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10.3389/fpsyg.2021.712610 http://scihub22266oqcxt.onion/10.3389/fpsyg.2021.712610 34393952!8357985!34393952     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Front+Psychol 2021 ; 12 (ä): 712610 Nephropedia Template TP {{tp|p=34393952|t=2021. Intersection of Data Science and Smart Destinations: A Systematic Review |pdf=|usr=}}{{34393952}} gab.com Text Intersection of Data Science and Smart Destinations: A Systematic Review JO: Front Psychol http://www.kidney.de/pget.php?&tw=1&pmid=34393952 #FOAvip This systematic review adopts a formal and structured approach to review the intersection of data science and smart tourism destinations in terms of components found in previous research. The study period corresponds to 1995-2021 focusing the analysis mainly on the last years (2015-2021), identifying and characterizing the current trends on this research topic. The review comprises documentary research based on bibliometric and conceptual analysis, using the VOSviewer and SciMAT software to analyze articles from the Web of Science database. There is growing interest in this research topic, with more than 300 articles published annually. Data science technologies on which current smart destinations research is based include big data, smart data, data analytics, social media, cloud computing, the internet of things (IoT), smart card data, geographic information system (GIS) technologies, open data, artificial intelligence, and machine learning. Critical research areas for data science techniques and technologies in smart destinations are public tourism marketing, mobility-accessibility, and sustainability. Data analysis techniques and technologies face unprecedented challenges and opportunities post-coronavirus disease-2019 (COVID-19) to build on the huge amount of data and a new tourism model that is more sustainable, smarter, and safer than those previously implemented. Twit Text FOAVip Intersection of Data Science and Smart Destinations: A Systematic Review ????Front Psychol http://www.kidney.de/pget.php?&tw=1&pmid=34393952 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34393952 #FOAvip English Wikipedia <ref>{{Cite pmid|34393952}}</ref> Intersection of Data Science and Smart Destinations: A Systematic Review #MMPMID34393952 Aguirre Montero A; Lopez-Sanchez JA Front Psychol 2021[]; 12 (ä): 712610 PMID34393952show ga This systematic review adopts a formal and structured approach to review the intersection of data science and smart tourism destinations in terms of components found in previous research. The study period corresponds to 1995-2021 focusing the analysis mainly on the last years (2015-2021), identifying and characterizing the current trends on this research topic. The review comprises documentary research based on bibliometric and conceptual analysis, using the VOSviewer and SciMAT software to analyze articles from the Web of Science database. There is growing interest in this research topic, with more than 300 articles published annually. Data science technologies on which current smart destinations research is based include big data, smart data, data analytics, social media, cloud computing, the internet of things (IoT), smart card data, geographic information system (GIS) technologies, open data, artificial intelligence, and machine learning. Critical research areas for data science techniques and technologies in smart destinations are public tourism marketing, mobility-accessibility, and sustainability. Data analysis techniques and technologies face unprecedented challenges and opportunities post-coronavirus disease-2019 (COVID-19) to build on the huge amount of data and a new tourism model that is more sustainable, smarter, and safer than those previously implemented. äIntersection of Data Science and Smart Destinations: A Systematic Revi(epmc).pdf DeepDyve Pubget Overpricing