LEADER 03337nam 2200853z- 450 001 9910595079203321 005 20231214133203.0 035 $a(CKB)5680000000080733 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/92131 035 $a(EXLCZ)995680000000080733 100 $a20202209d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGeo Data Science for Tourism 210 $aBasel$cMDPI Books$d2022 215 $a1 electronic resource (188 p.) 311 $a3-0365-5029-1 311 $a3-0365-5030-5 330 $aThis 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.. 606 $aResearch & information: general$2bicssc 606 $aGeography$2bicssc 610 $agreen hotel 610 $acorporate social responsibility 610 $agreen hotel certification 610 $aChinese regional tourism 610 $asocioeconomic and environmental drivers 610 $aspatiotemporal influencing factors 610 $aspatiotemporal estimation mapping 610 $aBayesian STVC model 610 $aspatiotemporal nonstationary regression 610 $ageographical data modeling analysis 610 $asports tourism 610 $aspatial distribution 610 $ageographic detector 610 $ainfluencing factors 610 $aChina 610 $aA-level scenic spots 610 $aspatiotemporal evolution 610 $atrend analysis 610 $aGeodetector 610 $atourism economic vulnerability 610 $aobstacle factors 610 $atrend prediction 610 $amajor tourist cities 610 $atourism flow 610 $acellular signaling data 610 $asocial network analysis 610 $anetwork connection 610 $anode centrality 610 $acommunities 610 $arelatedness between attractions 610 $aonline tourism reviews 610 $aheterogeneous information network 610 $aembedding 610 $aattraction image 610 $atopic extraction 610 $aAGNES clustering 610 $atourist attraction clustering 610 $atourist attraction reachability space model 610 $aspace-time deduction 610 $atour route searching 615 7$aResearch & information: general 615 7$aGeography 700 $aMarchetti$b Andrea$4edt$0127673 702 $aLo Duca$b Angelica$4edt 702 $aMarchetti$b Andrea$4oth 702 $aLo Duca$b Angelica$4oth 906 $aBOOK 912 $a9910595079203321 996 $aGeo Data Science for Tourism$93035014 997 $aUNINA