广州市制造业与生产性服务业协同集聚与空间相似性

基于企业微观大数据,从空间相似性的视角,运用核密度法、双变量空间自相关法和地理探测器等分析方法,对广州市6个制造行业和5个生产性服务行业的空间协同关系展开分析,在街镇尺度下从行业层面探讨生产性服务业与制造业在城市内部的协同集聚.研究发现:行业集聚中心的空间布局形态显示,广州的制造业与生产性服务业布局具有空间相似性.制造...     

根据GPS和InSAR数据反演2001年昆仑山口西地震同震破裂分布

利用干涉合成孔径雷达（interferometric synthetic aperture radar, InSAR）技术可以获取空间连续的地表形变监测结果,但仅能得到真实三维形变在雷达视线方向上的一维投影,降低了地表形变解译的可靠性。全球导航卫星系统（global navigation satellite system, GNSS）可提供高精度的三维形变结果,但其空间分辨率较低。因此可通过融合InSAR和GNSS来实现高精度高空间分辨率的三维地表形变监测。提出利用方差分量估计(variance component estimation, VCE)方法对经典的联合大地和卫星同步形变测量值的应变张量估计(simultaneous and integrated strain tensor estimation from geodetic and satellite deformation measurements, SISTEM)方法进行改进,发挥SISTEM方法可以考虑相邻点地表形变的空间相关性的优势,优化了函数模型中InSAR和GNSS观测的构成比例,引入VCE方法精确估计了各类观测值的后验方差,进而实现了高精度的三维地表形变测量。首先通过设计模拟实验对所提方法进行了验证,然后将该方法应用于2007年夏威夷基拉韦厄火山喷发案例中。相较于SISTEM方法,该方法在东西、南北、垂直向解算的形变精度均有大幅提升。     

基于空间组合特征的农村居民点重构方向识别

农村居民点空间优化重构是推进乡村振兴的重要内容之一。以中国湖北省鄂州市为研究区,从人的生存生活需求出发,构建宜居性评价指标体系,评估鄂州市宜居性高低;通过网络分析法构建城乡人口流动网络,发现乡村人口流动规律。在此基础上,综合宜居性和人口流动强度的组合特征进行农村居民点空间重构。结果表明：(1)鄂州市整体宜居性较高,其中,生存保障功能差距较小,而生活服务功能和生活提升功能差距较大;(2)人口按照主城区、所属镇、附近优势城镇的优先顺序流动;城镇辐射范围有限,现有的点轴结构不能起到以点带面的作用,亟需发展中心村、一般村、基层村形成完整的乡村结构,带动全域发展;(3)基于乡村宜居性和人口流动强度将居民点划分为搬迁撤并类、城郊融合类和集聚提升类(中心村、一般村和基层村)。该研究可重塑乡村发展核心,完善乡村结构,促进资源优化配置,为人口快速流动区乡村聚落重构提供决策依据。     

利用多分辨率半边进行全球多分辨率DEM无缝表达

矢量化的复杂建筑平面图纸受各类图元的干扰,其空间布局识别难度非常高,然而在5G基站建设、智能家居以及AR/VR（augmented reality/virtual reality）中广为应用。提出了一种结合栅格图像和矢量表示的高性能融合对偶识别方法。首先,采用建筑平面图纸的栅格化表示,提取建筑图纸的主要发展方向,构建分割的若干个封闭区域空间。然后,采用建筑平面图纸的矢量化表示,利用空间识别的有向性和邻接特性,创新地提出使用半墙数据结构进行墙体几何位置和拓扑关系的计算,从而根据空间对偶原理高精度地整体重建墙体布局。最后,获取包含各类墙体布局的矢量建筑平面图数据集,并进行方法的实验验证。实验结果证明了所提出的先空间后墙体的融合对偶识别算法对于各类建筑模型类型均具有可用性和有效性。与传统先墙体后空间的识别方法相比,所提方法具有更高的鲁棒性,受特定建筑图纸类型的干扰更少。     

两种方法在地下水位估值中的应用

对于许多区域水资源问题,用数值方法进行潜水水流模拟时,需要给出每个节点上地下水位值.本文首先简单介绍了趋势面方法,然后着重阐述了泛克里格方法的基本原理及它们在地下水位估值中的应用,通过比较两种方法的计算结果可以得出泛克里格方法是进行地下水位估值的空间最优估计方法.     

生态地理建模中的多尺度问题

本文在分析生态地理建模内涵的基础上,讨论了生态地理建模中的尺度转换问题、跨尺度相互作用问题、空间尺度与时间尺度的关联问题和多尺度数据处理问题.由于生态地理问题的非线性、生态环境的异质性和随机事件,简单的线性尺度转换方法远不能满足生态地理建模的要求.为了从根本上解决生态地理建模中的时空尺度问题,除需要运用微分几何学和等级理论等经典方法外,还需要引入格点生成法和网格计算等现代理论和技术手段.     

Spatiotemporal estimation of snow depth using point data from snow stakes,digital terrain models,and satellite data

Snow availability in Alpine catchments plays an important role in water resources management. In this paper, we propose a method for an optimal estimation of snow depth (areal extension and thickness) in Alpine systems from point data and satellite observations by using significant explanatory variables deduced from a digital terrain model. It is intended to be a parsimonious approach that may complement physical‐based methodologies. Different techniques (multiple regression, multicriteria analysis, and kriging) are integrated to address the following issues: We identify the explanatory variables that could be helpful on the basis of a critical review of the scientific literature. We study the relationship between ground observations and explanatory variables using a systematic procedure for a complete multiple regression analysis. Multiple regression models are calibrated combining all suggested model structures and explanatory variables. We also propose an evaluation of the models (using indices to analyze the goodness of fit) and select the best approaches (models and variables) on the basis of multicriteria analysis. Estimation of the snow depth is performed with the selected regression models. The residual estimation is improved by applying kriging in cases with spatial correlation. The final estimate is obtained by combining regression and kriging results, and constraining the snow domain in accordance with satellite data. The method is illustrated using the case study of the Sierra Nevada mountain range (Southern Spain). A cross‐validation experiment has confirmed the efficiency of the proposed procedure. Finally, although it is not the scope of this work, the snow depth is used to asses a first estimation of snow water equivalent resources.     

Deducing the spatial variability of exchange within a longitudinal channel water balance

Developing an appropriate data collection scheme to infer stream–subsurface interactions is not trivial due to the spatial and temporal variability of exchange flowpaths. Within the context of a case study, this paper presents the results from a number of common data collection techniques ranging from point to reach scales used in combination to better understand the spatial complexity of subsurface exchanges, infer the hydrologic conditions where individual influences of hyporheic and groundwater exchange components on stream water can be characterized, and determine where gaps in information arise. We start with a tracer‐based, longitudinal channel water balance to quantify hydrologic gains and losses at a sub‐reach scale nested within two consecutive reaches. Next, we look at groundwater and stream water surface levels, shallow streambed vertical head gradients, streambed and aquifer hydraulic conductivities, water chemistry, and vertical flux rates estimated from streambed temperatures to provide more spatially explicit information. As a result, a clearer spatial understanding of gains and losses was provided, but some limitations in interpreting results were identified even when combining information collected over various scales. Due to spatial variability of exchanges and areas of mixing, each technique frequently captured a combination of groundwater and hyporheic exchange components. Ultimately, this study provides information regarding technique selection, emphasizes that care must be taken when interpreting results, and identifies the need to apply or develop more advanced methods for understanding subsurface exchanges. Copyright © 2013 John Wiley & Sons, Ltd.     

Spatiotemporal variability in hydrochemistry of shallow groundwater in a small pre‐alpine catchment: The importance of landscape elements

Topography and landscape characteristics affect the storage and release of water and, thus, groundwater dynamics and chemistry. Quantification of catchment scale variability in groundwater chemistry and groundwater dynamics may therefore help to delineate different groundwater types and improve our understanding of which parts of the catchment contribute to streamflow. We sampled shallow groundwater from 34 to 47 wells and streamflow at seven locations in a 20‐ha steep mountainous catchment in the Swiss pre‐Alps, during nine baseflow snapshot campaigns. The spatial variability in electrical conductivity, stable water isotopic composition, and major and trace ion concentrations was large and for almost all parameters larger than the temporal variability. Concentrations of copper, zinc, and lead were highest at sites that were relatively dry, whereas concentrations of manganese and iron were highest at sites that had persistent shallow groundwater levels. The major cation and anion concentrations were only weakly correlated to individual topographic or hydrodynamic characteristics. However, we could distinguish four shallow groundwater types based on differences from the catchment average concentrations: riparian zone‐like groundwater, hillslopes and areas with small upslope contributing areas, deeper groundwater, and sites characterized by high magnesium and sulfate concentrations that likely reflect different bedrock material. Baseflow was not an equal mixture of the different groundwater types. For the majority of the campaigns, baseflow chemistry most strongly resembled riparian‐like groundwater for all but one subcatchment. However, the similarity to the hillslope‐type groundwater was larger shortly after snowmelt, reflecting differences in hydrologic connectivity. We expect that similar groundwater types can be found in other catchments with steep hillslopes and wet areas with shallow groundwater levels and recommend sampling of groundwater from all landscape elements to understand groundwater chemistry and groundwater contributions to streamflow.     

Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models

This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km² located in the north‐west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase.