Modelling Urban Development with Geographical Information Systems and Cellular Automata

Anglický jazyk

Modelling Urban Development with Geographical Information Systems and Cellular Automata

Autor: Yan Liu

Na objednávku, dodanie 2-4 týždne

66.96 €

bežná cena: 74.40 €

O knihe

Urban development and migration from rural to urban areas are impacting prime agricultural land and natural landscapes, particularly in the less developed countries. These phenomena will persist and require serious study by those monitoring global environmental change. To address this need, various models have been devised to analyze urbanization and the physical, socioeconomic, and institutional factors impacting urban development. The most promising and rapidly developing of these paradigms take advantage of new Geographical Information System (GIS) technology. Modelling Urban Development with Geographical Information Systems and Cellular Automata presents one such cutting-edge model that is more than just predictive. It describes how the model simulates the urbanization process, and it provides theoretical context to promote understanding. Starting with a practical overview of the modelling techniques used in urban development research, the author focuses on the cellular automata model and its greatest strength - the incorporation of fuzzy set and fuzzy logic approaches through which urban development can be viewed as a spatially and temporally continuous process. Real-Life Application to Develop Future Planning Methods The text describes a landmark study underway, in which the fuzzy constrained cellular automata model has been implemented in a GIS environment to simulate urban development in Sydney, Australia. Featuring a survey of associated research and a geographical database for the Sydney simulation, this book answers many general "what if" questions for urban planners and details a new approach that they can adapt to their own testing and evaluation needs. This modeling method will provide researchers and planners with the means to not just predict population trends, but to better prepare for their consequences.