On the use of dimensioned measures of error to evaluate the performance of spatial interpolators

Spatial cross‐validation and average‐error statistics are examined with respect to their abilities to evaluate alternate spatial interpolation methods. A simple cross‐validation methodology is described, and the relative abilities of three, dimensioned error statistics—the root‐mean‐square error (RMSE), the mean absolute error (MAE), and the mean bias error (MBE)—to describe average interpolator performance are examined. To illustrate our points, climatologically averaged weather‐station temperatures were obtained from the Global Historical Climatology Network (GHCN), Version 2, and then alternately interpolated spatially (gridded) using two spatial‐interpolation procedures. Substantial differences in the performance of our two spatial interpolators are evident in maps of the cross‐validation error fields, in the average‐error statistics, as well as in estimated land‐surface‐average air temperatures that differ by more than 2°C. The RMSE and its square, the mean‐square error (MSE), are of particular interest, because they are the most widely reported average‐error measures, and they tend to be misleading. It (RMSE) is an inappropriate measure of average error because it is a function of three characteristics of a set of errors, rather than of one (the average error). Our findings indicate that MAE and MBE are natural measures of average error and that (unlike RMSE) they are unambiguous.     

Testing the effects of positional uncertainty on spatial decision‐making

There is now increasing agreement that the uncertainty associated with spatial information should be represented to users in a manner that is comprehensive and unambiguous. To assist with this task, researchers have developed a variety of methods to portray spatial uncertainty. While there has been some testing of the effectiveness of these displays, the possible effects of such representations on decision‐making have not yet been thoroughly investigated. Indeed, studies from the psychological literature indicate that people do not always make the same decisions when presented with the same information, and they can also be sensitive to the effects of presentation, task, and context. This paper examines how the use of four different methods to represent positional uncertainty can affect spatial decision‐making. The authors found that extremely significant differences in participants' responses were exhibited, depending on the manner in which positional uncertainty was displayed, although little difference was observed in the ability of the participants to comprehend the four display methods. In addition, strong preferences were recorded for certain representations over others.     

A spatial approach to forest‐management optimization: linking GIS and multiple objective genetic algorithms

Forest‐management decision‐support systems are largely monolithic structures. Spatial details are left out during the optimization process and are elaborated during the operational planning. This might produce misleading results and plans that are impossible to implement. In this paper, a forest‐management spatial decision‐support systems is presented, in which spatial formulation needed for wildlife models is included during the optimization process. To this end, a multiple‐objective genetic algorithm is combined with a geographical information system. An online spatial evaluation of the objective functions is made possible. This is illustrated by a pilot study in Kirkhill forest, Aberdeen.     

A comparison of geometric approaches to assessing spatial similarity for GIR

This research compares the geographic information retrieval (GIR) performance of a set of logistic regression models with those of five non‐probabilistic methods that compute a spatial similarity score for a query–document pair. All methods are applied to a test collection of queries and documents indexed spatially by two convex conservative geometric approximations: the minimum bounding box (MBB) and the convex hull. In the comparison, the tested logistic regression models outperform, in terms of standard information retrieval recall and precision measures, all of the non‐probabilistic methods. The retrieval performance achieved by the logistic regression models on MBB approximations is similar to that achieved by the use of the non‐probabilistic methods on convex hulls. Although these results are valid only for the test collection used in this study, they suggest that a logistic regression approach to GIR provides an alternative to the use of higher‐quality geometric representations that are more difficult to obtain, implement, and process. Additionally, this research demonstrates the ability of a probabilistic approach to effectively incorporate information about geographic context in the spatial ranking process.     

GIS‐based multicriteria spatial modeling generic framework

Multicriteria analysis is a set of mathematical tools and methods allowing the comparison of different alternatives according to many criteria, often conflicting, to guide the decision maker towards a judicious choice. Multicriteria methods are used in spatial context to evaluate and compare spatial decision alternatives, often modeled through constraint‐based suitability analysis and represented by point, line, and polygon features or their combination, and evaluated on several space‐related criteria, to select a restricted subset for implementation. Outranking methods, a family of multicriteria methods, may be useful in spatial decision problems, especially when ordinal evaluation criteria are implied. However, it is recognized that these methods, except those devoted to multicriteria classification problems, are subject to computational limitations with respect to the number of alternatives. This paper proposes a framework to facilitate the incorporation and use of outranking methods in geographical information systems (GIS). The framework is composed of two phases. The first phase allows producing a planar subdivision of the study area obtained by combining a set of criteria maps; each represents a particular vision of the decision problem. The result is a set of non‐overlapping spatial units. The second phase allows constructing decision alternatives by combining the spatial units. Point, line and polygon feature‐based decision alternatives are then constructed as an individual, a grouping of linearly adjacent or a grouping of contiguous spatial units. This permits us to reduce considerably the number of alternatives, enabling the use of outranking methods. The framework is illustrated through the development of a prototype and through a step‐by‐step application to a corridor identification problem. This paper includes also a discussion of some conceptual and technical issues related to the framework.     

Exploring spatial data uncertainties in land‐use change scenarios

This paper evaluates errors and uncertainties in representing landscapes that arise from different data rasterization methods, spatial resolutions, and downscaled land‐use change (LUC) scenarios. A vector LU dataset for Luxembourg (minimum mapping unit: 0.15 ha; year 2000) was used as the baseline reference map. This map was rasterized at three spatial resolutions using three cell class assignment methods. The landscape composition and configuration of these maps were compared. Four alternative scenarios of future LUC were also generated for the three resolutions using existing LUC scenarios and a statistical downscaling method creating 37 maps of LUC for the year 2050. These maps were compared in terms of composition and spatial configuration using simple metrics of landscape fragmentation and an analysis of variance (ANOVA). Differences in landscape composition and configuration between the three cell class assignment methods and the three spatial resolutions were found to be at least as large as the differences between the LUC scenarios. This occurred in spite of the large LUC projected by the scenarios. This demonstrates the importance of the rasterization method and the level of aggregation as a contribution to uncertainty when developing future LUC scenarios and in analysing landscape structure in ecological studies.     

A multicriteria spatial decision support system for solving emergency service station location problems

Earthquakes occurring in urban areas constitute an important concern for emergency management and rescue services. Emergency service location problems may be formulated in discrete space or by restricting the potential location(s) to a specified finite set of points in continuous space. We propose a Multicriteria Spatial Decision Support System to identify shelters and emergency service locations in urban evacuation planning. The proposed system has emerged as an integration of the geographical information systems (GIS) and the multicriteria Decision-Making method of Preference Ranking Organization Method for Enrichment Evaluation IV (PROMETHEE IV). This system incorporates multiple and often conflicting criteria and decision-makers’ preferences into a spatial decision model. We consider three standard structural attributes (i.e., durability density, population density, and oldness density) in the form of spatial maps to determine the zones most vulnerable to an earthquake. The information on these spatial maps is then entered into the ArcGIS software to define the relevant scores for each point with regards to the aforementioned attributes. These scores will be used to compute the preference functions in PROMETHEE IV, whose net flow outranking for each alternative will be inputted in ArcGIS to determine the zones that are most vulnerable to an earthquake. The final scores obtained are integrated into a mathematical programming model designed to find the most suitable locations for the construction of emergency service stations. We demonstrate the applicability of the proposed method and the efficacy of the procedures and algorithms in an earthquake emergency service station planning case study in the city of Tehran.     

地震、测井和地质数据融合与实现

厚覆盖区地下地质模型所依靠的数据主要是地震和测井资料,但地震数据反映范围广而分辨率低,测井数据则分辨率高而反映范围小,如何有效地将两者组合利用,克服各自不足是论证的重点,说明了用数据树的形式组织所有井与地震地质等数据,并能同时生成和编辑多口井的合成地震记录,来确定不同井的深时关系,为把不同域的地震数据和测井数据融合显示建立基础,提供了实时修改子波的频率和相位来做合成记录的功能,也有反射系数与子波的分布褶积等最新的方法,以慢镜头的形式说明合成记录的形成过程。这些功能的应用,给用户确定正确的地下速度场提供了先进的手段,为地质模型建立提供了先进的方法。     

基性岩脉与金成矿的关系

基性岩脉是大陆伸展背景下,主要来自陆下软流圈或地幔岩石圈的岩浆侵入岩体.大量研究成果表明基性岩脉发育与金矿脉在空间展布、构造格局和形成时间具有紧密依存关系,虽然对基性岩脉是否为金矿脉提供金源尚无统一定论,但是根据基性岩脉与金矿化紧密的时空关系,基性岩脉可以作为找金矿的良好标志.     

一种基于加权非负矩阵分解的矿产预测方法

提出了一种新颖的基于加权非负矩阵分解的矿产预测方法,运用非负矩阵分解的非负性、降维性及稀疏性对多维矿产数据进行处理。通过R型聚类分析,按照变量相似度将变量聚合成群,对相关性高的元素的聚类结果进行加权非负矩阵分解得到基向量,进行回归分析验证基向量用于矿产预测的有效性。最后,以广东省新寮岽铜多金属矿区数据为例,通过基向量预测圈定异常,绘制矿产预测分布图,得到明显的异常区域,取得了好的预测结果。