摘要:

Data and information about the location of features on or near the Earth's surface are not without error or uncertainty they can be misrepresented and used for purposes that are inconsistent with their attributes and other characteristics. These difficulties can result in harm to the individual or organization that relies on the material, and the real or perceived possibility of this harm can be an impediment to development, dissemination and the use of geographical information products. The issue then becomes one of whom is liable for this material and how shall this liability be managed. Insurance is already applied as a major mechanism for the management of risk and liability in our everyday lives, and it is a mechanism available to both governments and private organizations alike. This paper presents and discusses the application of liability insurance mechanisms to the special problems of collection, assembly, dissemination and use of geographical information.

ISSN:1365-8816    

DOI:10.1080/136588198241879

出版商:Taylor & Francis Group    

年代:1998

数据来源: Taylor

摘要:

The paper explores connections between decision support systems, remotely sensed and GIS data for environmental planning, and monitoring secondary soil salinization. The paper introduces a decision support knowledge base system called SALT MANAGER used as the starting point for three experiments in data mining which structure the paper. In the first, classified GIS data is passed to an inductive learning programme. The task is to reconstruct the classification rules. The resulting rules are used to improve knowledge base system performance. The second experiment reports on patterns of attribute value combinations occurring for specific classification classes. These patterns can be used to elicit new knowledge in the domain and lead to a form of knowledge discovery. This process is commonly referred to as ‘data mining'. The third experiment measures the effect of an additional electromagnetic data layer on the knowledge base system. Again, our efforts yield novel knowledge discovery results from the application of data mining techniques. Finally, a comparison of different machine learning algorithms in the secondary salinization domain is given.

ISSN:1365-8816    

DOI:10.1080/136588198241888

出版商:Taylor & Francis Group    

年代:1998

数据来源: Taylor

摘要:

Geographical Information Systems (GIS) involve the manipulation of large spatial data sets, and the performance of these systems is often determined by how these data sets are organized on secondary storage (disk). This paper describes a simulation study investigating the performance of two non-recursive spatial clustering methods the Inverted Naive and the Spiral methods in extensive detail and comparing them with the Hilbert fractal method that has been shown in previous studies to outperform other recursive clustering methods. The paper highlights the importance of analysing the sample variance when evaluating the relative performance of various spatial ordering methods. The clustering performance of the methods is examined in terms of both the mean and variance values of the number of clusters (runs of consecutive disk blocks) that must be accessed to retrieve a query region of a given size and orientation. The results show that, for a blocking factor of 1, the mean values for the Spiral method are the best, and on average, about 30% better than for the other two methods. In terms of variance, the inverted naive method is the best followed by the Spiral and Hilbert methods, in that order. We also study the impact of varying query size and the skew ratio (between the X and Y dimensions) for each method. While these performance results do not generalize for higher blocking factors, we believe that they are useful for both researchers and practitioners to know because several previous studies have also examined this special case, and also because it has important implications for the performance of GIS applications.

ISSN:1365-8816    

DOI:10.1080/136588198241897

出版商:Taylor & Francis Group    

年代:1998

数据来源: Taylor

ISSN:1365-8816    

DOI:10.1080/136588198241905

出版商:Taylor & Francis Group    

年代:1998

数据来源: Taylor