Adjustment computations: spatial data analysis

The book is a standard textbook on least squares adjustment for surveying students. It was originally written by Paul R.Wolf in the early 1980s. Now the fifth edition is available, authored for by Charles D. Ghilani, who was already principal author for the fourth edition. The book covers least squares adjustment, quality estimation of the results, and a range of applications. The book provides an introduction to the matrix calculus and its use to solve systems of equations, basic statistics, linearization using Taylor’s theorem, and map projections. This is useful for readers lacking experience in the field of adjustment computations or surveying because all information to understand the problems and the solutions are provided in the same form. The book covers the topics in cycles of three steps: first, the theory is laid out and the necessary formulae are developed. This is done slowly. The necessary formulae are not only listed and mathematically developed but also carefully explained and discussed. Practical examples show the application of the formulae. The software available on www.wiley.com allows the results given in the book to be checked through experiments with similar examples. Finally, review questions provide feedback on the understanding of what was just learned. This gives students plenty of opportunity to test their knowledge and to learn to apply the theory. The drawback of this method is that it requires a lot of space. Together with a very readable style of writing this creates a book consisting of 650 pages. The book is written for surveyors and students of surveying. This is clearly reflected by both wording and examples. The stochastic variables that need corrections due to random deviations are called observations and they usually represent distance, angle, or coordinate measurements. The use of the ‘true value’ concept is typically for surveyors as is the separation between random, systematic, and gross errors (blunders). All problems that emerge in surveying practice, from the adjustment of level nets to the combined adjustment of GPS and terrestrial observations are dealt with. However, in some places the discussion could be elaborated in more detail or at least reference to literature containing this discussion could be added. A simple example is the chapter on error ellipses where the use of eigenvalues and eigenvectors would provide a different perspective for computational algorithms. Mathematicians would have structured the book differently. They typically start their discussion by defining the properties desired for the solution, for example, best, linear, uniformly unbiased estimate (BLUUE). Then they develop the solution using vector spaces and similar tools. The advantage of this method is that differences between solutions can be shown based on the properties selected in the beginning. This helps to consolidate the understanding of the problem. However, the requirements for the mathematical background necessary to understand these discussions are significantly higher than the requirements for this book. It is thus more suitable for bachelor students and practitioners. Scientists who seek deep knowledge and want to understand the embedding of adjustment theory in the system of mathematical concepts may need to read different books. Teachers, however, may find many useful examples to illustrate problems and their respective solutions. Unfortunately, some methods are not (yet?) covered in the book. The whole field of robust estimation is completely ignored. It would have probably exceeded the scope of the 326 Book Reviews