A statistical way to describe a surface in space is by specifying the expected value and variance at each point, and their mutual covariances. The surface is assumed to consist of a trend function and a residual function. The covariances between those points are expressed in a covariance function, based on the residual function. An application of the covariance function is Kriging, an interpola...

Archaeological excavation documentation poses several challenges to spatial data management. This work gives a data model, respective query functionality and the description of the resulting prototype system for recording and analyzing the data produced during the course of an excavation. Based on a requirements analysis, we develop a data model that not only captures georeferenced finds (potte...

Data mining is a non-trivial process. That is identifying novel, valid and potentially useful patterns in data. Data mining supports automatic data exploration. That is extracting hidden information from the huge database. Data mining refers to search useful and relevant information from the database. Spatial mining is a branch of data mining. The spatial mining deals with the location or geo-r...

Aggregation and visualization of geographical data are an important part of environmental data mining, environmental modelling, and agricultural management. However, it is difficult to aggregate geospatial data of the various formats, such as maps, census and survey data. This paper presents a framework named PlaniSphere, which can aggregate the various geospatial datasets, and synthesizes raw ...

Analysis of range queries on spatial (multidimensional) data is both important and challenging. Most previous analysis attempts have made certain simplifying assumptions about the datasets and/or queries to keep the analysis tractable. As a result, they may not be universally applicable. This paper proposes a set of five analysis techniques to estimate the selectivity and number of index nodes ...