The experience developed by Ian McHarg represents the first attempt to base environmental planning on more objective methods. In particular, he supposed that the real world can be considered as a layer cake and each layer represents a sectoral analysis. This metaphor represents the fundamental of overlay mapping. At the beginning, these principles have been applied only by hand, just considering the degree of darkness, produced by layer transparency, as a negative impact. In the following years, this craftmade approach, has been adopted for data organization in Geographical Information Systems producing analyses with a high level of quality and rigour. Nowadays, great part of studies in environmental planning field have been developed using GIS. The next step relative to the simple use of geographic information in supporting environmental planning is the adoption of spatial simulation models, which can predict the evolution of phenomena. As the use of spatial information has definitely improved the quality of data sets on which basing decision-making process, the use of Geostatistics, spatial simulation and, more generally, geocomputation methods allows the possibility of basing the decision-making process on predicted future scenarios. It is very strange that a discipline such as planning which programs the territory for the future years in great part of cases is not based on simulation models. Sectoral analyses, often based on surveys, are not enough to highlight dynamics of an area. Better knowing urban and environmental changes occurred in the past, it is possible to provide better simulations to predict possible tendencies. The aim of this book is to provide an overview of the main methods and techniques adopted in the field of environmental geocomputation in order to produce a more sustainable development.

The experience developed by Ian McHarg represents the first attempt to base environmental planning on more objective methods. In particular, he supposed that the real world can be considered as a layer cake and each layer represents a sectoral analysis. This metaphor represents the fundamental of overlay mapping. At the beginning, these principles have been applied only by hand, just considering the degree of darkness, produced by layer transparency, as a negative impact. In the following years, this craftmade approach, has been adopted for data organization in Geographical Information Systems producing analyses with a high level of quality and rigour. Nowadays, great part of studies in environmental planning field have been developed using GIS. The next step relative to the simple use of geographic information in supporting environmental planning is the adoption of spatial simulation models, which can predict the evolution of phenomena. As the use of spatial information has definitely improved the quality of data sets on which basing decision-making process, the use of Geostatistics, spatial simulation and, more generally, geocomputation methods allows the possibility of basing the decision-making process on predicted future scenarios. It is very strange that a discipline such as planning which programs the territory for the future years in great part of cases is not based on simulation models. Sectoral analyses, often based on surveys, are not enough to highlight dynamics of an area. Better knowing urban and environmental changes occurred in the past, it is possible to provide better simulations to predict possible tendencies. The aim of this book is to provide an overview of the main methods and techniques adopted in the field of environmental geocomputation in order to produce a more sustainable development.

Sixteen years ago, Franklin estimated that about 80% of data contain geo-referenced information. To date, the availability of geographic data and information is growing, together with the capacity of users to operate with IT tools and instruments. Spatial data infrastructures are growing and allow a wide number of users to rely on them. This growth has not been fully coupled to an increase of knowledge to support spatial decisions. Spatial analytical techniques, geographical analysis and modelling methods are therefore required to analyse data and to facilitate the decision process at all levels. Old geographical issues can find an answer thanks to new methods and instruments, while new issues are developing, challenging researchers towards new solutions. This volume aims to contribute to the development of new techniques and methods to improve the process of knowledge acquisition. The Geocomputational expression is related to the development and the application of new theories, methods and tools in order to provide better solutions to complex geographical problems. The geocomputational analysis discussed in this volume, could be classified according to three main domains of applications; the first one related to spatial decision support system and to spatial uncertainty, the second connected to artificial intelligence, the third based on all spatial statistics techniques.

Sixteen years ago, Franklin estimated that about 80% of data contain geo-referenced information. To date, the availability of geographic data and information is growing, together with the capacity of users to operate with IT tools and instruments. Spatial data infrastructures are growing and allow a wide number of users to rely on them. This growth has not been fully coupled to an increase of knowledge to support spatial decisions. Spatial analytical techniques, geographical analysis and modelling methods are therefore required to analyse data and to facilitate the decision process at all levels. Old geographical issues can find an answer thanks to new methods and instruments, while new issues are developing, challenging researchers towards new solutions. This volume aims to contribute to the development of new techniques and methods to improve the process of knowledge acquisition. The Geocomputational expression is related to the development and the application of new theories, methods and tools in order to provide better solutions to complex geographical problems. The geocomputational analysis discussed in this volume, could be classified according to three main domains of applications; the first one related to spatial decision support system and to spatial uncertainty, the second connected to artificial intelligence, the third based on all spatial statistics techniques.