Dear Colleague,
I would be grateful if you would consider to submit an abstract to the session *“Learning from spatial data: representation, inference and modelling in earth and soil sciences”* on the next *EGU* (European Geosciences Union) meeting, to be held in *Vienna *(Austria), from* 17–22 April 2016*. *The deadline for the receipt of abstracts is 13 Jan 2016* 13:00 CET (submission information at http://egu2016.eu//abstract_management/how_to_submit_an_abstract.html <http://egu2016.eu/abstract_management/how_to_submit_an_abstract.html>). It is deemed important to highlight that the EGU is committed to promoting the participation of both early career scientists and established researchers from low and middle income countries who wish to present their work at the EGU General Assembly (see http://www.egu.eu/ecs/financial-support ). Please, feel free to contact me for any information about the session. The details of the session are attached below or follow the link: http://meetingorganizer.copernicus.org/EGU2016/session/20486 Sincerely, Sebastiano Trevisani Session: SSS12.11/GM2.4 *Learning from spatial data: representation, inference and modelling in earth and soil sciences* Convener: Sebastiano Trevisani ; Co-Conveners: Paulo Pereira , Jean Golay , Igor Bogunović , Marco Cavalli Abstract: Spatial and spatiotemporal data are crucial for the analysis and modelling of the processes of interest in Earth and Soil Sciences; the heterogeneity characterizing the typology and quality of available datasets coupled with the complexity of the studied phenomena require advanced mathematical and statistical methodologies in order to fully exploit the informative content at hand. The session aims to explore the challenges and potentialities of quantitative spatial data analysis and modelling in the context of Earth and Soil Sciences. Studies presenting applied mathematical approaches according to an intuitive approach and highlighting the key potentialities and limitations are particularly appreciated. The main interest is toward studies applying techniques and methodologies that make the data “talk” to us about the studied geo-environmental processes and factors; from this perspective we refers to a broad suite of mathematical and statistical techniques such as (but not limited to!): • Machine learning • Statistical learning theory • Geostatistics • Geomorphometry and other GIS related techniques for terrain analysis • Pattern analysis and recognition • Expert systems (e.g., fuzzy systems) combining expert knowledge and spatial data • Alternative techniques of representation of spatial data (e.g.. visualization, sonification, haptic devices, etc.) The session aims to discuss three key elements of spatial analysis, emphasizing the connections between spatial data and geo-environmental processes and factors: 1) Analysis of sparse (fragmentary) spatial data for mapping purposes with evaluation of spatial uncertainty 2) Analysis and representation of exhaustive spatial data at different scales and resolutions (e.g., geomorphometry, pattern recognition, etc.) 3) Spatial modelling, possibly using the results from points 1 and 2, of the physicochemical processes and aspects of interest (e.g., surface flow processes, landslides susceptibility models, landscape evolution models, ecological modelling, etc.) * Sebastiano Trevisani, Ph.D.* * Assistant Professor* *Applied and Environmental Geology* *IUAV University of Venice: www.iuav.it <http://www.iuav.it/>* *Address: Dorsoduro 2206, Venice 30123, Italy Tel: +39. 041. 257 1299Mail: strevis...@iuav.it <strevis...@iuav.it> * *"Le opinioni espresse sono riferibili esclusivamente all'autore e non * * riflettono in alcun modo una posizione ufficiale dello IUAV "* *"The views expressed are purely those of the writer and may not in any circumstances be regarded as stating an official position of the IUAV."* 2015-06-01 19:12 GMT+02:00 Mohammad Abedini <abed...@shirazu.ac.ir>: > > After sending the message, I checked the relation and noticed a few > missing terms. > > COV[Y(si),Y(sj)]=E{[Y(si)-m(si)][Y(sj)-m(sj)]}=E{[W(si)][W(sj)]}= COV[W(si > ),W(sj)] > > -- > With Best Wishes > Mohammad J. Abedini > Department of Civil and Environmental Engineering > School of Engineering, Shiraz University > Office Phone #: Direct: 0711-6474604, Ext.: 0711-(613)3132 > Cell Phone #: 09173160456 > > ------------------------------ > *Subject:* Inquiry > *From:* Mohammad Abedini <abed...@shirazu.ac.ir> > *Date:* Mon, 06/01/2015 09:38 PM > *To:* ai-geostats@jrc.it > > Dear Colleagues > > It is quite a while where our geo-mailing list is not active and we have > to delineate the source of this problem. > > Anyway, I would greatly appreciate it if I could have your comments and > assessment regarding the following issue: > > Generally speaking, any random function can be written as Y(s)=m(s)+W(s). > where m(s)=E[Y(s)]. > > 1. When m=cte independent of spatial location, then, the covariance of > Y at two spatial locations is the same as covariance of W at the same two > spatial locations. > > 2. When m is not constant, a few geostatisticians argue that > covariance of Y at two spatial locations cannot be defined and of course it > is not equal to covariance of W at the same two locations. > > 3. I am not quite convinced why covariance of Y at two spatial > locations is not defined. I am wondering if this lack of availability is at > theoretical level and/or at computational level. Assuming its availability, > look at the following mathematical manipulation: > > COV[Y(si),Y(sj)]=E{[Y(si)-m(si)][Y(sj)-(sj)]}=E{[W(si)][W(sj)]}= COV[W(si > ),Y(sj)] > > > > This implies that the covariance of Y and W is the same. > > > > Your critical assessment of the above assertion would be greatly > appreciated. > > > > -- > With Best Wishes > Mohammad J. Abedini > Department of Civil and Environmental Engineering > School of Engineering, Shiraz University > Office Phone #: Direct: 0711-6474604, Ext.: 0711-(613)3132 > Cell Phone #: 09173160456 > > >
