Data gaussianity is an important tool in spatial statistical modeling as well experimental data analysis. Usually field and observation deviate significantly from the normal distribution. This work presents alternative methods for transformation revisits applicability of a modified version well-known Box-Cox technique. The recently proposed method has significant advantage transforming negative...

An overview is presented of the use of spatial data structures in spatial databases. The focus is on hierarchical data structures, including a number of variants of quadtrees, which sort the data with respect to the space occupied by it. Such techniques are known as spatial indexing methods. Hierarchical data structures are based on the principle of recursive decomposition. They are attractive ...

Spatial data warehouses (SDW) enable analytical multidimensional queries together with spatial analysis. Mainly, three operations are related to SDW query processing performance: (i) joining large fact tables and large spatial and non-spatial dimension tables; (ii) computing one or more costly spatial predicates based on spatial ad hoc query windows; and (iii) aggregating data according to diff...

National spatial data clearinghouses, worldwide development and impact PhD Thesis Wageningen University, Wageningen 2006. – with references-with summaries in English and Dutch

Spatial computing is a set of ideas and technologies that facilitate understanding the geo-physical world, knowing and communicating relations to places in that world, and navigating through those places. The transformational potential of mobility services is already evident. From Google Maps [17] to consumer Global Positioning System (GPS) devices, society has benefited immensely from spatial ...

Information is not a neutral commodity, and discussion of its position and use within the humanitarian system should not be limited to purely technical questions to do with databases, the Internet or.. . GIS technologies.

In the analogue era, concern for spatial data and its distribution were tasks for mapping organisations earning a reputation for producing quality products based on their spatial characteristics, including their visualisation in printed map form to be used for many different types of problems. In contrast, from the second half of the twentieth century onwards, with the developments in computer ...

The paper deals with the relations between knowledge management, uncertainty and the context evaluation on the background of the new possibilities of information technologies that can help us to carry out the knowledge management strategies. The paper discusses the problem of wide context including to compensate and decrease the uncertainty of data on one hand and to increase accuracy of classi...

This chapter is concerned with multidimensional data models for spatial data warehouses. Over the last few years different approaches have been proposed in the literature for modelling multidimensional data with geometric extent. Nevertheless, the definition of a comprehensive and formal data model is still a major research issue. The main contributions of the chapter are twofold: first, it dra...

Spatial Data Mining is the process of finding hidden patterns in a large spatial data set. It can be defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data . Spatial data refers to the data that pertains location and spatial dimensions of geographical entities. Some objects have spatial attributes such as positions or areas. An example...