VISUALIZING SPATIAL ENVIRONMENTAL DATA
International Journal of Agricultural and Environmental Information Systems (IJAEIS)
Special Issue On: Analysing, Modelling and Visualizing Spatial Environmental Data
Guest Editors:
Beniamino Murgante, University of Basilicata, Italy
Mikhail Kanevski, University of Lausanne, Switzerland
Antonino Marvuglia, University College Cork, Ireland
Maurizio Cellura, University of Palermo, Italy
Introduction and Objectives of The Special Issue:
In recent times the growing awareness among environmental disciplines about the importance to include spatial aspects in data analysis has led to the development and the consequent application of new methodologies for automatic processing of environmental data at mapping and classification purposes.
It is well known that the level of uncertainty in data analysis increases considerably when analyzing environmental data, due to natural systems’ complexity. In the most of the cases environmental data manifolds are noisy and nonlinear and the relations among the involved variables are often not very clear.
Large databases and long periods of environmental observation, monitoring of pollution, rare and extreme events and recent remote sensing technologies entail the use of new analytical and processing tools.
Observation only provides the necessary data sets, but a correct interpretation of the monitored phenomena requires a process of knowledge extraction from data aimed to the detection of spatial patterns and underlying relations among the measured variables. This is possible only through a careful data mining process.
New approaches can be provided, only to cite some examples, by Machine Learning (ML), which is a general and powerful field in data processing and modelling, and by Exploratory Data Analysis (EDA), which is an approach to discover underlying features contained in data. This volume will provide a good sample of the cutting-edge data analysis and modelling tools, by presenting concepts, algorithms, and real case studies from spatial environmental problems, natural hazards, natural and renewable resources, socio-economic data and other fields of application. It consists of a series of researches on spatial environmental data analysis, treatment and visualization using intelligent modelling techniques, for an environmental automatic decision-oriented treatment of data.
Recommended Topics:
Topics to be discussed in this special issue include (but are not limited to) the following:
- Artificial neural networks: multilayer perceptron, general regression neural networks, probabilistic neural networks, radial basis function networks, self-organising maps.
- Autocorrelation techniques and spatial simulation;
- Case studies: spatial classification, spatial prediction, density modeling, monitoring network optimisation, novelty detection.
- Environmental data mining.
- Integration of Spatial OLAP and Spatial data mining;
- Learning from geospatial data.
- MLA and geostatistics.
- Remote sensing: enhanced technique for remote sensed images treatment.
- Spatial Data Warehouse and Spatial OLAP;
- Spatial Decision Support Systems;
- Spatial extension of Fuzzy Set theory;
- Spatial Multicriteria Decision Analysis;
- Spatial Rough Set;
- Statistical learning theory: support vector machines, kernel methods.
Submission
To view the full guidelines for submission, click here.
All submissions and inquiries should be directed to the attention of:
Antonino Marvuglia and Beniamino Murgante
Guest Editors
Email: a.marvuglia@4c.ucc.ie and beniamino.murgante@unibas.it
