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1.
As an important GIS function, spatial interpolation is one of the most often used geographic techniques for spatial query, spatial data visualization, and spatial decision-making processes in GIS and environmental science. However, less attention has been paid on the comparisons of available spatial interpolation methods, although a number of GIS models including inverse distance weighting, spline, radial basis functions, and the typical geostatistical models (i.e. ordinary kriging, universal kriging, and cokriging) are already incorporated in GIS software packages. In this research, the conceptual and methodological aspects of regression kriging and GIS built-in interpolation models and their interpolation performance are compared and evaluated. Regression kriging is the combination of multivariate regression and kriging. It takes into consideration the spatial autocorrelation of the variable of interest, the correlation between the variable of interest and auxiliary variables (e.g., remotely sensed images are often relatively easy to obtain as auxiliary variables), and the unbiased spatial estimation with minimized variance. To assess the efficiency of regression kriging and the difference between stochastic and deterministic interpolation methods, three case studies with strong, medium, and weak correlation between the response and auxiliary variables are compared to assess interpolation performances. Results indicate that regression kriging has the potential to significantly improve spatial prediction accuracy even when using a weakly correlated auxiliary variable. 相似文献
2.
Fires threaten human lives, property and natural resources in Southern African savannas. Due to warming climate, fire occurrence may increase and fires become more intense. It is crucial, therefore, to understand the complexity of spatiotemporal and probabilistic characteristics of fires. This study scrutinizes spatiotemporal characteristics of fires and the role played by abiotic, biotic and anthropogenic factors for fire probability modelling in a semiarid Southern African savanna environment. The MODIS fire products: fire hot spots (MOD14A2 and MYD14A2) and burned area product MODIS (MCD45A1), and GIS derived data were used in analysis. Fire hot spots occurrence was first analysed, and spatial autocorrelation for fires investigated, using Moran's I correlograms. Fire probability models were created using generalized linear models (GLMs). Separate models were produced for abiotic, biotic, anthropogenic and combined factors and an autocovariate variable was tested for model improvement. The hierarchical partitioning method was used to determine independent effects of explanatory variables. The discriminating ability of models was evaluated using area under the curve (AUC) from the receiver operating characteristic (ROC) plot. The results showed that 19.2–24.4% of East Caprivi burned when detected using MODIS hot spots fire data and these fires were strongly spatially autocorrelated. Therefore, the autocovariate variable significantly improved fire probability models when added to them. For autologistic models, i.e. models accounting for spatial autocorrelation, discrimination was good to excellent (AUC 0.858–0.942). For models not counting spatial autocorrelation, prediction success was poor to moderate (AUC 0.542–0.745). The results of this study clearly showed that spatial autocorrelation has to be taken in to account in the fire probability model building process when using remotely sensed and GIS derived data. This study also showed that fire probability models accounting for spatial autocorrelation proved to be superior in regional scale burned area estimation when compared with MODIS burned area product (MCD45A1). 相似文献
3.
地理空间数据模型及其对地理对象-空间关系的描述是地理信息科学（GIS）理论与应用研究的核心内容。介绍和讨论了一种新的GIS空间数据模型—空间染色模型（spatial chromatic model,SCM）的结构、操作、应用及其在地理学上的含义。SCM的特点是其每个基础数据单元（细胞）具有一个唯一的编码,这些编码提供了面向各种GIS空间分析和运算的丰富信息。SCM的本质是一种基于实体第一性的相对性地理空间,它有助于从一个新的视角更加深入地理解地理实体-空间关系。 相似文献
4.
《International Journal of Digital Earth》2013,6(10):825-839
With the increased use of locational information, spatial location referencing and coding methods have become much more important to the mining of both geographical and nongeographical data in digital earth system. Unfortunately, current methods of geocoding, based on reverse lookup of coordinates for a given address, have proven too lossy with respect to administrative and socioeconomic data. This paper proposes a spatial subdivision and geocoding model based on spatial address regional tessellation (SART). Given a hierarchical address object definition, and based on the ‘region of influence’ characteristics of an address, SART creates multiresolution spatial subdivisions by irregular and continuous address regions. This model reflects most of the geographical features and many of the social and economic implications for a given address. It also better reflects the way people understand addresses and spatial locations. We also propose an appropriate method of geocoding for standard addresses (SART-GC). The codes generated by this method can record address footprints, hierarchical relationships, and spatial scales in a single data structure. Finally, by applying our methods to the Shibei District of Qingdao, we demonstrate the suitability of SART-GC for multi-scale spatial information representation in digital earth systems. 相似文献
5.
Comparison of interpolation models for estimating heavy metals in soils under various spatial characteristics and sampling methods 总被引：2，自引：0，他引：2 
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下载免费PDF全文 Heavy metal pollution in soils has become increasingly challenging, especially in developing countries. Estimating the spatial distribution of heavy metals in soils is essential to preventing their build‐up. This article aims to identify the effects of spatial scales, spatial autocorrelation, sampling methods, and proportion on interpolation models in estimating the distribution of heavy metals in soils. Six interpolation models (area‐and‐point kriging, AAPK; inverse distance weighting, IDW; local polynomial interpolation, LP; ordinary kriging, OK; simple kriging, SK; and thin plate spline, TPS), three sampling methods (random, stratified, and systematic sampling), and five sampling proportions (1, 5, 10, 15, and 20%) are considered in this study using sets of simulated data, and the real situation was tested for verification. The results show that, in general, with the increase of spatial autocorrelation or the sampling percentage, the accuracy and stability of different interpolation models gradually increase; however, the various interpolation models have their own specific characteristics and application conditions. The best application conditions of the interpolation models compared with other models under the same situation are summarized and explained in theory. These conclusions have implications for future work. 相似文献
6.
Dynamic spatial panels: models, methods, and inferences 总被引：7，自引：1，他引：6
J. Paul Elhorst 《Journal of Geographical Systems》2012,14(1):5-28
This paper provides a survey of the existing literature on the specification and estimation of dynamic spatial panel data
models, a collection of models for spatial panels extended to include one or more of the following variables and/or error
terms: a dependent variable lagged in time, a dependent variable lagged in space, a dependent variable lagged in both space
and time, independent variables lagged in time, independent variables lagged in space, serial error autocorrelation, spatial
error autocorrelation, spatial-specific and time-period-specific effects. The survey also examines the reasoning behind different
model specifications and the purposes for which they can be used, which should be useful for practitioners. 相似文献
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Yukio Sadahiro 《Journal of Geographical Systems》2011,13(4):373-391
A spatial tessellation is a set of regions that are collectively exhaustive and mutually exclusive except for the boundaries.
In geographical analysis, it may represent such administrative units as census tracts, postal zones, and electoral or school
districts. Spatial tessellations of a certain area are often closely related to each other. Areas of local communities are
related to school districts, market areas of retail stores, and administrative units. Postal zones and census tracts are determined
by collecting or dividing administrative units. Analysis of such relations among tessellations often reveals their underlying
spatial phenomena. To this end, this paper proposes a new exploratory method for analyzing the relations among spatial tessellations.
It aims to detect spatial patterns, especially those with a hierarchical structure, and to provide a tessellation classification
scheme. Topological relations and similarity measures are introduced to evaluate the relations between tessellation pair.
For more tessellations, tree representations are proposed. These not only visualize relations, but also provide a means of
classifying tessellations. The method is applied to the analysis of two sets of spatial tessellations: one with five hypothetical
tessellations, and another with 34 candidate plans for the new Doshusei administrative system in Japan. The application reveals
the properties of the method and quantitative measures used in analysis. 相似文献
10.
面向流媒体传输的空间数据变化累积模型 总被引：1，自引：1，他引：0
将流媒体技术引入到以矢量数据为主的网络GIS传输中,其关键是建立逐级精细化的多层次表达模型,并在服务器端数据预组织上建立线性索引结构.本研究针对该目标建立一种矢量数据多重表达的新模型,即变化累积模型,认为尺度空间中从粗到细的数据表达过程是一些"变化"的累积,"变化"表现为两个连续尺度表达间的差别,通过"变化"元素的逐渐增加或减少来实现渐进传输.基于该模型对面状数据渐进式传输进行研究,将多边形分级剖分为一系列凸壳或外接矩形的基础上建立变化累积模型,通过剖分元素的组合完成渐进式流媒体传输.Abstract： The streaming transfer method which makes data immediately available application during the progress of transfer becomes a welcome transfer way over Web. The progressive transmission of map data provides the users witha self-adaptive strategy to access remote data. It not only speeds up the Web transfer but also offers an efficient navigation guide in information acquisition. The key technology in this transmission is the efficient multiple representation of spatial data and pre-organization on server site. This paper aims at vector data offering a new multiple representation model called changes accumulation model, which considers the spatial representation from one scale to another scale as an accumulation of set of changes. The difference between two consecutive representations is recorded in a linear index and through gradually addition or subtraction realizes the progressive transmission. As an example, the progressive transmission of area feature based on changes accumulation model is investigated in the paper. The model is built by hierarchical decomposition of polygon into series of convex hulls or bounding rectangles and through component of the decomposed elements to accomplish the progressive transmission. 相似文献
11.
Qingmin Meng Chris Cieszewski Marguerite Madden 《ISPRS Journal of Photogrammetry and Remote Sensing》2009,64(1):27-36
Large area forest inventory is important for understanding and managing forest resources and ecosystems. Remote sensing, the Global Positioning System (GPS), and geographic information systems (GIS) provide new opportunities for forest inventory. This paper develops a new systematic geostatistical approach for predicting forest parameters, using integrated Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, GPS, and GIS. Forest parameters, such as basal area, height, health conditions, biomass, or carbon, can be incorporated as a response variable, and the geostatistical approach can be used to predict parameter values for uninventoried points. Using basal area as the response and Landsat ETM+ images of pine stands in Georgia as auxiliary data, this approach includes univariate kriging (ordinary kriging and universal kriging) and multivariable kriging (co-kriging and regression kriging). The combination of bands 4, 3, and 2, as well as the combination of bands 5, 4, and 3, normalized difference vegetation index (NDVI), and principal components (PCs) were used in this study with co-kriging and regression kriging. Validation based on 200 randomly sampling points withheld field inventory was computed to evaluate the kriging performance and demonstrated that band combination 543 performed better than band combination 432, NDVI, and PCs. Regression kriging resulted in the smallest errors and the highest R-squared indicating the best geostatistical method for spatial predictions of pine basal area. 相似文献
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Andrea Vaona 《Journal of Geographical Systems》2010,12(1):89-103
Ramsey’s regression specification error test (RESET) is thought to be robust to spatial correlation. Building on the literature
on spurious spatial regression, we show that this is not so in presence of spatial correlation in both the error and the independent
variable of an econometric model. Correcting the test for spatial correlation improves its performance, though in large samples
this strategy is not completely successful. Once assuming that spatial autocorrelation in both the independent variable and
in the error is produced by a spatial moving average model instead of a spatial autoregressive one, RESET displays more robustness. 相似文献
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《制图学和地理信息科学》2013,40(1):25-35
Previously, we developed an integrated software package called ICAMS (Image Characterization and Modeling System) to provide specialized spatial analytical functions for interpreting remote sensing data. This paper evaluates three fractal dimension measurement methods that have been implemented in ICAMS: isarithm, variogram, and a modified version of triangular prism. To provide insights into how the fractal methods compare with conventional spatial techniques in measuring landscape complexity, the performance of two spatial autocorrelation methods, Moran's I and Geary's C, is also evaluated. Results from analyzing 25 simulated surfaces having known fractal dimensions show that both the isarithm and triangular prism methods can accurately measure a range of fractal surfaces. The triangular prism method is most accurate at estimating the fractal dimension of surfaces having higher spatial complexity, but it is sensitive to contrast stretching. The variogram method is a comparatively poor estimator for all surfaces, particularly those with high fractal dimensions. As with the fractal techniques, spatial autocorrelation techniques have been found to be useful for measuring complex images, but not images with low dimensionality. Fractal measurement methods, as well as spatial autocorrelation techniques, can be applied directly to unclassified images and could serve as a tool for change detection and data mining. 相似文献
15.
Environmental modelling usually requires spatially distributed inputs for model operation. We propose that such inputs are best obtained from field measured data. Geographic information systems (GIS) provide a logical framework to distribute measured inputs spatially, to manipulate ensuing data fields during analysis, and to display the results. This paper describes a study conducted on a 123 km2 catchment in Pennsylvania. The purpose was to evaluate how spatial variability of macroporosity affects distribution of other infiltration-related parameters. We measured sorptivity, conductivity and macroporosity at specific points within a catchment, and interpolated their spatial distributions by kriging. The measurements were made with ring and disk infiltrometers, sampling locations were geo-referenced with a global positioning system (GPS), and data were analysed using geostatistical techniques in a GIS context. Field values ( hard data ) were supplemented by soft data derived from cumulative distribution functions (cdfs) and available soil maps. Results showed that, when spatial variability associated with macroporosity was removed, infiltration parameters became less variable. Observed correlation among measured parameters suggested a form of potential transfer functions. We conclude that infiltration can be modelled at either the farm or catchment scale if macroporosity and spatial variability of infiltration parameters are adequately defined, and we suggest approaches which can be used in a GIS context to attain that goal. 相似文献
16.
《ISPRS Journal of Photogrammetry and Remote Sensing》2000,55(3):139-149
When we map an area or create a digital database for it, the first task is often to partition the space into smaller units. There are traditionally two methods of partitioning: vector and raster. A vector partition delineates the boundary of features by polylines while a raster partition subdivides the space into a regular matrix of square or rectangular pixels. These two are complementary methods of subdividing the space either by features or by unconstrained space cells. In the third dimension, they are extended to polyhedra and voxels, respectively. We will argue in this paper that the terms “vector” and “raster” cannot describe all cases of tessellation. With advances in data modelling, variations of the two traditional methods have been developed, such as the representation of a feature by pixels and not by polylines. At present, there is a lack of systematic terminology to describe the various methods of tessellation. In this paper, we will propose a taxonomy for three-dimensional space tessellation. Its essential feature is to distinguish between abstract concepts of tessellation and their encoding methods. We recognise that tessellation of geographic space is carried out in different stages with increasingly precise mathematical meaning. This provides us with an insight into the process of spatial tessellation and a model to systematically describe the various structures. These concepts could form a basis for spatial data models. 相似文献
17.
空间数据是空间分析的基础,随着海量空间数据的增大,其之间关系的日益复杂,空间数据的组织就显得愈发重要。基于超图的数据结构以超图和集合理论为基础,可以表达空间数据之间的层次关系和横向关系,使GIS空间数据结构在空间分析中的完备性和适应性问题得到解决。本论文在对超图的层次数据结构和非层次结构进行详细研究的基础上,初步探讨利用面向对象的关系数据库系统实现该模型,并以海洋数据为例,利用HBDS组织海洋原始数据、等深线数据、特征线数据、地貌数据等。论文的最后对HBDS在GIS中的应用做简单举例,并期将HBDS和OLAP相结合的方法进行数据挖掘。 相似文献
18.
Benjamin G Jacob Daniel A Griffith James T Gunter Ephantus J Muturi Erick X Caamano Josephat I Shililu John I Githure James L Regens Robert J Novak 《Transactions in GIS》2008,12(4):515-539
This research accounts for spatial autocorrelation by including latent map pattern components as predictor variables in a malaria mosquito aquatic habitat model specification. The data used to derive the model was from a digitized grid-based algorithm, generated in an ArcInfo database, using QuickBird visible and near-infrared (NIR) data. The Feature Extraction (FX) Module in ENVI 4.4® was used to categorize individual pixels of field sampled aquatic habitats into separate spectral classes, convert remotely sensed raster layers to vector coverages, and classify output layers to vector format as ESRI shapefiles. These data were used to construct a geographic weights matrix for evaluation of field and remote sampled covariates of Anopheles arabiensis aquatic habitats, a major vector of malaria in East Africa. The principal finding is that synthetic map pattern variables, which are eigenvectors computed for a geographic weights matrix, furnish an alternative way of capturing spatial dependency effects in the mean response term of a regression model. The spatial autocorrelation components suggest the presence of roughly 11 to 28% redundant information in the aquatic habitat larval count samples. The presence of redundant information in the models suggest that the sampling configuration of the An. arabiensis aquatic habitats, in the study sites, may cause field and remote observations of aquatic habitats to be dependent, rather than independent, moving data analysis away from the classical statistical independence model. A Poisson regression model, with a non-constant, gamma-distributed mean, can decompose field and remote sampled An. arabiensis data into positive and negative spatial autocorrelation eigenvectors, which can assess the precision of a malaria mosquito aquatic habitat map and the significance of all factors associated with larval abundance and distribution in a riceland agroecosystem. 相似文献
19.
The effects of spatial autoregressive dependencies on inference in ordinary least squares: a geometric approach 总被引：1，自引：1，他引：0
There is a common belief that the presence of residual spatial autocorrelation in ordinary least squares (OLS) regression
leads to inflated significance levels in beta coefficients and, in particular, inflated levels relative to the more efficient
spatial error model (SEM). However, our simulations show that this is not always the case. Hence, the purpose of this paper
is to examine this question from a geometric viewpoint. The key idea is to characterize the OLS test statistic in terms of
angle cosines and examine the geometric implications of this characterization. Our first result is to show that if the explanatory
variables in the regression exhibit no spatial autocorrelation, then the distribution of test statistics for individual beta
coefficients in OLS is independent of any spatial autocorrelation in the error term. Hence, inferences about betas exhibit all the optimality properties of
the classic uncorrelated error case. However, a second more important series of results show that if spatial autocorrelation
is present in both the dependent and explanatory variables, then the conventional wisdom is correct. In particular, even when
an explanatory variable is statistically independent of the dependent variable, such joint spatial dependencies tend to produce
“spurious correlation” that results in over-rejection of the null hypothesis. The underlying geometric nature of this problem
is clarified by illustrative examples. The paper concludes with a brief discussion of some possible remedies for this problem. 相似文献
20.
This article studies Bayesian hierarchical spatial modelling that monitors the changes of residual spatial pattern (structure) of the outcome variable for exploring unknown risk factors in small‐area analysis. Spatially structured random effects (SRE) and unstructured random effects (URE) terms added to the conventional logistic regression model take into account overdispersion and residual spatial structure, which if unaccounted for could cause incorrect identification of risk factors. Mapping and/or calculating the ratio of random effects that are spatially‐structured monitor the extent of residual spatial structure. The monitoring provides insights into identification of unknown covariates that have similar spatial structures to those of SRE. Adding such covariates to the model has the potential to diminish the residual spatial structure, until possibly all or most of the spatial structure can be explained. Risk factors identified are the added covariates that have statistically significant regression coefficients. We apply the methods to the analysis of domestic burglaries in Cambridgeshire, England. Small‐area analysis of crime where data often display apparent spatial structure would particularly benefit from the methodologies. We discuss the methodologies, their relevancy in our analysis of domestic burglaries, their limitations, and possible paths for future research. 相似文献