Lynette Bloom

Direct minimum/maximum autocorrelation factors within the framework of a two structure linear model of coregionalisation

In this paper, we present an approach to the method of minimum/maximum autocorrelation factors (MAF) that involves the derivation of the factors in the space of the sample data. The usual approach to MAF begins with an a priori normal score transformation of each attribute. However, as the MAF method is based on principal component analysis (PCA) this initial transformation is unnecessary. Since our method derives the MAF directly in the space of the sample data, we refer to it as direct minimum/maximum autocorrelation factors (DMAF). We present a theoretical derivation of DMAF that simplifies the multi-Gaussian approach. The DMAF method is particularly advantageous when the factors are simulated using a direct simulation algorithm, as no further transformation is required. We demonstrate the DMAF method by means of the simulation of attributes from a multivariate soil data set and show that this method successfully transforms the sample attributes into uncorrelated factors for all lag spacings and is useful for multivariate simulation.

Implementation of enhanced areal interpolation using MapInfo

Abstract In the analysis of spatial data there may be a need to transfer data collected originally on one set of areal units (source regions) to a different set of areal units (target regions). The MapInfo Desktop Mapping Package currently enables the user to carry out this process by simple areal weighting. The Enhanced Areal Interpolation method introduced by Flowerdew, Green, and Kehris, which is essentially an application of the EM Algorithm, postulates a more sophisticated statistical model and makes use of ancillary information available on the target regions. It is an iterative method and uses the simple areal weighting estimates as its starting point. In this paper we shall describe the implementation of this method for count data in MapInfo, thus making it readily accessible to the general user.

Direct sequential simulation with histogram reproduction: A comparison of algorithms

Sequential simulation is a widely used technique applied in geostatistics to generate realisations that reproduce properties such as the mean, variance and semivariogram. Sequential Gaussian simulation requires the original variable to be transformed to a standard normal distribution before implementing variography, kriging and simulation procedures. Direct sequential simulation allows one to perform the simulation using the original variable rather than in normal score space. The shape of the local probability distribution from which simulated values are drawn is generally unknown and this results in direct simulation not being able to guarantee reproduction of the target histogram; only the Gaussian distribution ensures reproduction of the target distribution, and most geostatistical data sets are not normally distributed. This problem can be overcome by defining the shape of the local probability distribution through the use of constrained optimisation algorithms or by using the target normal-score transformation. We investigate two non-parametric approaches based on the minimisation of an objective function subject to a set of linear constraints, and an alternative approach that creates a lookup table using Gaussian transformation. These approaches allow the variography, kriging and simulation to be performed using original data values and result in the reproduction of both the histogram and semivariogram, within statistical fluctuations. The programs for the algorithms are written in Fortran 90 and follow the GSLIB format. Routines for constrained optimisation have been incorporated.

King Prawn Catch by Grade Category from an Economic and a Stock Management Perspective

We give a geostatistical analysis of western king prawn logbook data collected from the Shark Bay prawn fishing fleet in Western Australia for the 2000 and the 2004 fishing seasons, aggregated into total catch, together with three weight sub-classes and grouped into lunar months. For each of the two years we discuss both the spatial correlation between the weight classes and the spatial correlation for corresponding months in the two years under consideration. Finally, we use a cost function that takes account of the different weight classes to compare the financial return by location between 2000 and 2004.

Total Catch and Effort in the Shark Bay King Prawn Fishery

This poster details the variography and the Ordinary Kriging estimation maps for catch rates (kg/h) of western king prawns (Peneous latisulcatus) in the Shark Ray Managed Prawn Fishery.