LEADER 03337nam  2200853z- 450 
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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