基于GIS的华北地区降雨量空间变异性

根据华北地区1971年至2000年48个气象站降水空间观测数据,其降水稀疏且分布 不均匀,应用地理信息系统IDRISI软件对降雨量进行空间变异性分析,首先将数据进行处理,生成矢量数据文件,使用IDRISI软件中空间依赖性建模器对矢量数据文件进行分析,生成半方差表面图和方向方差图,进而分析华北地区降雨量空间变异性,总结降雨分布规律.即在127度方向上降雨量变异性最大,在同一条37度斜线附近气象站的降雨量变异性最小.     

云南省县域地形起伏度与人口分布的耦合关系研究

用1∶20万的云南省数字高程模型,以ArcGIS 10.0这一软件平台,利用Spatial Analyst中的栅格邻域计算工具窗口分析方法提取出云南省县域尺度的地形起伏度,加以分析云南省地形起伏度的规律,结合地形起伏度与人口密度的耦合指数,在Geoda软件支持下对各区县地形起伏度与人口密度的耦合指数进行全局空间自相关分析和局部空间自相关分析。研究结果表明:云南省地形起伏度空间分布整体趋势为东南低于西北,南部低于北部,中部地区居中,地形起伏度数值随着海拔高度、坡度的增大而增大,地形起伏度与人口密度的多项式合曲线拟合度为0.521 7。其县域地形起伏度介于1.30~4.61之间,利用Geoda软件分析得出云南省地形起伏度与人口密度耦合指数的全局自相关Global Morans I指数为0.582 2,表明云南省地形起伏度与人口密度密度的空间分布存在较显著的集聚特征,对其局部空间的Local Morans I值进行显著性检验,局部空间自相关有一定差异性。     

宁夏区域经济空间差异的ESDA-GIS研究

基于ESDA全局和局部空间自相关分析,在ARCGIS软件和Geo DA空间统计分析软件的支持下,利用宁夏回族自治区2008年的县域人均GDP数据,对宁夏县域经济之间的总体和局部空间差异进行了实证分析。结果表明:全局Moran’s I指数为0.7426,在空间上相关性很强,且与经济发展水平有很大的关系,在空间上经济水平差不多的地区空间上分布较集中,并在此基础上绘制了局域Moran’s I散点图和LISA集聚图。     

甘肃省区域经济空间差异的ESDA-GIS分析

基于ESDA全局和局部空间自相关分析．在ArcView GⅠS软件和GeoDA空间统计分析软件的支持下，利用甘肃省2004年县域人均GDP数据，对甘肃省县域经济之间的总体和局部空间差异进行了实证分析．结果表明，甘肃省2004年总体上经济空间差异较大，经济重心与人口重心和几何中心均有较大偏移；全局Moran’s Ⅰ指数为0．3911，空间自相关特性明显，经济发展水平相似的地区在空间上集中分布．在此基础上绘制了局域Moran’S Ⅰ散点图和LⅠSA集聚图，并采用空间插值获得局域Ⅰ值分异图．     

昆嵛山腮扁叶蜂幼虫空间自相关研究

运用全局Moran’s Ⅰ和局部Moran’s Ⅰ方法对昆嵛山腮扁叶蜂幼虫空间分布的自相关性进行了研究。结果表明：昆嵛山腮扁叶蜂幼虫在样地的分布具有一定的空间自相关性,但未达到显著的程度,没有形成特定的空间分布格局;局部空间自相关分析结果显示也没有局部空间聚集现象。由于样地环境比较单一,因此只考虑昆嵛山腮扁叶蜂易感林木的空间自相关对昆嵛山腮扁叶蜂空间自相关的影响。用相关性分析昆嵛山腮扁叶蜂全局Moran’s Ⅰ值和易感林木全局Moran’s Ⅰ值的相关性可知,昆嵛山腮扁叶蜂全局空间自相关性受易感林木全局空间自相关性显著影响。     

云南省县域地形起伏度与人口分布的耦合关系研究

由于人口分布受地形因素的影响，本文用1:20万的云南省数字高程模型，以ArcGIS10.0 为软件平台，利用 SpatialAnalyst 中的栅格邻域计算工具窗口分析方法提取出云南省县域尺度的地形起伏度，加以分析云南省地形起伏度的规律，结合地形起伏度与人口密度的耦合指数，在 Geoda 软件支持下对各区县地形起伏度与人口密度的耦合指数进行全局空间自相关分析和局部空间自相关分析。研究结果：云南省地形起伏度空间分布整体趋势为东南低于西北，南部低于北部，中部地区居中，地形起伏度数值随着海拔高度、坡度的增大而增大，地形起伏度与人口密度的多项式合曲线拟合度为 0.5217。其县域地形起伏度介于 1.30~4.61 之间，利用 Geoda 软件分析得出云南省地形起伏度与人口密度耦合指数的全局自相关 Global Moran’s I 指数为 0.5822，表明云南省地形起伏度与人口密度密度的空间分布存在较显著的集聚特征，对其局部空间的 Local Moran’s I 值进行显著性检验，局部空间自相关有一定差异性。
     

全局空间自相关Moran指数和G系数对比研究

研究了最常用的衡量全局空间自相关的指标:全局Moran指数和全局G系数,基于模拟的空间区域,设计了一些有代表性的空间聚集方案进行计算,比较了两种指标的探测结果。得出的初步结论是:G系数探测高值聚集的能力强于低值聚集;当研究范围内同时存在高值和低值聚集时,G系数会受聚集区域规模的影响,当高值聚集区域和低值聚集区域规模相当时,G系数往往为正数,表明G系数对高值敏感;Moran指数主要受聚集区域规模的影响,随着空间聚集范围的扩展,Moran指数会明显增大。这些结论对于探测空间自相关模式时选择何种全局指标有指导意义。     

面向大范围降水空间插值的普通克里金模型开发与实例分析

基于Matlab平台,开发了面向大范围降水空间插值的普通克里金模型—Matlab based ordinary Kriging(MatOK).与已有模型相比,MatOK的主要特点是:(1)在降水空间变异函数计算环节,采用SCE-UA算法拟合理论变异函数;(2)在OK方程组估值环节,引入降水空间发生概率的计算,完善了日等短时间尺度降水空间估值方法;(3)通过对OK方程组的标准化处理,有效提高了模型数值计算的稳定性.同时,将MatOK初步应用于集水面积为83374km2的赣江流域年、月、日降水空间插值中,并重点讨论了MatOK的计算稳定性和计算效率,结果初步说明了采用MatOK进行大范围区域降水空间插值是可行的.     

近10年来乌鲁木齐市城市人口变动分析

基于2000年和2010年的全国人口普查数据,利用ArGIS和GS+Version 7软件对乌鲁木齐市街区人口数据空间化,分析其人口变动情况,并运用莫兰指数和半方差函数探索10年来各街区人口密度空间分布的相关性和变异性.结果表明,乌鲁木齐市人口增长迅速,且各辖区表现出"人口数量增长率大密度小,增长率小而密度大"的差异性格局;各街区人口增长分布不均匀,街道之间人口数量差异越来越明显;2000—2010年乌鲁木齐市各街区人口密度空间分布相关性分析显示呈正相关性,且相关性增大;空间变异性分析显示人口密度空间分布整体结构越来越稳定,但影响空间分布的随机因素不确定性也在增加.     

精河流域表层土壤盐分空间变异和格局分析

土壤盐渍化是影响干旱区农业可持续发展、制约绿洲稳定性的重要因素,分析土壤盐分对土壤盐渍化的动态变化有着重要意义。文章以精河流域为研究靶区,基于GIS和地统计学方法,研究了精河流域表层(0-10cm)土壤盐分的空间变异性。结果表明:(1)表层土壤盐分电导率平均值为6.13ms·cm-1,属于中度盐渍化类型,变异系数较高,呈强变异性;(2)土壤表层盐分变异函数最优模型为指数模型,决定系数0.766。空间变异比为9.4%,表层盐分变异与空间强烈相关,格局受制于土壤自身性状;(3)研究区表层土壤盐分空间上整体呈条带状分布,局部表现为斑块格局;(4)土壤盐分与土壤水分状况显著相关,线性拟合R2为0.769;(5)土壤含盐量与土地利用相关分析表明,人类活动干扰具有明显正向影响。     

A Voronoi-based spatial algebra for spatial relations

Spatial relation between spatial objects is a very important topic for spatial reasoning, query and analysis in geographical information systems (GIS). The most popular models in current use have fundamental deficiencies in theory. In this paper, a generic algebra for spatial relations is presented, in which (i) appropriate operators from set operators (i.e. union, intersection, difference, difference by, symmetric difference, etc.) are utilized to distinguish the spatial relations between neighboring spatial objects; (ii) three types of values are used for the computational results of set operations-content, dimension and number of connected components; and (iii) a spatial object is treated as a whole but the Voronoi region of an object is employed to enhance its interaction with its neighbours. This algebra overcomes the shortcomings of the existing models and it can effectively describe the relations of spatial objects.     

A spatial entropy reflecting distribution of spatial objects

Decision trees are mainly used to classify data and predict data classes. A spatial decision tree has been designed using Euclidean distance between objects for reflecting spatial data characteristic. Even though this method explains the distance of objects in spatial dimension, it fails to represent distributions of spatial data and their relationships. But distributions of spatial data and relationships with their neighborhoods are very important in real world. This paper proposes decision tree based on spatial entropy that represents distributions of spatial data with dispersion and dissimilarity. The rate of dispersion by dissimilarity presents how related distribution of spatial data and nonspatial attributes. The experiment evaluates the accuracy and building time of decision tree as compared to previous methods and it shows that the proposed method makes efficient and scalable classification for spatial decision support.     

A spatial entropy reflecting distribution of spatial objects

Decision trees are mainly used to classify data and predict data classes. A spatial decision tree has been designed using Euclidean distance between objects for reflecting spatial data characteristic. Even though this method explains the distance of objects in spatial dimension, it fails to represent distributions of spatial data and their relationships. But distributions of spatial data and relationships with their neighborhoods are very important in real world. This paper proposes decision tree based on spatial entropy that represents distributions of spatial data with dispersion and dissimilarity. The rate of dispersion by dissimilarity presents how related distribution of spatial data and non-spatial attributes. The experiment evaluates the accuracy and building time of decision tree as compared to previous methods and it shows that the proposed method makes efficient and scalable classification for spatial decision support.     

空间面板滞后模型的权重选取方式探究

通过最大化空间滞后项与残差的相关系数,提出了空间面板滞后模型中权重矩阵的选取方法,并与2SLSCWB方法进行了对比,模拟和实例表明该方法效果很好.     

基于GIS空间分析的北京城市空间发展

根据城市空间相互作用理论和城市综合引力模型 ,以北京市空间发展为例 ,探讨了应用地理信息系统 (GIS)研究城市空间发展的方法流程。在 CIS支持下 ,建立了空间数据库和属性数据库 ,并运用 GIS的多种空间分析功能 ,通过发展背景分析、发展轴线确定、引力参数定义、城市距离确定、质量数据处理等具体过程 ,综合计算了北京市的空间发展潜力。结果表明 ,北京市未来的空间发展首先应该面向廊坊市、天津市、黄骅市的东南方向发展     

Motion-induced spatial conflict

Borders defined by small changes in brightness (luminance contrast) or by differences in colour (chromatic contrast) appear to move more slowly than those defined by strong luminance contrast. As spatial coding is influenced by motion, if placed in close proximity, the different types of moving border might appear to drift apart. Using this configuration, we show here that observers instead report a clear illusory spatial jitter of the low-luminance-contrast boundary. This visible interaction between motion and spatial-position coding occurred at a characteristic rate (approximately 22.3 Hz), although the stimulus motion was continuous and invariant. The jitter rate did not vary with the speed of movement. The jitter was not due to small involuntary movements of the eyes, because it only occurred at a specific point within the stimulus, the low-luminance-contrast boundary. These findings show that the human visual system contains a neural mechanism that periodically resolves the spatial conflict created by adjacent moving borders that have the same physical but different perceptual speeds.     

Spatial aggregations for spatial based decision making in spatial data warehouses

For spatial based decision making such as choice of best place to construct a new department store, spatial data warehousing system is required more and more previous spatial data warehousing systems; however, provided decision making of non-spatial data on a map and so those cannot support enough spatial based decision making. The spatial aggregations are proposed for spatial based decision making in spatial data warehouses. The meaning of aggregation operators for applying spatial data was modified and new spatial aggregations were defined. These aggregations can support hierarchical concept of spatial measure. Using these aggregations, the spatial analysis classified by non-spatial data is provided. In case study, how to use these aggregations and how to support spatial based decision making are shown.     

有效支持空间OLAP的空间数据仓库模型研究

为了实现各类空间联机分析,提供更加全面灵活的空间决策支持,提出了一个空间数据立方体与空间索引结构相互协作的空间数据仓库模型,并同时构建了一个与之协作的空间索引结构aR^ B_tree．aR^ B_tree基于R^ _tree和B_tree实现,存储了空间维及时间维的聚集信息和层次关系,可以有效支持区域聚集查询．对各种空间联机分析操作进行了分类,分析表明该模型充分利用了传统数据仓库成熟的建模和联机分析技术,结合了空间索引结构在空间层次结构上的灵活特性,可以有效地支持各类空间联机分析．最后比较了aR^ B_tree与其他索引结构在区域聚集查询方面的性能．实验结果证明,在区域聚集查询中aR^ B_tree的节点访问次数和查询执行时间均小于现有索引结构．     

Version based spatial record management techniques for spatial database management system

The search operation of spatial data was a principal operation in existent spatial database management system, but the update operation of spatial data such as tracking are occurring frequently in the spatial database management system recently. So, necessity of concurrency improvement among transactions is increasing. In general database management system, many techniques have been studied to solve concurrency problem of transaction. Among them, multi-version algorithm does to minimize interference among transactions. However, to apply existent multi-version algorithm to improve concurrency of transaction to spatial database management system, the waste of storage happens because it must store entire version for spatial record even if only aspatial data of spatial record is changed. This paper has proposed the record management techniques to manage separating aspatial data version and spatial data version to decrease waste of storage for record version and improve concurrency among transactions.     

Spatial aggregations for spatial based decision 

For spatial based decision making such as choice of best place to construct a new department store, spatial data warehousing system is required more and more previous spatial data warehousing systems; however, provided decision making of nonspatial data on a map and so those cannot support enough spatial based decision making. The spatial aggregations are proposed for spatial based decision making in spatial data warehouses. The meaning of aggregation operators for applying spatial data was modified and new spatial aggregations were defined. These aggregations can support hierarchical concept of spatial measure. Using these aggregations, the spatial analysis classified by nonspatial data is provided. In case study, how to use these aggregations and how to support spatial based decision making are shown.