Preface: how to read this book

This book was born out of the simple conviction: that there is a right way and a wrong way to design decentralized spatial algorithms. This conviction grew from many frustrating months designing decentralized spatial algorithms in, what I now believe to be, the wrong way. Conventional centralized approaches to algorithm design cannot be reliably applied to decentralized systems because they do not account adequately for the critical features of these systems: the interaction between nodes, and the limited, local knowledge of individual system components. The issues of decentralization have long been a topic of study in the domain of distributed computing. However, advances in technology, such as geosensor networks and smart phones, are bringing these issues to the fore of spatial computing too. Like so many other books in the field of geographic information science, the key question is once again: " What's special about spatial? " More specifically, why should we study the problems of designing decentralized spatial algorithms separately from the more general problems of decentralized algorithms? The answers to this question begin in Chapter 1 (on the second page of Chapter 1), but continue throughout the book, to the final chapter. This book has a simple story to tell, and so is best read in chapter order. The chapters form a logical progression, each chapter building on the previous: Chapter 1 setting the scene; Chapter 2 specifying an abstract formal model of decentralized spatial information systems; Chapter 3 introducing our decentralized spatial algorithm design technique; Chapter 4 exploring the design of increasingly sophisticated decentralized algorithms, with minimal spatial information; Chapter 5 introducing more sophisticated spatial capabilities; Chapter 6 adding time into the mix; Chapter 7 verifying empirically the efficiency ; Chapter 8 verifying the veracity and robustness of those algorithms; and Chapter 9 finally outlining some of the important topics for further work in this area.

[1]  J. Concato,et al.  Randomized, controlled trials, observational studies, and the hierarchy of research designs. , 2000, The New England journal of medicine.

[2]  T. Kuhn.  The structure of scientific revolutions, 3rd ed. , 1996 .

[3]  Donald B. Rubin,et al.  Formal modes of statistical inference for causal effects , 1990 .

[4]  I. Hozo,et al.  At what degree of belief in a research hypothesis is a trial in humans justified? , 2002, Journal of evaluation in clinical practice.

[5]  Ron Kohavi,et al.  Ten Supplementary Analyses to Improve E-commerce Web Sites , 2003 .

[6]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[7]  T. Speed,et al.  On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9 , 1990 .

[8]  A. S. C. Ehrenberg,et al.  The Teaching of Statistics: Corrections and Comments , 1975 .

[9]  Shalom Tsur,et al.  Data Dredging , 1990, IEEE Data Eng. Bull..

[10]  E. H. Simpson,et al.  The Interpretation of Interaction in Contingency Tables , 1951 .

[11]  Morton B. Brown.  400: A Method for Combining Non-Independent, One-Sided Tests of Significance , 1975 .

[12]  B. Freedman.  Equipoise and the ethics of clinical research. , 1987, The New England journal of medicine.

[13]  R Doll,et al.  Controlled trials: the 1948 watershed , 1998, BMJ.

[14]  Stefan H. Thomke,et al.  Experimentation Matters: Unlocking the Potential of New Technologies for Innovation , 2003 .

[15]  T. Greenhalgh,et al.  Education and debate , 1997 .

[16]  D. Rubin.  Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[17]  Pierangela Samarati,et al.  Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression , 1998 .

[18]  John R. Hauser,et al.  Metrics: you are what you measure! , 1998 .

[19]  Michael G. Vann.  Of Rats, Rice, and Race: The Great Hanoi Rat Massacre, an Episode in French Colonial History , 2003 .

[20]  G. Guyatt,et al.  Users' Guides to the Medical Literature: IX. A Method for Grading Health Care Recommendations , 1995 .

[21]  Eugene S. Edgington,et al.  An Additive Method for Combining Probability Values from Independent Experiments , 1972 .

[22]  D. Campbell,et al.  Regression-Discontinuity Analysis: An Alternative to the Ex-Post Facto Experiment , 1960 .

[23]  Donald T. Campbell,et al.  Assessing the Impact of Planned Social Change* , 2010, Journal of MultiDisciplinary Evaluation.