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Multidimensional Map Algebra: design and implementation of a spatio-temporal GIS processing language / J. Mennis in Transactions in GIS, vol 14 n° 1 (February 2010)  

Public [article]  inTransactions in GIS > vol 14 n° 1 (February 2010) . - pp 1 - 21 Titre : Multidimensional Map Algebra: design and implementation of a spatio-temporal GIS processing language Type de document : Article/Communication Auteurs : J. Mennis, Auteur Année de publication : 2010 Article en page(s) : pp 1 - 21 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes IGN] analyse de données [Termes IGN] données localisées 3D [Termes IGN] données maillées [Termes IGN] données spatiotemporelles [Termes IGN] implémentation (informatique) [Termes IGN] Java (langage de programmation) [Termes IGN] Map Algebra [Termes IGN] modèle conceptuel de données [Termes IGN] modélisation 4D [Termes IGN] SIG temporel Résumé : (Auteur) Due to the increasing volume of spatio-temporal data generated from remote sensing, sensor networks and computational simulation, there is a need for a generic, domain-independent framework for spatio-temporal data analysis. This research presents a generic set of data processing and manipulation tools for spatio-temporal raster data called multidimensional map algebra (MMA). MMA is an extension of conventional map algebra that operates not only on data that are two-dimensional in space but also on data that are: (1) one-dimensional in time; (2) both two-dimensional in space and one-dimensional in time; (3) three-dimensional in space; and (4) both three-dimensional in space and one-dimensional in time. MMA data types, neighborhoods, lags, and functions are presented, including rules for combining data types of different dimensionality within local, focal, and zonal functions. A prototype implementation in JAVA is provided as a demonstration and syntax specification for the functions. Challenges to continued development of MMA include performance and efficiency issues for processing very large multidimensional data sets. Copyright Blackwell Publishing Numéro de notice : A2010-002 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1467-9671.2009.01179.x Date de publication en ligne : 17/01/2010 En ligne : https://doi.org/10.1111/j.1467-9671.2009.01179.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30198 [article]

Mapping the results of geographically weighted regression / J. Mennis in Cartographic journal (the), vol 43 n° 2 (July 2006)  

Public [article]  inCartographic journal (the) > vol 43 n° 2 (July 2006) . - pp 171 - 179 Titre : Mapping the results of geographically weighted regression Type de document : Article/Communication Auteurs : J. Mennis, Auteur Année de publication : 2006 Article en page(s) : pp 171 - 179 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse bivariée [Termes IGN] carte choroplèthe [Termes IGN] densité de population [Termes IGN] distribution spatiale [Termes IGN] Pennsylvanie (Etats-Unis) [Termes IGN] régression géographiquement pondérée Résumé : (Auteur) Geographically weighted regression (GWR) is a local spatial statistical technique for exploring spatial nonstationarity. Previous approaches to mapping the results of GWR have primarily employed an equal step classification and sequential no-hue colour scheme for choropleth mapping of parameter estimates. This cartographic approach may hinder the exploration of spatial nonstationarity by inadequately illustrating the spatial distribution of the sign, magnitude, and significance of the influence of each explanatory variable on the dependent variable. Approaches for improving mapping of the results of GWR are illustrated using a case study analysis of population density-median home value relationships in Philadelphia, Pennsylvania, USA. These approaches employ data classification schemes informed by the (nonspatial) data distribution, diverging colour schemes, and bivariate choropleth mapping. Copyright British Cartographic Society Numéro de notice : A2006-611 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1179/000870406X114658 En ligne : https://doi.org/10.1179/000870406X114658 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28334 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
030-06021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Cubic map algebra functions for spatio-temporal analysis / J. Mennis in Cartography and Geographic Information Science, vol 32 n° 1 (January 2005)  

Public [article]  inCartography and Geographic Information Science > vol 32 n° 1 (January 2005) . - pp 17 - 32 Titre : Cubic map algebra functions for spatio-temporal analysis Type de document : Article/Communication Auteurs : J. Mennis, Auteur ; R. Viger, Auteur ; C. Dana Tomlin, Auteur Année de publication : 2005 Article en page(s) : pp 17 - 32 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] dimension temporelle [Termes IGN] données spatiotemporelles [Termes IGN] Etats-Unis [Termes IGN] flore locale [Termes IGN] Map Algebra [Termes IGN] occupation du sol [Termes IGN] système d'information géographique [Termes IGN] télédétection spatiale [Termes IGN] tessellation [Termes IGN] voxel Résumé : (Auteur) We propose an extension of map algebra to three dimensions for spatio-temporal data handling. This approach yields a new class of map algebra functions that we call "cube functions". Whereas conventional map algebra functions operate on data layers representing two-dimensional space, cube functions operate on data cubes representing two-dimensional space over a third-dimensional period of time. We describe the prototype implementation of a spatio-temporal data structure and selected cube function versions of conventional local, focal and zonal map algebra functions. The utility of cube functions is demonstrated through a case study analysing the spatio-temporal variability of remotly sensed, southeastern U.S. vegetation character over various land covers and during different El Nino/Southeastern Oscillation (ENSO) phases. Like conventional map algebra, the application of cube functions may demand significant data prepocessing when integrating diverse data sets, and are subject to limitations related to data storage and algorithm performance. Solutions to these issues include extending data compression and computing strategies for calculations on very large data volumes to spatio-temporal data handling. Numéro de notice : A2005-135 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1559/1523040053270765 En ligne : https://doi.org/10.1559/1523040053270765 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27273 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-05011	RAB	Revue	Centre de documentation	En réserve L003	Disponible