01063nam0-2200313---450-99000883274040332120100211160446.0000883274FED01000883274(Aleph)000883274FED0100088327420090330d2009----km-y0itay50------baitaITa-------101yyAzionamenti elettricievoluzione tecnologica e problematiche emergenti20. seminario interattivoraccolta degli attiBressanone (BZ) 23-24 marzo 2009Associazione nazionale azionamenti elettrici[s.l.s.n]stampa 2009NapoliLitografia Litonew279 p.ill.21 cmAzionamenti elettriciSeminari621.310 42Associazione nazionale azionamenti elettrici319478ITUNINARICAUNIMARCBK99000883274040332110 PRO 533/AS.I.DINEL10 PRO 533/BS.I.DINELDINELAzionamenti elettrici806231UNINA03397nam 2200865z- 450 991059507920332120230220(CKB)5680000000080733(oapen)https://directory.doabooks.org/handle/20.500.12854/92131(oapen)doab97461(EXLCZ)99568000000008073320202209d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierGeo Data Science for TourismBaselMDPI - Multidisciplinary Digital Publishing Institute20221 online resource (188 p.)3-0365-5029-1 3-0365-5030-5 This reprint describes the recent challenges in tourism seen from the point of view of data science. Thanks to the use of the most popular Data Science concepts, you can easily recognise trends and patterns in tourism, detect the impact of tourism on the environment, and predict future trends in tourism. This reprint starts by describing how to analyse data related to the past, then it moves on to detecting behaviours in the present, and, finally, it describes some techniques to predict future trends. By the end of the reprint, you will be able to use data science to help tourism businesses make better use of data and improve their decision making and operations..GeographybicsscResearch & information: generalbicsscA-level scenic spotsAGNES clusteringattraction imageBayesian STVC modelcellular signaling dataChinaChinese regional tourismcommunitiescorporate social responsibilityembeddingGeodetectorgeographic detectorgeographical data modeling analysisgreen hotelgreen hotel certificationheterogeneous information networkinfluencing factorsmajor tourist citiesnetwork connectionnode centralityobstacle factorsonline tourism reviewsrelatedness between attractionssocial network analysissocioeconomic and environmental driversspace-time deductionspatial distributionspatiotemporal estimation mappingspatiotemporal evolutionspatiotemporal influencing factorsspatiotemporal nonstationary regressionsports tourismtopic extractiontour route searchingtourism economic vulnerabilitytourism flowtourist attraction clusteringtourist attraction reachability space modeltrend analysistrend predictionGeographyResearch & information: generalMarchetti Andreaedt127673Lo Duca AngelicaedtMarchetti AndreaothLo Duca AngelicaothBOOK9910595079203321Geo Data Science for Tourism3035014UNINA