1,721,031 research outputs found
Geocomputation, Sustainability and Environmental Planning
No full textThe 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 developmen
La valutazione del rischio ambientale: un modello di analisi spaziale a criteri multipli
No full textGeocomputation and Urban Planning
No full textSixteen 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.
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
A Multi-Agent Geosimulation Infrastructure for Planning
No full textUrban planning is confronted with multifaceted complexities, related to the complex nature of phenomena, dynamics and processes it has to deal with. We argue that good tools for planning must be informed by these complexities, and therefore must have specific characteristics, in terms of modularity, flexibility, user-friendliness, generality, adaptability, computational efficiency and cost-effectiveness. In this chapter we present and try to make the case for a multi-agent geosimulation infrastructure framework called MAGI, showing how it delivers as such a tool for planning. The modelling and simulation infrastructure MAGI possesses characteristics, features and computational strategies particularly relevant for strongly geo-spatially oriented agent-based simulations. The infrastructure is composed of a development environment for building and executing simulation models, and a class library based on open source components. Differently from most of the existing tools for geosimulation, both raster and vector representation of simulated entities are allowed and managed with efficiency. This is obtained through the integration of a geometry engine implementing a core set of operations on spatial data through robust geometric algorithms, and an efficient spatial indexing strategy for moving agent
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