The statistical analysis of spatial data is usually done under Gaussian assumption for the underlying random field model. When this assumption is not satisfied, block bootstrap methods can be used to analyze spatial data. One of the crucial problems in this setting is specifying the block sizes. In this paper, we present asymptotic optimal block size for separate block bootstrap to estimate the...

This paper describes the use of ArcGIS and the 3D Analyst extension for the integration of multiple petroleum datasets as part of research undertaken by the University of Newcastle upon Tyne, UK, on behalf of Shell International Exploration and Production. The integration of datasets from both surface and subsurface application areas is essential to present a complete picture for decision-makin...

Spatial data mining is the process of discovering, motivating and previously unknown, but potentially helpful patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more tricky than extracting the parallel patterns from established numeric and definite data due to the complexity of spatial data types, spatial relationships, and spatial autocorr...

A wide range of aggregate data is used to examine the spatial variation in homeownership rates among a cross-section of 96 German planning regions. The analysis accounts for potential endogeneity of prices and affordability as well as for spatial dependence in homeownership levels. In addition to familiar demographic and socioeconomic variables, the regional price and affordability of owner-occ...

Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatia...