共查询到20条相似文献，搜索用时 31 毫秒
1.
Spatial Scale Problems and Geostatistical Solutions: A Review 总被引：1，自引：0，他引：1
The concept of spatial scale is fundamental to geography, as are the problems of integrating data obtained at different scales. The availability of GIS has provided an appropriate environment to re‐scale data prior to subsequent integration, but few tools with which to implement the re‐scaling. This sparsity of appropriate tools arises primarily because the nature of the spatial variation of interest is often poorly understood and, specifically, the patterns of spatial dependence and error are unknown. Spatial dependence can be represented and modelled using geostatistical approaches providing a basis for the subsequent re‐scaling of spatial data (e.g., via spatial interpolation). Geostatistical techniques can also be used to model the effects of re‐scaling data through the geostatistical operation of regularization. Regularization provides a means by which to re‐scale the statistics and functions that describe the data rather than the data themselves. These topics are reviewed in this paper and the importance of the spatial scale problems that remain is emphasized. 相似文献
2.
Modeling Short-Term Spatial and Temporal Variability of Groundwater Level Using Geostatistics and GIS 总被引：1，自引：0，他引：1
Deepesh Machiwal Amit Mishra Madan K. Jha Arun Sharma S. S. Sisodia 《Natural Resources Research》2012,21(1):117-136
Continuous depletion of groundwater levels from deliberate and uncontrolled exploitation of groundwater resources lead to
the severe problems in arid and semi-arid hard-rock regions of the world. Geostatistics and geographic information system
(GIS) have been proved as successful tools for efficient planning and management of the groundwater resources. The present
study demonstrated applicability of geostatistics and GIS to understand spatial and temporal behavior of groundwater levels
in a semi-arid hard-rock aquifer of Western India. Monthly groundwater levels of 50 sites in the study area for 36-month period
(May 2006 to June 2009; excluding 3 months) were analyzed to find spatial autocorrelation and variances in the groundwater
levels. Experimental variogram of the observed groundwater levels was computed at 750-m lag distance interval and the four
most-widely used geostatistical models were fitted to the experimental variogram. The best-fit geostatistical model was selected
by using two goodness-of-fit criteria, i.e., root mean square error (RMSE) and correlation coefficient (r). Then spatial maps of the groundwater levels were prepared through kriging technique by means of the best-fit geostatistical
model. Results of two spatial statistics (Geary’s C and Moran’s I) indicated a strong positive autocorrelation in the groundwater levels within 3-km lag distance. It is emphasized that the
spatial statistics are promising tools for geostatistical modeling, which help choose appropriate values of model parameters.
Nugget-sill ratio (<0.25) revealed that the groundwater levels have strong spatial dependence in the area. The statistical
indicators (RMSE and r) suggested that any of the three geostatistical models, i.e., spherical, circular, and exponential, can be selected as the
best-fit model for reliable and accurate spatial interpolation. However, exponential model is used as the best-fit model in
the present study. Selection of the exponential model as the best-fit was further supported by very high values of coefficient
of determination (r
2 ranging from 0.927 to 0.994). Spatial distribution maps of groundwater levels indicated that the groundwater levels are strongly
affected by surface topography and the presence of surface water bodies in the study area. Temporal pattern of the groundwater
levels is mainly controlled by the rainy-season recharge and amount of groundwater extraction. Furthermore, it was found that
the kriging technique is helpful in identifying critical locations over the study area where water saving and groundwater
augmentation techniques need to be implemented to protect depleting groundwater resources. 相似文献
3.
Geostatistics applies statistics to quantitatively describe geological sites and assess the uncertainty due to incomplete sampling. Strong assumptions are required regarding the location independence of statistical parameters to construct numerical models with geostatistical tools. Most geological data exhibit large-scale deterministic trends together with short-scale variations. Such location dependence violates the common geostatistical assumption of stationarity. The trend-like deterministic features should be modeled prior to conventional geostatistical prediction and accounted for in subsequent geostatistical calculations. The challenge of using a trend in geostatistical simulation algorithms for the continuous variable is the subject of this paper. A stepwise conditional transformation with a Gaussian mixture model is considered to provide a stable and artifact-free numerical model. The complex features of the regionalized variable in the presence of a trend are removed in the forward transformation and restored in the back transformation. The Gaussian mixture model provides a seamless bin-free approach to transformation. Data from a copper deposit were used as an example. These data show an apparent trend unsuitable for conventional geostatistical algorithms. The result shows that the proposed algorithm leads to improved geostatistical models. 相似文献
4.
There are 71 surface sediment samples collected from the eastern Beibu Gulf. The moment parameters (i.e. mean size, sorting and skewness) were obtained after applying grain size analysis. The geostatistical analysis was then applied to study the spatial autocorrelation for these parameters; while range, a parameter in the semivariogram that meters the scale of spatial autocorrelation, was estimated. The results indicated that the range for sorting coefficient was physically meaningful. The trend vectors calculated from grain size trend analysis model were consistent with the annual ocean circulation patterns and sediment transport rates according to previous studies. Therefore the range derived from the semivariogram of mean size can be used as the characteristic distance in the grain size trend analysis, which may remove the bias caused by the traditional way of basing on experiences or testing methods to get the characteristic distance. Hence the results from geostatistical analysis can also offer useful information for the determination of sediment sampling density in the future field work. 相似文献
5.
地统计法在粒径趋势分析中的应用:以北部湾东部为例(英文) 总被引：4，自引：1，他引：3
There are 71 surface sediment samples collected from the eastern Beibu Gulf. The moment parameters (i.e. mean size, sorting
and skewness) were obtained after applying grain size analysis. The geostatistical analysis was then applied to study the
spatial autocorrelation for these parameters; while range, a parameter in the semivariogram that meters the scale of spatial
autocorrelation, was estimated. The results indicated that the range for sorting coefficient was physically meaningful. The
trend vectors calculated from grain size trend analysis model were consistent with the annual ocean circulation patterns and
sediment transport rates according to previous studies. Therefore the range derived from the semivariogram of mean size can
be used as the characteristic distance in the grain size trend analysis, which may remove the bias caused by the traditional
way of basing on experiences or testing methods to get the characteristic distance. Hence the results from geostatistical
analysis can also offer useful information for the determination of sediment sampling density in the future field work. 相似文献
6.
土地利用/覆盖变化空间变异特征与遥感影像空间分辨率的选取——以北京地区为例 总被引：1，自引：0，他引：1
遥感数据为土地利用/覆盖变化提供了海量数据来源,如何选择合适空间分辨率的遥感影像进行特定地区的土地利用/覆盖变化研究,成为土地利用/覆盖变化研究的一个重要内容。地统计学方法已经广泛应用与遥感图像处理以及土地覆盖分类研究中,但应用于土地利用/覆盖变化的研究还比较缺乏。北京地区为研究区,运用遥感和地统计分析方法对该区土地利用/覆盖变化的空间结构的变异特征和合理的遥感影像数据源的选取问题做了初步探讨。研究表明地统计学方法能够揭示土地利用/覆盖变化的空间变异特征,有助于选择有效的遥感影像数据进行不同地区的土地利用/覆盖变化分析。 相似文献
7.
8.
黄河三角洲多尺度土壤盐分的空间分异 总被引：20，自引：5，他引：15
本文利用多尺度采样数据,探索了两个深度土壤盐分的空间分异,分析了不同尺度、深度土壤盐分的变异系数和空间相关性(结构方差与基台值之比)的变化,揭示了形成这种空间变异的地貌因素,最后利用普通克立格(ordinary kriging)方法对土壤盐分的分布进行了估测。分析发现,研究区土壤盐分的空间变异具有三个尺度。随着采样间隔的增加和区域的扩大,盐分分布空间相关性增强,且下层比上层具有更高的空间相关性。地貌因素(微地貌类型、坡度和高程)均具有较高的空间相关性,当与地貌因素关系密切时,该尺度及深度的土壤盐分空间相关性就大;反之,则小,这时可能主要受具有较小空间相关性其他因子的影响(如人为活动)。最后对合理的土壤采样提出了建议。 相似文献
9.
我国落叶松林生产力的空间变化特征 总被引：1，自引：1，他引：1
落叶松林在我国有着广泛的分布。其空间差异也比较大。采用地统计学方法对我国落叶松林生产力的空间特征进行了分析。结果表明。落叶松种群和群落地理替代作用明显；落叶松林生产力空间异质性较大。其变化与空间尺度密切相关；地理位置、海拔高度等是影响落叶松林生产力的重要因素；在天然落叶松林的管理和保护以及人工落叶松林的发展上应考虑其空间特征。 相似文献
10.
Fine scale disaster response and recovery data suitable for spatial analysis are still relatively rare. This is unfortunate as insight into spatial patterns of recovery could be invaluable in predicting the reestablishment of homes, streets and neighborhoods. The purpose of this paper is to show how fine scale geographic data can be collected in near real-time for the intermediate phase between response and recovery. These data will initially be used to assess the degree of damage (with relation to the Enhanced F scale) while also establishing a baseline for subsequent recovery monitoring. A spatial video system is used to collect data from the post-disaster landscape of Tuscaloosa which was hit by a large tornado in April 2011. This video, once processed, can be viewed within a Geographic Information System which combines street-level images with exact location. These data can be used to support ongoing recovery efforts, while also archiving a dataset suitable for the spatial analysis of the changing post-disaster landscape. 相似文献
11.
Drumond David Alvarenga Rolo Roberto Mentzingen Costa João Felipe Coimbra Leite 《Natural Resources Research》2019,28(1):145-152
Experimental variograms are crucial for most geostatistical studies. In kriging, for example, the variography has a direct influence on the interpolation weights. Despite the great importance of variogram estimators in predicting geostatistical features, they are commonly influenced by outliers in the dataset. The effect of some randomly spatially distributed outliers can mask the pattern of the experimental variogram and produce a destructuration effect, implying that the true data spatial continuity cannot be reproduced. In this paper, an algorithm to detect and remove the effect of outliers in experimental variograms using the Mahalanobis distance is proposed. An example of the algorithm’s application is presented, showing that the developed technique is able to satisfactorily detect and remove outliers from a variogram.
相似文献12.
13.
Elizabeth A. Wentz Donna J. Peuquet Sharolyn Anderson 《International journal of geographical information science》2013,27(9):1309-1325
The availability of spatial data on an unprecedented scale as well as advancements in analytical and visualization techniques gives researchers the opportunity to study complex problems over large urban and regional areas. Nevertheless, few individual data sets exist that provide both the requisite spatial and/or temporal observational frequency to truly facilitate detailed investigations. Some data are collected frequently over time but only at a few geographic locations (e.g., weather stations). Similarly, other data are collected with a high level of spatial resolution but not at regular or frequent time intervals (e.g., satellite data). The purpose of this article is to present an interpolation approach that leverages the relative temporal richness of one data set with the relative spatial richness of another to fill in the gaps. Because different interpolation techniques are more appropriate than others for specific types of data, we propose a space–time interpolation approach whereby two interpolation methods – one for the temporal and one for the spatial dimension – are used in tandem to increase the accuracy results. We call our ensemble approach the space–time interpolation environment (STIE). The primary steps within this environment include a spatial interpolation processor, a temporal interpolation processor, and a calibration processor, which enforces phenomenon-related behavioral constraints. The specific interpolation techniques used within the STIE can be chosen on the basis of suitability for the data and application at hand. In this article, we first describe STIE conceptually including the data input requirements, output structure, details of the primary steps, and the mechanism for coordinating the data within those steps. We then describe a case study focusing on urban land cover in Phoenix, Arizona, using our working implementation. Our empirical results show that our approach increased the accuracy for estimating urban land cover better than a single interpolation technique. 相似文献
14.
北京市健身俱乐部多尺度空间格局 总被引：1，自引：0，他引：1
城市健身俱乐部是现代城市游憩空间中的新事物。利用点格局识别和探索性数据分析方法,借助GIS和地统计分析等软件,分析北京市健身俱乐部空间格局特征。最邻近距离系数和样方分析表明,健身俱乐部在全局尺度上存在明显空间聚集,但在行政分区和交通线路分割的单元中,则表现出聚集、随机和离散分布的不同空间格局。1~5km共5个尺度格网单元统计分析进一步验证了健身俱乐部空间格局具有显著尺度效应。样本密度、最邻近距离系数、Moran’s I系数分析发现,样本密度和最邻近距离系数均呈现明显的空间分异和空间自相关,其中2km、3km尺度反映的微观形态特征最为显著。证明全局尺度并非分析健身俱乐部空间格局的唯一和最好尺度,部分微观单元上空间格局将更明显,格局特征也可能会与全局尺度相反。因此多类型、多尺度统计单元能够更全面地反映点要素分布的规律。多尺度空间格局研究,为准确描述城市游憩空间中的点要素空间格局特征,提供了新的研究思路和具体实证。 相似文献
15.
《Urban geography》2013,34(7):635-647
Traditional measures of segregation, such as the index of dissimilarity, have been criticized as aspatial in nature. Spatial measures of segregation have been proposed, but they are difficult to use. Based on the idea that segregation implies a spatial separation of ethnic groups, the degree of spatial correlation among groups can reflect the level of segregation. This paper suggests that several geostatistical measures, especially the standard deviational ellipse, are effective tools for capturing the spatial characteristics of a population group. By comparing the ellipses of different groups, measures of segregation can be derived. The paper demonstrates this approach to measuring segregation by way of both a simulation and a case study. [Key words: spatial segregation, geostatistics, deviational ellipses, spatial correspondence/correlation.] 相似文献
16.
空间自相关的可塑性面积单元问题效应 总被引：13，自引：3，他引：10
可塑性面积单元问题(modifiable areal unit problem,MAUP) 效应是对空间数据分析结果产生不确定性影响的主要原因之一,在空间自相关分析中也不例外.本文分别利用网格模拟数据和中国人均GDP实例数据为数据源,以全局Moran's I 系数来探究空间自相关统计中的MAUP效应,分析结果表明,变量的空间自相关程度依赖于空间的粒度大小与单元的划分方法,但空间单元的变化与自相关性并不存在某种函数关系.因此,在进行空间自相关研究时必须选择合适的地理单元的粒度大小和分区.最后本文给出一种基于地统计内插方法来降低MAUP对空间自相关分析影响. 相似文献
17.
A pedogeochemical exploratory survey of gold deposits was carried out in the region of São Sepé (southernmost Brazil). The region comprises a predominantly metamorphosed belt of volcanoclastics, sediments, serpentinites, basalts, gabbros, chert, tuffs, and banded iron formation of the Proterozoic age. The anomalies were identified first by stream sediment heavy mineral survey at the regional scale of exploration. Once spatial continuity was modeled, ordinary block kriging was performed to generate geochemical maps. Indicator block kriging also was used as an alternative in analyzing and interpreting geochemical data. A novel approach is proposed, which combines both ordinary and indicator kriging for delineating geochemical anomalies. Probability maps proved to be appropriate for selecting new sites for further exploration. Gold anomalies in soils trending NE were well defined by geostatistical analysis and subsequently confirmed by drilling. 相似文献
18.
人穷还是地穷?空间贫困陷阱的地统计学检验 总被引：1，自引：1，他引：0
引入地统计学的变异函数和交叉相关图方法,以甘肃省六盘山片区为案例区,通过分析村级贫困的空间格局及其与地理因子关系随空间尺度的变化,提供空间贫困陷阱检验关于尺度的深入理解。结果表明：地统计学方法兼具有效性和可靠性,可以反映地理因素—贫困状况关系随时空的变化,对“人地关系”视角下反贫困理论与实践研究具有积极意义。案例区空间贫困陷阱问题突出,村级贫困在一定空间范围内具有自相关性,空间总变异中自相关部分远高于随机性部分,这与不同尺度上地形、气候、区位等结构性因素的影响和控制有关,总体上到县城距离、海拔和总人口3个因子的影响范围和强度较大。 相似文献
19.
Talebi Hassan Mueller Ute Tolosana-Delgado Raimon Grunsky Eric C. McKinley Jennifer M. Caritat Patrice de 《Natural Resources Research》2019,28(3):869-891
Prediction of true classes of surficial and deep earth materials using multivariate spatial data is a common challenge for geoscience modelers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained, and features are ranked based on their contribution to the predictive model. To generate potential and uncertainty maps, compositional data are simulated at unsampled locations via a chain of transformations (isometric log-ratio transformation followed by the flow anamorphosis) and geostatistical simulation. The simulated results are subsequently back-transformed to the original compositional space. The trained predictive model is used to estimate the probability of classes for simulated compositions. The proposed approach is illustrated through two case studies. In the first case study, the major crustal blocks of the Australian continent are predicted from the surface regolith geochemistry of the National Geochemical Survey of Australia project. The aim of the second case study is to discover the superficial deposits (peat) from the regional-scale soil geochemical data of the Tellus Project. The accuracy of the results in these two case studies confirms the usefulness of the proposed method for geological class prediction and geological process discovery.
相似文献20.
基于西双版纳18个样本村、612个农户、跨时6年的面板数据,利用Pearson相关系数测度了2012—2014和2014—2018年2个阶段“投入—产出”结构之间的拟合程度,分析了橡胶主产区农户橡胶依赖的变化情况;并通过构建Tobit模型分析了橡胶路径依赖的核心影响因素。结果表明：1）橡胶主产区的橡胶依赖呈下降趋势。2）橡胶依赖符合自增强机制假设。橡胶产出依赖程度具有传递效应,表明橡胶依赖可以逆势调控,可以利用政策补贴等手段增强投入激励,提高本期橡胶依赖,进而提高下一期橡胶依赖。3）收益递增并不必然导致橡胶依赖。农户更加看重未来的期望收益,提升期望收益才能保障农户种植橡胶的积极性。4）橡胶依赖表现为土地依赖。土地种植橡胶后具备了“专用资产属性”,转变种植结构需要付出较大的时间和资金成本。5）橡胶依赖还表现为海拔依赖。主要供给橡胶的低海拔地区,农户调整橡胶种植规模的时间成本较高海拔地区小,更容易毁弃胶林,应将低海拔地区做为橡胶产业政策的关注点。 相似文献