Ricardo A. Olea

Engineers and earth scientists are increasingly interested in quantitative methods for the analysis, interpretation, and modeling of data that imperfectly describe natural processes or attributes measured at geographical locations. Inference from imperfect knowledge is the realm of classical statistics. In the case of many natural phenomena, auto- and cross- correlation preclude the use of classical statistics. The appropriate choice in such circumstances is geostatistics, a collection of numerical techniques for the characterization of spatial attributes similar to the treatment in time series analysis of auto-correlated temporal data. As in time series analysis, most geostatistical techniques employ random variables to model the uncertainty that goes with the assessments. The applicability of the methods is not limited by the physical nature of the attributes. Geostatistics for Engineers and Earth Scientists presents a concise introduction to geostatistics with an emphasis on detailed explanations of methods that are parsimonious, nonredundant, and through the test of time have proved to work satisfactorily for a variety of attributes and sampling schemes. Most of these methods are various forms of kriging and stochastic simulation. The presentation follows a modular approach making each chapter as self-contained as possible, thereby allowing for reading of individual chapters, reducing excessive cross-referencing to previous results and offering possibilities for reviewing similar derivations under slightly different circumstances. Guidelines and rules are offered wherever possible to help choose from among alternative methods and to select parameters, thus relieving the user from making subjective calls based on an experience that has yet to be acquired. Geostatistics for Engineers and Earth Scientists is intended to assist in the formal teaching of geostatistics or as a self tutorial for anybody who ismotivated to employ geostatistics for sampling design, data analysis, or natural resource characterization. Real data sets are used to illustrate the application of the methodology.

If you want to achieve something, if you want to write a book, paint a picture, be sure the center of your existence is somewhere else and that it’s solidly grounded; only then will you be able to keep your cool and laugh at the attacks that are bound to come. ” P. Feyerabend This is a book about interdisciplinary public health reasoning and epidemic m- th elling, in general, and the study of the infamous 14 century AD Black Death d- aster, in particular. We focus on the intellectual context in which epidemic mod- ling takes place, in a way that accounts for the present-day interdisciplinary and multicultural trends in scientific inquiry. Like most scientific fields, public health research defines itself based on knowledge, which raises serious epistemic and cognitive issues. Therefore, we maintain that for public health modellers to fu- tion in an often complex environment, they should be aware of the divergent c- ceptions of knowledge and the technological changes that these imply, the mul- ple sources of information commonly available and their reliability, the different styles of thinking adopted by the disciplines involved, and the importance of - veloping sound interdisciplinary knowledge integration skills.

If you want to achieve something, if you want to write a book, paint a picture, be sure the center of your existence is somewhere else and that it’s solidly grounded; only then will you be able to keep your cool and laugh at the attacks that are bound to come. ” P. Feyerabend This is a book about interdisciplinary public health reasoning and epidemic m- th elling, in general, and the study of the infamous 14 century AD Black Death d- aster, in particular. We focus on the intellectual context in which epidemic mod- ling takes place, in a way that accounts for the present-day interdisciplinary and multicultural trends in scientific inquiry. Like most scientific fields, public health research defines itself based on knowledge, which raises serious epistemic and cognitive issues. Therefore, we maintain that for public health modellers to fu- tion in an often complex environment, they should be aware of the divergent c- ceptions of knowledge and the technological changes that these imply, the mul- ple sources of information commonly available and their reliability, the different styles of thinking adopted by the disciplines involved, and the importance of - veloping sound interdisciplinary knowledge integration skills.

Engineers and earth scientists are increasingly interested in quantitative methods for the analysis, interpretation, and modeling of data that imperfectly describe natural processes or attributes measured at geographical locations. Inference from imperfect knowledge is the realm of classical statistics. In the case of many natural phenomena, auto- and cross- correlation preclude the use of classical statistics. The appropriate choice in such circumstances is geostatistics, a collection of numerical techniques for the characterization of spatial attributes similar to the treatment in time series analysis of auto-correlated temporal data. As in time series analysis, most geostatistical techniques employ random variables to model the uncertainty that goes with the assessments. The applicability of the methods is not limited by the physical nature of the attributes. Geostatistics for Engineers and Earth Scientists presents a concise introduction to geostatistics with an emphasis on detailed explanations of methods that are parsimonious, nonredundant, and through the test of time have proved to work satisfactorily for a variety of attributes and sampling schemes. Most of these methods are various forms of kriging and stochastic simulation. The presentation follows a modular approach making each chapter as self-contained as possible, thereby allowing for reading of individual chapters, reducing excessive cross-referencing to previous results and offering possibilities for reviewing similar derivations under slightly different circumstances. Guidelines and rules are offered wherever possible to help choose from among alternative methods and to select parameters, thus relieving the user from making subjective calls based on an experience that has yet to be acquired. Geostatistics for Engineers and Earth Scientists is intended to assist in the formal teaching of geostatistics or as a self tutorial for anybody who ismotivated to employ geostatistics for sampling design, data analysis, or natural resource characterization. Real data sets are used to illustrate the application of the methodology.