LEADER 01220nam0-2200385---450 001 990005626820203316 005 20190403124935.0 035 $a000562682 035 $aUSA01000562682 035 $a(ALEPH)000562682USA01 035 $a000562682 100 $a20021105e19991949|||y0itaa50------ba 101 $afre 102 $afr 105 $a0 00||| 200 1 $a<> imitation dans l'interpsychologie de Tarde et ses prolongements$fJ. Vuillemin 205 $a[Paris : s.n.$b1999] 210 $ap. 421-449$a21 cm 215 $aEstratto da: Journal de psychologie, 1949 423 $1001SA0007327$1101$afre$12001$aJournal de psychologie, 1949. 606 $aTARDE, GABRIEL$xPSICOLOGIA SOCIALE$xTEMA DELL'IMITAZIONE$2F 620 $dPARIS 676 $a302 700 1$aVUILLEMIN,$bJules$051141 801 0$aIT$bSA$c20111219 912 $a990005626820203316 950 0$aDipar.to di Filosofia - Salerno$dDFDOC 302 VUI$e1313 FIL 951 $aDOC 302 VUI$b1313 FIL 959 $aBK 969 $aFIL 979 $c20121027$lUSA01$h1525 979 $c20121027$lUSA01$h1614 996 $aImitation dans l'interpsychologie de Tarde et ses prolongements$91134308 997 $aUNISA NUM $aSA0006347 LEADER 05624nam 2200769 a 450 001 9910877575803321 005 20200520144314.0 010 $a1-280-84783-2 010 $a9786610847839 010 $a0-470-39491-9 010 $a0-470-61226-6 010 $a1-84704-620-7 035 $a(CKB)1000000000688250 035 $a(EBL)700766 035 $a(OCoLC)769341544 035 $a(SSID)ssj0000310019 035 $a(PQKBManifestationID)11234447 035 $a(PQKBTitleCode)TC0000310019 035 $a(PQKBWorkID)10283546 035 $a(PQKB)10881778 035 $a(MiAaPQ)EBC700766 035 $a(MiAaPQ)EBC275628 035 $a(Au-PeEL)EBL275628 035 $a(OCoLC)935261876 035 $a(EXLCZ)991000000000688250 100 $a20070327d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aSpatial interpolation for climate data $ethe use of GIS in climatology and meterology /$fedited by Hartwig Dobesch, Pierre Dumolard, Izabela Dyras 210 $aLondon ;$aNewport Beach, CA $cISTE$d2007 215 $a1 online resource (304 p.) 225 1 $aGeographical information systems series 300 $aDescription based upon print version of record. 311 $a1-905209-70-3 320 $aIncludes bibliographical references and index. 327 $aSpatial Interpolation for Climate Data; Table of Contents; Preface; Part 1. GIS to Manage and Distribute Climate Data; Chapter 1. GIS, Climatology and Meteorology; 1.1. GIS technology and spatial data (working group 1); 1.1.1. Introduction; 1.1.2. Weather and GIS; 1.1.3. Geographical data, environmental data and weather data; 1.1.4. A GIS approach to access weather data; 1.2. Data and metadata; 1.2.1. Introduction; 1.2.2. Important datasets; 1.2.3. Metadata; 1.2.4. Open Geospatial Consortium; 1.2.5. EU strategies for data handling and standards 327 $a1.2.6. Meteorological datasets, important projects and programs1.2.7. Projects using Earth Observation satellites; 1.3. Interoperability; 1.3.1. Introduction; 1.3.2. Technology for service-oriented architectures; 1.3.3. Interoperability in GIS; 1.3.4. Open Geospatial Consortium foundation ideas; 1.3.5. Standardized geospatial Web services; 1.3.6. GIS and AS interoperability potential: data model and formats; 1.3.7. Atmospheric data model; 1.3.8. Support from GIS for atmospheric data formats; 1.4. Conclusions; 1.5. Bibliography; Chapter 2. SIGMA: A Web-based GIS for Environmental Applications 327 $a2.1. Introduction2.2. CPTEC-INPE; 2.3. SIGMA; 2.3.1. Basic functions; 2.4. Impacts of weather conditions on the economy; 2.5. Severe Weather Observation System (SOS); 2.5.1. Tracking of convective clouds; 2.5.2. Risk of lightning occurrence; 2.6. SOS interface; 2.7. Conclusions; 2.8. Acknowledgements; 2.9. Bibliography; Chapter 3. Web Mapping: Different Solutions using GIS; 3.1. Introduction; 3.2. Examples of Web mapping based on the usage of GIS technology in offline mode; 3.3. Examples of Web mapping using GIS tools in online mode; 3.4. Conclusion; 3.5. Bibliography 327 $aChapter 4. Comparison of Geostatistical and Meteorological Interpolation Methods (What is What?)4.1. Introduction; 4.2. Mathematical statistical model of spatial interpolation; 4.2.1. Statistical parameters; 4.2.2. Linear meteorological model for expected values; 4.2.3. Linear regression formula; 4.3. Geostatistical interpolation methods; 4.3.1. Ordinary kriging formula; 4.3.2. Universal kriging formula; 4.3.3. Modeling of unknown statistical parameters in geostatistics; 4.4. Meteorological interpolation; 4.4.1. Meteorological interpolation formula 327 $a4.4.2. Possibility of modeling unknown statistical parameters in meteorology4.4.3. Difference between geostatistics and meteorology; 4.5. Software and connection of topics; 4.6. Example of the MISH application; 4.7. Bibliography; Chapter 5. Uncertainty from Spatial Sampling: A Case Study in the French Alps; 5.1. Introduction; 5.2. The sample as a whole; 5.3. Looking in detail where the sample is not representative; 5.4. Summarizing the sampling uncertainty; 5.4.1. 2D simplification; 5.4.2. 3D generalization; 5.4.3. Geographic homogenous sub-regions of the sample 327 $a5.4.4. Interpolation of a climate parameter 330 $aThis title gives an authoritative look at the use of Geographical Information Systems (GIS) in climatology and meterology. GIS provides a range of strategies, from traditional methods, such as those for hydromet database analysis and management, to new developing methods. As such, this book will provide a useful reference tool in this important aspect of climatology and meterology study. 410 0$aGeographical information systems series. 606 $aClimatology$xData processing 606 $aMeteorology$xData processing 606 $aGeospatial data$xMathematical models 606 $aGeographic information systems 606 $aSpatial data infrastructures 615 0$aClimatology$xData processing. 615 0$aMeteorology$xData processing. 615 0$aGeospatial data$xMathematical models. 615 0$aGeographic information systems. 615 0$aSpatial data infrastructures. 676 $a551.60285 701 $aDobesch$b Hartwig$0912237 701 $aDumolard$b Pierre$0122244 701 $aDyras$b Izabela$0912238 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910877575803321 996 $aSpatial interpolation for climate data$92042586 997 $aUNINA