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This book provides a thoughtful and rigorous guide to coverage modeling, reviewing essential models, solution approaches, and related applications. Since the early developments of the Location Set Covering Problem and the Maximal Covering Location Problem, models based upon some form of coverage have been extended and applied in a number of areas, helping to improve services offered to citizens of large cities and regions. Examples include trauma care services, transit systems design, cell tower location, and many others. The book not only describes the strengths and weaknesses of currently available models, but also presents details on major developments, including solution procedures and applications, making it well suited both as a reference text and a textbook for graduate level courses.

Spatial econometrics deals with spatial dependence and spatial heterogeneity, critical aspects of the data used by regional scientists. These characteristics may cause standard econometric techniques to become inappropriate. In this book, I combine several recent research results to construct a comprehensive approach to the incorporation of spatial effects in econometrics. My primary focus is to demonstrate how these spatial effects can be considered as special cases of general frameworks in standard econometrics, and to outline how they necessitate a separate set of methods and techniques, encompassed within the field of spatial econometrics. My viewpoint differs from that taken in the discussion of spatial autocorrelation in spatial statistics - e.g., most recently by Cliff and Ord (1981) and Upton and Fingleton (1985) - in that I am mostly concerned with the relevance of spatial effects on model specification, estimation and other inference, in what I caIl a model-driven approach, as opposed to a data-driven approach in spatial statistics. I attempt to combine a rigorous econometric perspective with a comprehensive treatment of methodological issues in spatial analysis.

Book is in NEW condition. 2.05

This book provides a thoughtful and rigorous guide to coverage modeling, reviewing essential models, solution approaches, and related applications. Since the early developments of the Location Set Covering Problem and the Maximal Covering Location Problem, models based upon some form of coverage have been extended and applied in a number of areas, helping to improve services offered to citizens of large cities and regions. Examples include trauma care services, transit systems design, cell tower location, and many others. The book not only describes the strengths and weaknesses of currently available models, but also presents details on major developments, including solution procedures and applications, making it well suited both as a reference text and a textbook for graduate level courses.

Acceptable/Fair condition. Book is worn, but the pages are complete, and the text is legible. Has wear to binding and pages, may be ex-library. 1.2

This richly illustrated third edition provides a thorough training in practical mathematical biology and shows how exciting mathematical challenges can arise from a genuinely interdisciplinary involvement with the biosciences. It has been extensively updated and extended to cover much of the growth of mathematical biology. From the reviews: ""This book, a classical text in mathematical biology, cleverly combines mathematical tools with subject area sciences."--SHORT BOOK REVIEWS

Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio­-temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R-INLA, which has proven to be a valid alternative to the commonly used Markov Chain Monte Carlo simulations

Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio­-temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R-INLA, which has proven to be a valid alternative to the commonly used Markov Chain Monte Carlo simulations

Unread book in perfect condition.

Spatial econometrics deals with spatial dependence and spatial heterogeneity, critical aspects of the data used by regional scientists. These characteristics may cause standard econometric techniques to become inappropriate. In this book, I combine several recent research results to construct a comprehensive approach to the incorporation of spatial effects in econometrics. My primary focus is to demonstrate how these spatial effects can be considered as special cases of general frameworks in standard econometrics, and to outline how they necessitate a separate set of methods and techniques, encompassed within the field of spatial econometrics. My viewpoint differs from that taken in the discussion of spatial autocorrelation in spatial statistics - e.g., most recently by Cliff and Ord (1981) and Upton and Fingleton (1985) - in that I am mostly concerned with the relevance of spatial effects on model specification, estimation and other inference, in what I caIl a model-driven approach, as opposed to a data-driven approach in spatial statistics. I attempt to combine a rigorous econometric perspective with a comprehensive treatment of methodological issues in spatial analysis.

This book presents a geometric voting model for analyzing parliamentary roll call data. Each legislator is represented by one point and each roll call is represented by two points that correspond to the policy consequences of voting Yea or Nay. On every roll call each legislator votes for the closer outcome point, at least probabilistically. These points form a spatial map that summarizes the roll calls. In this sense a spatial map is much like a road map because it visually depicts the political world of a legislature. These maps can be used to study a wide variety of topics related to legislative voting.

From the Preface: An important goal of historical archeology is to use its rich data to solve problems of general archeological and anthropological interest. One general problem concerns the spatial scale of sociocultural processes in stratified societies.

This richly illustrated third edition provides a thorough training in practical mathematical biology and shows how exciting mathematical challenges can arise from a genuinely interdisciplinary involvement with the biosciences. It has been extensively updated and extended to cover much of the growth of mathematical biology. From the reviews: ""This book, a classical text in mathematical biology, cleverly combines mathematical tools with subject area sciences."--SHORT BOOK REVIEWS

Professors Merrill and Grofman develop a unified model that incorporates voter motivations and assesses its empirical predictions--for both voter choice and candidate strategy--in the United States, Norway, and France. The analyses show that a combination of proximity, direction, discounting, and party ID are compatible with the mildly but not extremely divergent policies that are characteristic of many two-party and multiparty electorates. All of these motivations are necessary to understand the linkage between candidate issue positions and voter preferences.

Spatial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis. The text covers different modeling-related topics for continuous dependent variables, including: mapping data on spatial units, exploratory spatial data analysis, working with regression models that have spatially dependent regressors, and estimating regression models with spatially correlated error structures.  Using social sciences examples based on real data, Michael D. Ward and Kristian Skrede Gleditsch illustrate the concepts discussed, and show how to obtain and interpret relevant results. The examples are presented along with the relevant code to replicate all the analysis using the R package for statistical computing. Users can download both the data and computer code to work through all the examples found in the text. New to the Second Edition is a chapter on mapping as data exploration and its role in the research process, updates to all chapters based on substantive and methodological work, as well as software updates, and information on estimation of time-series, cross-sectional spatial models.

Acceptable/Fair condition. Book is worn, but the pages are complete, and the text is legible. Has wear to binding and pages, may be ex-library. 2.05

Spatial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis. The text covers different modeling-related topics for continuous dependent variables, including: mapping data on spatial units, exploratory spatial data analysis, working with regression models that have spatially dependent regressors, and estimating regression models with spatially correlated error structures.  Using social sciences examples based on real data, Michael D. Ward and Kristian Skrede Gleditsch illustrate the concepts discussed, and show how to obtain and interpret relevant results. The examples are presented along with the relevant code to replicate all the analysis using the R package for statistical computing. Users can download both the data and computer code to work through all the examples found in the text. New to the Second Edition is a chapter on mapping as data exploration and its role in the research process, updates to all chapters based on substantive and methodological work, as well as software updates, and information on estimation of time-series, cross-sectional spatial models.

Book is in Used-VeryGood condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain very limited notes and highlighting. 1.85

The author, professor of psychology at San Francisco State College, starts with the assumption that human behavior is best understood in the context of ties between persons. He uses as elegant model of awareness - the Johari Window - to describe communication processes and to recognize the enigmatic nature of human interaction, the inevitability of certain unawareness, and the nature of change. Among the many topics he discusses are nonverbal interaction, influence and interaction values, entrapment and misery, the power and risk of disclosure, and the experience of feeling understood.

The natural framework of high-energy physics is relativistic quantum ?eld theory. This is a complex subject, and it is di?cult to illustrate it in all its aspects within a normal undergraduate course in particle physics, while devoting a su?cient attention to phenomenological aspects. However, in the small-wavelength limit, the semi-classical approximation is, in many cases of practical relevance, accurate enough to provide reliable predictions without entering the technicalities connected with radiative corrections. In particular, in the framework of the semi-classical approximation it is possible to obtain, in alimited number of pages,the expressionsfor relativisticcrosssections and decay rates in a self-contained and rigorous presentation, starting from the basic principles of Quantum Mechanics. Furthermore, even in the case of the standard model of Electroweak Interactions, the construction of the theory in the semi-classical approximation is exhausted by the study of the classical Lagrangian; many di?cult problems, such as those related to the unphysical content of gauge theories, can be dealt with by means of simple prescriptions. Thesearethereasonsthathavedeterminedourchoicetobasetheselecture notes on the semi-classicalapproximationto relativisticquantum ?eld theory. Webelievethatthisapproachleadstoadescriptionofthemostrelevantph- ical processes in high-energy physics, which is adequate to an undergraduate level course on fundamental interactions. Ofcourse,the lackof controlonradiativecorrectionshassomedrawbacks: forexample,theroleofanomalies,andthelimitationstotheHiggsmechanism, cannot be discussed in this context. These issues, however, are beyond the scope of the present text. During the preparation of our manuscript we have bene?ted of the inva- able help and encouragement of Raymond Stora. We are also grateful to our editor Marina Forlizzi for her continuous assistance and frieldly advices. Genova, Carlo M.