Andrew D. Heap

Bio: Andrew D. Heap is an academic researcher from Geoscience Australia. The author has contributed to research in topics: Reef & Sediment. The author has an hindex of 19, co-authored 46 publications receiving 2284 citations. Previous affiliations of Andrew D. Heap include University of Tasmania & James Cook University.

Review: Spatial interpolation methods applied in the environmental sciences: A review

Abstract: Spatially continuous data of environmental variables are often required for environmental sciences and management. However, information for environmental variables is usually collected by point sampling, particularly for the mountainous region and deep ocean area. Thus, methods generating such spatially continuous data by using point samples become essential tools. Spatial interpolation methods (SIMs) are, however, often data-specific or even variable-specific. Many factors affect the predictive performance of the methods and previous studies have shown that their effects are not consistent. Hence it is difficult to select an appropriate method for a given dataset. This review aims to provide guidelines and suggestions regarding application of SIMs to environmental data by comparing the features of the commonly applied methods which fall into three categories, namely: non-geostatistical interpolation methods, geostatistical interpolation methods and combined methods. Factors affecting the performance, including sampling design, sample spatial distribution, data quality, correlation between primary and secondary variables, and interaction among factors, are discussed. A total of 25 commonly applied methods are then classified based on their features to provide an overview of the relationships among them. These features are quantified and then clustered to show similarities among these 25 methods. An easy to use decision tree for selecting an appropriate method from these 25 methods is developed based on data availability, data nature, expected estimation, and features of the method. Finally, a list of software packages for spatial interpolation is provided. Display Omitted Comparison of commonly used spatial interpolation methods in environmental science.Analysis of factors affecting the performance of spatial interpolation methods.Classification of 25 methods to illustrate their relationship.Guidelines for selecting an appropriate method for a given dataset.A list of software packages for commonly used spatial interpolation methods.

Application of machine learning methods to spatial interpolation of environmental variables

Abstract: Machine learning methods, like random forest (RF), have shown their superior performance in various disciplines, but have not been previously applied to the spatial interpolation of environmental variables. In this study, we compared the performance of 23 methods, including RF, support vector machine (SVM), ordinary kriging (OK), inverse distance squared (IDS), and their combinations (i.e., RFOK, RFIDS, SVMOK and SVMIDS), using mud content samples in the southwest Australian margin. We also tested the sensitivity of the combined methods to input variables and the accuracy of averaging predictions of the most accurate methods. The accuracy of the methods was assessed using a 10-fold cross-validation. The spatial patterns of the predictions of the most accurate methods were also visually examined for their validity. This study confirmed the effectiveness of RF, in particular its combination with OK or IDS, and also confirmed the sensitivity of RF and its combined methods to the input variables. Averaging the predictions of the most accurate methods showed no significant improvement in the predictive accuracy. Visual examination proved to be an essential step in assessing the spatial predictions. This study has opened an alternative source of methods for spatial interpolation of environmental properties.

Classification of Australian Clastic Coastal Depositional Environments Based Upon a Quantitative Analysis of Wave, Tidal, and River Power

Abstract: A statistical assessment of wave, tide, and river power was carried out using a database of 721 Australian clastic coastal depositional environments to test whether their geomorphology could be predicted from numerical values. The geomorphic classification of each environment (wave- and tide-dominated deltas, wave- and tide-dominated estuaries, lagoons, strand plains, and tidal flats) was established independently from remotely sensed imagery. To our knowledge, such a systematic numerical analysis has not been previously attempted for any region on earth. The results of our analysis indicate that a relationship exists between the ratio of annual mean wave power to mean tidal power and the geomorphic development of clastic coastal depositional environments. Deltas and estuaries are associated with statistically significant differences in mean wave and tidal power. Statistically significant distinctions between populations of deltas, estuaries, strand plains, and tidal flats are also associated with river discharge and river flow rate (defined as discharge divided by open water area). Our results support the hypotheses of previous workers that wave, tide, and river power exert the principal control over the gross geomorphology and facies distribution patterns in clastic coastal depositional environments. Mean values and confidence limits of wave power, tide power, and river flow for the coastal depositional environments targeted in this study may provide a basis for the comparison of modern environments as well as constraints for paleo-reconstructions.

Statistics for Spatio-Temporal Data

Abstract: Noel Cressie and Christopher WikleHardcover: 624 pagesYear: 2011Publisher: John WileyISBN-13: 978-0471692744Here is the new reference book about spatial and spatio-temporal statistical modeling! No...

Review of wave energy technologies and the necessary power-equipment

Abstract: The wave energy is having more and more interest and support as a promising renewable resource to replace part of the energy supply, although it is still immature compared to other renewable technologies. This work presents a complete analysis of the wave energy technology, starting with the characterisation of this global resource in which the most suitable places to be exploited are showed, and the classification of the different types of wave energy converters in according to several features. It is also described in detail each of the stages that are part in the energy conversion, that is, from the capture of the energy from the waves to the extraction of a proper electrical signal to be injected to the grid. Likewise, existing offshore energy transmission alternatives and possible layouts are described.

Geographically Weighted Regression

Abstract: Being newly immersed in the upstream part of the oil business, I just recently had my first work session with data in ARC–GIS®. The project involves subsurface geographical modeling. Obviously I had considerable interest in discovering if the methodology in this book would enhance my modeling capabilities. The book is for social scientists, but I had no difficulty imagining my own important oil exploration application within the framework of geographically weighted regression (GWR). The first chapter nicely explains what is unique in this book. A standard regression model using geographically oriented data (the example is housing prices across all of England) is a global representation of a spatial relationship, an average that does not account for any local differences. In y = f (x), imagine a whole family of f ’s that are indexed by spatial location. That is the focus of this book. It is about one form of local spatial modeling, which is GWR. A more general resource for this topic is the earlier book by Fotheringham and Wegener (2000), which escaped the notice of Technometrics. Imagine a display of model parameters in a geographical information system (GIS) and you will understand the focus for this book. The authors note, “only where there is no significant spatial variation in a measured relationship can global models be accepted” (p. 10). The second chapter develops the basis of GWR. It analyzes the housing sales prices versus the 33 boroughs in London and begins by fitting a conventional multiple regression model versus housing characteristics. The GWR is motivated by differences in the regression models fitted separately by borough. The GWR is a spatial moving-window approach with all data distances weighted versus a specific data point using a weighting function and a bandwidth. A GIS can then be used to evaluate the spatial dependency of the parameters. As in kriging, local standard errors also are calculated. The chapter also provides all the math. Chapter 3 comprises several further considerations: parameters that are globally constant, outliers, and spatial heteroscedasticity. The first issue leads to hypothesis tests for model comparison using an Akaike information criterion (AIC). Local outliers are hard to detect. Studentized (deletion) residuals are recommended. The outliers can be plotted geographically. Robust regression is suggested as a less computationally intensive alternative. Hetereoscedasticity is harder to handle. Chapter 4 adds statistical inference to the capabilities of GWR: both a confidence interval approach using local likelihood and an AIC method. Four additional methodology chapters present various extensions of GWR. Chapter 5 considers the relationship between GWR and spatial autocorrelation, and includes a combined version of GWR and spatial regression using some complex hybrid models. Chapter 6 examines the relationship of scale and zoning problems in spatial analysis to GWR. Chapter 7 introduces the use of initial exploratory data analysis using geographically weighted statistics, which are based on the idea of using a kernel around each data point to create weights. Univariate statistics and correlation coefficients are defined for exploring local patterns in data. A final set of extensions in Chapter 8 discusses regression models with non-Gaussian errors, logistic regression, local principal components analysis, and local probability density estimation. The methods all use some kind of distributional model. The million-dollar question for me is always, “What about software?” The authors have a stand-alone program, GWR 3, available in CD–ROM by contacting the authors. Basically the drill with GWR 3 is to gather your data, use Excel to transform and reformat the data for GWR 3, use GWR 3 to produce a set of coefficients, and feed those coefficients to your favorite GIS to produce your maps. Forty pages of discussion about using the software are provided. A final epilogue chapter also discusses embedding GWR in R or Matlab and includes some references to people who have done that type of work. I probably would not have read this book if I had not happened to have had it in my briefcase on a visit with the exploration technologists. Though inclusive of appropriate mathematical development, this material is readily approachable because of the many illustrations and the pages and pages of GIS displays. The authors unabashedly present much of the material as their developmental work, so GWR offers a lot of opportunity for research and further development through novel applications and extensions.