A model to predict ordinal suitability using sparse and uncertain data

We describe the development of the algorithms that comprise the Spatial Decision Support System (SDSS) CaNaSTA (Crop Niche Selection in Tropical Agriculture). The system was designed to assist farmers and agricultural advisors in the tropics to make crop suitability decisions. These decisions are frequently made in highly diverse biophysical and socioeconomic environments and must often rely on sparse datasets.The field trial datasets that provide a knowledge base for SDSS such as this are characterised by ordinal response variables. Our approach has been to apply Bayes’ formula as a prediction model.This paper does not describe the entire CaNaSTA system, but rather concentrates on the algorithm of the central prediction model. The algorithm is tested using a simulated dataset to compare results with ordinal regression, and to test the stability of the model with increasingly sparse calibration data. For all but the richest input datasets it outperforms ordinal regression, as determined using Cohen’s weighted kappa. The model also performs well with sparse datasets. Whilst this is not as conclusive as testing with real world data, the results are encouraging.     

STARTER: a statistical GIS-based model for the prediction of snow avalanche susceptibility using terrain features—application to Alta Val Badia, Italian Dolomites

This article describes a methodology to localise areas with high potential towards natural snowpack instability under particular meteorological conditions, at the scale of an Alpine valley. Localisation is based on statistically relating known release areas of past avalanche events to maps of: (1) slope inclination, (2) slope orientation (aspect), (3) elevation, (4) distance from crest lines, (5) terrain roughness and (6) concavities/convexities. The maps have been built using two different GIS softwares while the statistical analyses have been performed with a specific software handling also Fuzzy Set theory algorithms. The results of the statistical analyses have been verified on test release—areas which have not been used as input data for the statistical analyses. Verification allowed to quantify how reliably the susceptibility values were calculated, to compare the values obtained using different combinations of terrain features and to finally decide on the most efficient combination. The susceptibility maps were calculated and verified for three different meteorological scenarios (given by three classes of snow depth). Verification has shown that the accuracy of the susceptibility maps was between 67% and 82%. The three susceptibility maps show a remarkable difference in the spatial pattern of the highest susceptibility pixels suggesting that for different meteorological scenarios different classes of terrain features need to be considered.The possibility to make combinations of terrain features and to assess and verify their statistical relationship with release areas of past avalanche events is the major original step made by STARTER. Linking those release areas to meteorological scenarios is an attempt to include in the analysis the combined influence of terrain features and meteorological conditions towards snowpack instability.     

A new method to compensate for bias in magnetotellurics

Error estimates from statistical regression analysis are often obviously too small, leading to doubts about the given equations, the statistical method itself and finally, with resignation, to the conclusion that mathematical equations and reality never agree. However, for magnetotelluric data we have found an almost perfect fit between observed scattering and predicted confidence limits of regression coefficients after accounting for a systematic error—the bias.
Different methods to compensate for bias in magnetotelluric impedance estimation have been described using additional data from a reference station. However, sufficiently accurate reference data are often not available. A new method has been developed that enables bias compensation without additional data. For the new method we derive a linear relationship between the effect of bias and an expression depending on the data fit. From this we extrapolate the solution for the unbiased impedance. The new method assumes a special model of uncorrelated noise as well as an approximation for the structure of the impedance tensor. From each pair of components of the unrotated impedance tensor corresponding to the same output channel, one of the pair can be compensated if its magnitude is large compared to that of the other.
The method has been successfully applied in many cases. We claim that the solution is closer to the true impedance than any solution based on the selection of events. It gives a measure of the partitioning of noise between the electric and magnetic channels.
We applied the method to measurements from the North Anatolian Fault Zone (Turkey) and from the Merapi volcano (Central Java) in the period range 10–2500 s. Different instrumentation was used for the two sets of measurements, but in both cases we used fluxgate magnetometers to measure the magnetic variations.     

Comparison of existing methods for building triangular irregular network,models of terrain from grid digital elevation models

Abstract

Triangulated irregular networks (TINs) are increasingly popular for their efficiency in data storage and their ability to accommodate irregularly spaced elevation points for many applications of geographical information systems. This paper reviews and evaluates various methods for extracting TINs from dense digital elevation models (DEMs) on a sample DEM. Both structural and statistical comparisons show that the methods perform with different rates of success in different settings. Users of DEM to TIN conversion methods should be aware of the strengths and weaknesses of the methods in addition to their own purposes before conducting the conversion.     

一种面向大规模空间数据的拓扑关系检查算法

空间数据拓扑关系检查是GIS应用中空间关系分析及网络分析的重要基础。该文主要面向通用性GIS平台软件中的矢量数据拓扑关系检查,提出一种面向大规模空间数据的拓扑关系检查算法,介绍了算法设计思路、核心数据结构、处理流程等内容,并针对使用较为频繁的"线内无重叠"、"线内无悬线"、"面内无缝隙"3个拓扑关系检查规则进行了算法的详细阐述。最后,对这3个拓扑关系检查算法进行了对比验证和分析。实验表明,该算法在保证拓扑关系检查结果正确性的基础上,具有较高的检查性能,适宜于大规模空间数据的拓扑关系检查。     

A treatise on the use of geostatistics for the characterization of nonrenewable resources

Five decades of geostatistical development are reviewed to summarize the state of the art for spatial interpolation vis-à-vis kriging or a form thereof. Although a search of the literature reveals a variety of kriging methods, there are but two infrastructures for geostatistical interpolation: simple cokriging, for estimating a single variable using two variables, and generalized cokriging, for estimating one or more variables using the same number of variables that are estimated. The many forms of kriging are varieties of these two interpolation infrastructures. This notion is emphasized to aid the selection of an appropriate interpolation model for a nonrenewable resource. These models are discussed, and literature for the models and for applicable software is cited. Additionally, all aspects of spatial interpolation are discussed, including the adequacy of spatial sampling, distribution characteristics of spatial samples, semivariograms, search parameters, and selection of interpolation models in conformance with spatial data characteristics. Finally, the relationship between interpolation and raster-based geographic information systems is emphasized.     

多源空间数据无缝集成研究

随着地理信息系统的社会化发展 ,空间数据共享成为一种必然要求 ,然而要真正实现空间数据共享 ,多格式空间数据集成是一个亟待解决的问题。多源空间数据无缝集成( SIMS)技术实现了一种特殊的数据访问机制 ,不仅提供了直接存取多种数据格式的能力 ,而且使 GIS软件具有跨数据源复合分析功能 ,是实现空间数据共享的有效的技术基础。本文阐述了 SIMS技术体系结构 ,并探讨了 SIMS技术在 GIS软件开发中的应用。     

A peak-cluster assessment method for the identification of upland planation surfaces

Residual upland planation surfaces serve as strong evidence of peneplains during long intervals of base-level stability in the peneplanation process. Multi-stage planation surfaces could aid the calculation of uplift rates and the reconstruction of upland plateau evolution. However, most planation surfaces have been damaged by crustal uplift, tectonic deformation, and surface erosion, thus increasing the difficulty in automatically identifying residual planation surfaces. This study proposes a peak-cluster assessment method for the automatic identification of potential upland planation surfaces. It consists of two steps: peak extraction and peak-cluster characterization. Three critical parameters, namely, landform planation index (LPI), peak elevation standard deviation, and peak density, are employed to assess peak clusters. The proposed method is applied and validated in five case areas in the Tibetan Plateau using a Shuttle Radar Topography Mission digital elevation model (SRTM DEM) with 3 arc-second resolution. Results show that the proposed method can effectively extract potential planation surfaces, which are found to be stable with different resolutions of DEM data. A significant planation characteristic can be obtained in the relatively flat areas of the Gangdise–Nyainqentanglha Mountains and Qaidam Basin. Several vestiges of potential former planation areas are also extracted in the hilly-gully areas of the western part of the Himalaya Mountains, the northern part of the Tangula–Hengduan Mountains, and the northeastern part of the Kunlun–Qinling Mountains despite the absence of significant topographical features characterized by low slope angles or low terrain reliefs. Vestiges of planation surfaces are also identified in these hilly-gully upland areas. Hence, the proposed method can be effectively used to extract potential upland planation surfaces not only in flat areas but also in hilly-gully areas.     

Multi-level method for discovery of regional co-location patterns

Regional co-location patterns represent subsets of feature types that are frequently located together in sub-regions in a study area. These sub-regions are unknown a priori, and instances of these co-location patterns are usually unevenly distributed across a study area. Regional co-location patterns remain challenging to discover. This study developed a multi-level method to identify regional co-location patterns in two steps. First, global co-location patterns were detected, and other non-prevalent co-location patterns were identified as candidates for regional co-location patterns. Second, an adaptive spatial clustering method was applied to detect the sub-regions where regional co-location patterns are prevalent. To improve computational efficiency, an overlap method was developed to deduce the sub-regions of (k + 1)-size co-location patterns from the sub-regions of k-size co-location patterns. Experiments based on both synthetic and ecological data sets showed that the proposed method is effective in the detection of regional co-location patterns.     

A framework to enhance semantic flexibility for analysis of distributed phenomena

While some geographic phenomena hold uniform properties, such as land‐use zones, many geographic phenomena are distributed such that their properties vary across an extended area. While such distributed phenomena are best represented as continuous surfaces, individual objects (or features) often emerge among clusters of high or low values in a field. For example, areas of relatively high elevation may be viewed as hills, while flat low‐lying areas are perceived as plains in a terrain. A comprehensive spatial analysis of distributed phenomena should examine both the spatial variance of its attribute surfaces and the characteristics of individual objects embedded in the field. An immediate research challenge to meet such spatial analysis needs is that these emerging features often have vague boundaries that vary according to the use and the user. The nature, and even existence, of these objects depend upon the range of values, or thresholds, used to define them. We propose a representation framework that takes a dual raster‐vector approach to capture both field‐ and object‐like characteristics of distributed phenomena and maintain multiple representations of embedded features delineated by boundaries that are likely to be relevant for the expected uses of the data. We demonstrate how boundaries influence the analysis and understanding of spatiotemporal characteristics of distributed phenomena. Using precipitation as a proof of concept, we show how the proposed framework enhances semantic flexibility in spatiotemporal query and analysis of distributed phenomena in geographic information systems.     

Predicting air temperature simultaneously for multiple locations in an urban environment: A bottom up approach

Cities are characterized by high heterogeneity that results in varied microclimate effects. The current study introduces a new bottom–up approach linking the urban Canyon Air Temperature (CAT) model with spatially distributed inputs extracted from a GIS data-base and remote sensing products to predict intra-urban temperature variability simultaneously for multiple locations in an urban environment. To provide proof of concept, the model was applied for the city of Bat-Yam, Israel. Simulation shows a maximum nighttime urban heat island (UHI) intensity of 2–2.25 °C, relative to a rural reference point, during both summer and winter, with significant spatial variability related to the height-to-width ratio of urban street canyons and to the surface land cover. The CAT simulation also highlighted the important influence of the local wind regime on the development and persistence of the nocturnal UHI. We conclude that linking CAT to a GIS data-base supports simulations at the city scale that reflect the local intra-urban variability. The model can be used to investigate both macro and micro scale spatio-temporal characteristics of the UHI in various urban development scenarios, which may be applied to generate appropriate geographically-explicit mitigation and adaptation measures.     

A data model for moving regions of fixed shape in databases

Moving object databases are designed to store and process spatial and temporal object data. An especially useful moving object type is a moving region, which consists of one or more moving polygons suitable for modeling the spread of forest fires, the movement of clouds, spread of diseases and many other real-world phenomena. Previous implementations usually allow a changing shape of the region during the movement; however, the necessary restrictions on this model result in an inaccurate interpolation of rotating objects. In this paper, we present an alternative approach for moving and rotating regions of fixed shape, called Fixed Moving Regions, which provide a significantly better model for a wide range of applications like modeling the movement of oil tankers, icebergs and other rigid structures. Furthermore, we describe and implement several useful operations on this new object type to enable a database system to solve many real-world problems, as for example collision tests, projections and intersections, much more accurate than with other models. Based on this research, we also implemented a library for easy integration into moving objects database systems, as for example the DBMS Secondo (1) (2) developed at the FernUniversität in Hagen.     

A syllabus for teaching geographical information systems

The problems in teaching a course on geographical information systems in higher education are reviewed and a possible syllabus that will be most relevant to needs in the United Kingdom is suggested. The syllabus is divided into six major sections relating to the context, cartographic and spatial analytical concepts, their realization in a computing environment, operations, applications and institutional issues. A particular feature is an attempt to maintain a rigid distribution between underlying theory which is relatively stable and its more volatile operationalization on computers. The paper results from a symposium held in Leicester (U.K.) late in 1988 that brought together nine specialists in the held.     

A visualization approach for discovering colocation patterns

Colocation mining is one of the major spatial data mining tasks. When discovering colocation patterns, spatial statistics or data mining approaches are commonly used. Colocation mining results are typically presented in a textual form and do not provide any spatial information; thus, the results lack an intuitive approach to obtain cognition of colocation rules. Here, we propose a visualization approach to discover colocation patterns for two independent point distributions and generate visual results. This approach makes use of the ability of human color perception. For two geographic features, our approach first generates density surfaces of the input features and then visualizes the density surfaces using a red or green light with different intensities. Then, based on the law of additive color mixing, our approach mixes the colors of the two density surfaces to generate a colocation rule map. The visualization approach can also provide local details of colocation and be used for local colocation analysis. Users can detect colocation patterns and their distribution from the colocation rule maps. We use both synthetic data and real data to test the performance of our approach.     

数字社区数据标准研究

当前数字社区建设缺乏统一的智能化综合管理平台以及相应的各项数据标准，使得数字社区难以真正实现统一的数字化管理。该文参考国内外相应数据标准，研究数字社区的标准体系结构。针对现有标准体系缺乏相关数据标准的现状，提出数字社区系列数据标准，包括数据接口层标准、数据分类编码标准、数据模型、数据字典和元数据标准。阐述系列标准中各标准间的相互关系以及各标准所包含的具体内容。