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Infometric and statistical diagnostics to provide artificially-intelligent support for spatial analysis: the example of interpolation / C.H. Jarvis in International journal of geographical information science IJGIS, vol 17 n° 6 (september 2003)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 17 n° 6 (september 2003) . - pp 495 - 516 Titre : Infometric and statistical diagnostics to provide artificially-intelligent support for spatial analysis: the example of interpolation Type de document : Article/Communication Auteurs : C.H. Jarvis, Auteur ; N. Stuart, Auteur ; W. Cooper, Auteur Année de publication : 2003 Article en page(s) : pp 495 - 516 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] aide à la décision [Termes IGN] analyse spatiale [Termes IGN] arbre de décision [Termes IGN] diagnostic [Termes IGN] interpolation spatiale [Termes IGN] système d'information géographique Résumé : (Auteur) The wider uptake of GIS tools by many application areas outside GI science means that many newer users of GIS will have high-level knowledge of the wider task, and low-level knowledge of specific system commands as given in reference manuals. However, these newer users may not have the intermediate knowledge that experts in GI science have gained from working with GI systems over several years. Such intermediate knowledge includes an understanding of the assumptions implied by the use of certain functions, and an appreciation of how to combine functions appropriately to create a workflow that suits both the data and overall goals of the geographical analysis task. Focusing on the common but non-trivial task of interpolating spatial data, this paper considers how to help users gain the necessary knowledge to complete their task and minimise the possibility of methodological error. We observe that both infometric (or cognitive) knowledge and statistical knowledge are usually required to find a solution that jointly and efficiently meets the requirements of a particular user and data set. Using the class of interpolation methods as an example, we outline an approach that combines knowledge from multiple sources and argue the case for designing a prototype 'Intelligent' module that can sit between a user and a given GIS. The knowledge needed to assist with the task of interpolation is constructed as a network of rules, structured as a binary decision tree, that assist the user in selecting an appropriate method according to task-related knowledge (or 'purpose') and the characteristics of the data sets. The decision tree triggers exploratory diagnostics that are run on the data sets when a rule requires to be evaluated. Following evaluation of the rules, the user is advised which interpolation method might be and should not be considered for the data set. Any parameters required to interpolate the particular data set (e.g. a distance decay parameter for Inverse Distance Weighting) are also supplied through subsequent optimisation and model selection routines. The rationale of the decision process may be examined, so the 'Intelligent interpolator' also acts as a learning tool. Numéro de notice : A2003-195 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/1365881031000114099 En ligne : https://doi.org/10.1080/1365881031000114099 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22491 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-03061	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-03062	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Artificial neural networks as a tool for spatial interpolation / J.P. Rigol in International journal of geographical information science IJGIS, vol 15 n° 4 (june 2001)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 15 n° 4 (june 2001) . - pp 323 - 343 Titre : Artificial neural networks as a tool for spatial interpolation Type de document : Article/Communication Auteurs : J.P. Rigol, Auteur ; C.H. Jarvis, Auteur ; N. Stuart, Auteur Année de publication : 2001 Article en page(s) : pp 323 - 343 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] covariance [Termes IGN] interpolation [Termes IGN] krigeage [Termes IGN] modèle de simulation [Termes IGN] prédiction [Termes IGN] réseau neuronal artificiel [Termes IGN] température de l'air Résumé : (Auteur) This paper describes the spatial interpolation of daily minimum air temperature using a feedforward backpropagation neural network. Simple network configurations were trained to predict minimum temperature using as inputs: (1) date and terrain variables; (2) temperature observations at a number of neighbouring locations; (3) date, terrain variables and neighbouring temperature observations. This is the first time that trend and spatial association are explicitly considered together when interpolating using a neural network. The internal weights given to different inputs to the network were analysed to estimate the degree of spatial correlation between neighbouring stations in addition to the most influential variables contributing to the underlying trend. The spatial distribution of daily minimum temperature was estimated with the greatest accuracy by a network trained on the most comprehensive data set (3). The best model for the prediction of temperature accounts for 93% of the variance, measured by the correlation between independent estimated and observed values over a full year. This is comparable to accuracies reported in the literature using other approaches such as ordinary kriging of the residuals of multi-variate linear regression or partial thin plate splines. An advantage of this method is that the guiding variables are not assumed necessarily to be linearly related with the data being interpolated, and combinative effects are taken into account. Analysis of the internal network weights confirms that the networks are able to select adaptively between trend and covariance components of the interpolation function. Example interpolated daily minimum temperature surfaces for a 100 km x 100 km area in Yorkshire, UK, were generated using the selected network architectures to illustrate the results achievable with an ANN. Numéro de notice : A2001-041 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/13658810110038951 En ligne : https://doi.org/10.1080/13658810110038951 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21743 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-01041	RAB	Revue	Centre de documentation	En réserve L003	Disponible