In 1961 Jean Gottmann published his pioneering study of urban sprawl along the Boston-Washington corridor. The book's title soon became a household word, and its author gained worldwide acclaim for his insights into the dimensions of urbanism. Since writing "Megalopolis, " Gottmann has published more than eighty articles on the urban scene. Now, for the first time, the best of that work is available in a single volume.

"Since Megalopolis" treats urban questions from the ancient and modern worlds alike. What can today's planners learn from the ancient Greek city of Miletus? What do the shape and placement of the world's capitals tell us about their function? How large can our cities grow before suffocating in slums, pollution, and crime? Gottmann offers a hard-headed argument on the economic value of city parks--and a utopian vision of Manhattan auto traffic speeding through subway tunnels. He examines Tanaka's Tokyo and Solomon's Jerusalem--and tells why the king's wisdom did not extend to urban planning.

In an introductory essay new to this volume, Gottmann draws a lesson from an earlier megalopolis. "In antiquity, " he writes, "a great city flourished for 600 years on the small and craggy island of Delos in the Aegean sea. When circumstances excluded it from the predominant networks, it fell into ruins. Now an archaeological museum, Delos reminds us that cities are human artifacts and exist by participating in systems of relationships, not just as eagle nests."

This text develops a comprehensive theory of programming languages based on type systems and structural operational semantics. Language concepts are precisely defined by their static and dynamic semantics, presenting the essential tools both intuitively and rigorously while relying on only elementary mathematics. These tools are used to analyze and prove properties of languages and provide the framework for combining and comparing language features. The broad range of concepts includes fundamental data types such as sums and products, polymorphic and abstract types, dynamic typing, dynamic dispatch, subtyping and refinement types, symbols and dynamic classification, parallelism and cost semantics, and concurrency and distribution. The methods are directly applicable to language implementation, to the development of logics for reasoning about programs, and to the formal verification language properties such as type safety. This thoroughly revised second edition includes exercises at the end of nearly every chapter and a new chapter on type refinements.

The importance of typed languages for building robust software systems is, by now, an undisputed fact. Years of research have led to languages with richly expressive, yet easy to use, type systems for high-level programming languages. Types provide not only a conceptual framework for language designers, but also a ord positive bene ts to the programmer, principally the ability to express and enforce levels of abstraction within a program. Early compilers for typed languages followed closely the methods used for their untyped counterparts. The role of types was limited to the earliest s- ges of compilation, and they were thereafter ignored during the remainder of the translation process. More recently, however, implementors have come to - cognize the importance of types during compilation and even for object code. Several advantages of types in compilation have been noted to date: { They support self-checking by the compiler. By tracking types during c- pilation it is possible for an internal type checker to detect translation errors at an early stage, greatly facilitating compiler development. { They support certi cation of object code. By extending types to the ge- rated object code, it becomes possible for a code user to ensure the basic integrity of that code by checking its type consistency before execution. { They support optimized data representations and calling conventions, even in the presence of modularity. By passing types at compile-, link-, and even run-time, it is possible to avoid compromises of data representation imposed by untyped compilation techniques.

An introduction to dependent types, demonstrating the most beautiful aspects, one step at a time. A program's type describes its behavior. Dependent types are a first-class part of a language, and are much more powerful than other kinds of types; using just one language for types and programs allows program descriptions to be as powerful as the programs they describe. The Little Typer explains dependent types, beginning with a very small language that looks very much like Scheme and extending it to cover both programming with dependent types and using dependent types for mathematical reasoning. Readers should be familiar with the basics of a Lisp-like programming language, as presented in the first four chapters of The Little Schemer. The first five chapters of The Little Typer provide the needed tools to understand dependent types; the remaining chapters use these tools to build a bridge between mathematics and programming. Readers will learn that tools they know from programming-pairs, lists, functions, and recursion-can also capture patterns of reasoning. The Little Typer does not attempt to teach either practical programming skills or a fully rigorous approach to types. Instead, it demonstrates the most beautiful aspects as simply as possible, one step at a time.

Standard ML is a general-purpose programming language designed for large projects. This book provides a formal definition of Standard ML for the benefit of all concerned with the language, including users and implementers. Because computer programs are increasingly required to withstand rigorous analysis, it is all the more important that the language in which they are written be defined with full rigor.One purpose of a language definition is to establish a theory of meanings upon which the understanding of particular programs may rest. To properly define a programming language, it is necessary to use some form of notation other than a programming language. Given a concern for rigor, mathematical notation is an obvious choice. The authors have defined their semantic objects in mathematical notation that is completely independent of Standard ML.In defining a language one must also define the rules of evaluation precisely--that is, define what meaning results from evaluating any phrase of the language. The definition thus constitutes a formal specification for an implementation. The authors have developed enough of their theory to give sense to their rules of evaluation.The Definition of Standard ML is the essential point of reference for Standard ML. Since its publication in 1990, the implementation technology of the language has advanced enormously and the number of users has grown. The revised edition includes a number of new features, omits little-used features, and corrects mistakes of definition.