间因子分析——以贵州清镇王家寨小流域为例

以贵州喀斯特地区的王家寨小流域为研究对象,基于多源信息,依托RS和GIS技术获取2005年该流域石漠化景观格局信息,以此为基础进行石漠化景观分布指数、石漠化综合指数、-X-s平面分析模型以及三次曲线拟合等分析,旨在从小流域尺度上探讨石漠化景观在坡度、坡向、高程和与村庄距离等空间因子上的分布规律。结果表明:潜在、轻度石漠化景观受坡度影响最显著;其他类型石漠化景观受坡向影响最明显。石漠化程度先随坡度的增大而加重,27°后呈缓解趋势;各坡向中南、东南坡石漠化最严重;随高程增加石漠化加剧;距村庄越远石漠化越严重。初步推断各空间因子对石漠化程度的影响由强至弱的顺序为:坡度、坡向、高程、与村庄的距离。