This book uses an international perspective and draws on a wide range of new conceptual and empirical material to examine the sources of conflict and cooperation within the different landscapes of knowledge that are driving contemporary urban change. Based on the premise that historically established systems of regulation and control are being subject to unprecedented pressures, scholars critically reflect on the changing role of planning and governance in sustainable urban development, looking at how a shift in power relations between expert and local cultures in western planning processes has blurred the traditional boundaries between public, private and voluntary sectors.

Planning Knowledge and Research discusses various elements and contributions to urban planning research to show that some seemingly disparate topics do in fact intersect and together, contribute to ways of understanding urban planning.

A compact guide to knowledge management, this book makes the subject accessible without oversimplifying it. Organizational issues like strategy and culture are discussed in the context of typical knowledge management processes. The focus is always on pointing out all the issues that need to be taken into account in order to make knowledge management a success. The book then goes on to explore the role of information technology as an enabler of knowledge management relating various technologies to the knowledge management processes, showing the reader what can, and what cannot, be achieved through technology. Throughout the book, references to lessons learned from past projects underline the arguments. Managers will find this book a valuable guide for implementing their own initiatives, while researchers and system designers will find plenty of ideas for future work.

Print version originally published: Mahwah, NJ. : L. Erlbaum, 2004.

This book provides a state of the art approach to participatory planning, and generates innovative thought in planning theory and knowledge study. The book introduces a new conceptual framework for participatory planning, one which redefines concepts that have been taken for granted for too long: those of “public participation” and “local knowledge”. It draws on the rich repertoire of public participation practices that have developed globally over the last 50 years, and investigates the following questions: Which participatory practices most effectively capture residents’ genuine spatial needs, perceptions and desires? And how can these be incorporated into actual plans? The book is based on an empirical comparative examination of the effectiveness of various participatory processes, and proposes practical solutions for public participation through two new instruments: the Practices Evaluation Tool, and the Participatory Methods Ladder. These instruments calibrate participation methods according to certain criteria, in order to improve their ability to extract local knowledge and incorporate it into planning deliverables. These new instruments correspond to and elaborate on Arnstein’s ladder - the 1969 theoretical landmark for participatory planning. Both academics and practitioners in the area of urban and regional planning will find this book to be an invaluable resource, given the way it develops both theoretical and practical cutting-edge outcomes.

Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning. Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credit assignment and describe tractable classes of problems for which optimal plans can be derived. This book discusses as well how reactive, integrated systems give rise to new requirements and opportunities for machine learning. The final chapter deals with a method for learning problem decompositions, which is based on an idealized model of efficiency for problem-reduction search. This book is a valuable resource for production managers, planners, scientists, and research workers.

"This book covers theoretical, thematic, and country-specific issues of knowledge cities to underline the growing importance of KBUD all around the world, providing substantive research on the decisive lineaments of urban development for knowledge-based production (drawing attention to new planning processes to foster such development), and worldwide best practices and case studies in the field of urban development"--Provided by publisher.