Mining Big Data for Tourist Hot Spots: Geographical Patterns of Online Footprints

Understanding the complex, and often unequal, spatiality of tourist demand in urban contexts requires other methodologies, among which the information base available online and in social networks has gained prominence. Innovation supported by Information and Communication Technologies in terms of data access and data exchange has emerged as a complementary supporting tool for the more traditional data collection techniques currently in use, particularly, in urban destinations where there is the need to more (near)real-time monitoring. The capacity to collect and analise massive amounts of data on individual and group behaviour is leading to new data-rich research approaches. This chapter addresses the potential for discovering geographical insights regarding tourists’ spatial patterns within a destination, based on the analysis of geotagged data available from two social networks.

[1]  G. Parra,et al.  Mayer Schönberger, Viktor; Cukier, Kenneth. Big Data: A Revolution That Will Transform How We Live, Work and Think. London: John Murray, 2013 , 2015 .

[2]  J. Gutiérrez,et al.  Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS , 2015 .

[3]  D. Richardson,et al.  Envisioning Landscapes, Making Worlds : Geography and the Humanities , 2011 .

[4]  J. Heeley.  Inside City Tourism: A European Perspective , 2011 .

[5]  D. Sui,et al.  Crossing the qualitative- quantitative divide II , 2013 .

[6]  M. Kosinski,et al.  Computer-based personality judgments are more accurate than those made by humans , 2015, Proceedings of the National Academy of Sciences.

[7]  Luciano Floridi,et al.  Big Data and Their Epistemological Challenge , 2012 .

[8]  Brian H. Spitzberg,et al.  Mapping social activities and concepts with social media (Twitter) and web search engines (Yahoo and Bing): a case study in 2012 US Presidential Election , 2013 .

[9]  M. Haklay.  How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets , 2010 .

[10]  Bill Franks,et al.  Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics , 2012 .

[11]  Erez Lieberman Aiden,et al.  Quantitative Analysis of Culture Using Millions of Digitized Books , 2010, Science.

[12]  Michael Dear,et al.  GeoHumanities : Art, History, Text at the Edge of Place , 2011 .

[13]  Sean P. Gorman,et al.  The danger of a big data episteme and the need to evolve geographic information systems , 2013 .

[14]  Bin Jiang,et al.  Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges , 2015, ArXiv.

[15]  Claudio Cioffi-Revilla,et al.  Introduction to Computational Social Science: Principles and Applications , 2017 .

[16]  Chen Xu,et al.  Detecting tourism destinations using scalable geospatial analysis based on cloud computing platform , 2015, Comput. Environ. Urban Syst..

[17]  Gregory Ashworth,et al.  Urban tourism research: Recent progress and current paradoxes , 2011 .

[18]  Mark Graham.  Time machines and virtual portals , 2011 .

[19]  Josep Blat,et al.  Leveraging explicitly disclosed location information to understand tourist dynamics: a case study , 2008, J. Locat. Based Serv..

[20]  Sandra González-Bailón.  Big data and the fabric of human geography , 2013 .

[21]  Ago Luberg,et al.  Sightsmap: Crowd-Sourced Popularity of the World Places , 2013, ENTER.

[22]  M. Goodchild.  Citizens as sensors: the world of volunteered geography , 2007 .

[23]  R. Kitchin,et al.  Big data and human geography , 2013 .

[24]  Yuan Jin,et al.  Spatial cyberinfrastructures, ontologies, and the humanities , 2011, Proceedings of the National Academy of Sciences.

[25]  M. Gold.  Debates in the Digital Humanities , 2012 .

[26]  Laura Díaz,et al.  Web 2.0 Broker: A standards-based service for spatio-temporal search of crowd-sourced information , 2012 .

[27]  R. Law,et al.  Social Media in Tourism and Hospitality: A Literature Review , 2013 .

[28]  Anthony Townsend,et al.  Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia , 2013 .

[29]  Evelyn Ruppert,et al.  Rethinking empirical social sciences , 2013 .

[30]  Michael F. Goodchild,et al.  The convergence of GIS and social media: challenges for GIScience , 2011, Int. J. Geogr. Inf. Sci..

[31]  Trevor M. Harris,et al.  The Spatial Humanities: GIS and the Future of Humanities Scholarship , 2010 .

[32]  Mark Graham,et al.  Geography and the future of big data, big data and the future of geography , 2013 .

[33]  Dimitrios Buhalis,et al.  Smart Tourism Destinations , 2014, ENTER.

[34]  Daniel Z. Sui,et al.  Crossing the qualitative-quantitative chasm I , 2012 .

[35]  Giuseppe Alessandro Veltri.  Big Data is not only about data: The two cultures of modelling , 2017 .

[36]  M. Goodchild,et al.  Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice , 2012 .

[37]  Marija Dalbello,et al.  A genealogy of digital humanities , 2011, J. Documentation.

[38]  R. Kitchin,et al.  Big Data, new epistemologies and paradigm shifts , 2014, Big Data Soc..

[39]  Luis Encalada,et al.  Identifying Tourist Places of Interest Based on Digital Imprints: Towards a Sustainable Smart City , 2017 .

[40]  J. Krygier,et al.  An Introduction to Critical Cartography , 2005 .

[41]  Leo Breiman,et al.  Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .

[42]  R. Landis,et al.  Inductive reasoning: The promise of big data , 2017 .

[43]  T. Cresswell.  Geographic Thought: A Critical Introduction , 2013 .

[44]  Kristian E. Markon,et al.  The Effect of Response Model Misspecification and Uncertainty on the Psychometric Properties of Estimates , 2013 .

[45]  Carlo Ratti,et al.  Geo-located Twitter as proxy for global mobility patterns , 2013, Cartography and geographic information science.

[46]  L. Manovich,et al.  Trending: The Promises and the Challenges of Big Social Data , 2012 .

[47]  Bruce L. Rhoads,et al.  Intervention: Critical physical geography. , 2014 .

[48]  H. R. Miller,et al.  The Data Avalanche is Here: Shouldn’t We Be Digging? , 2010 .

[49]  Stefan Krumm,et al.  Toward Stable Predictions of Apprentices’ Training Success , 2011 .

[50]  Slava Kisilevich,et al.  Analysis of community-contributed space- and time-referenced data (example of flickr and panoramio photos) , 2009, 2009 IEEE Symposium on Visual Analytics Science and Technology.

[51]  M. Goodchild,et al.  Data-driven geography , 2014, GeoJournal.

[52]  Ralph Schroeder,et al.  Big Data and the brave new world of social media research , 2014, Big Data Soc..

[53]  Trevor J Barnes,et al.  Big data, little history , 2013 .

[54]  Giovanni Seni,et al.  Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions , 2010, Ensemble Methods in Data Mining.

[55]  Roger Burrows,et al.  After the crisis? Big Data and the methodological challenges of empirical sociology , 2014 .

[56]  Chris Brunsdon,et al.  Geographically Weighted Regression: The Analysis of Spatially Varying Relationships , 2002 .

[57]  R. Michael Alvarez,et al.  Computational Social Science: Discovery and Prediction , 2016, Analytical Methods for Social Research.

[58]  Jianshen Chen,et al.  Bayesian Model Averaging for Propensity Score Analysis , 2014, Multivariate behavioral research.

[59]  M. Goodchild,et al.  Volunteered Geographic Information, the Exaflood, and the Growing Digital Divide , 2013 .

[60]  Michael F. Goodchild,et al.  Assuring the quality of volunteered geographic information , 2012 .

[61]  C. Vogt,et al.  Information technology in everyday and vacation contexts , 2012 .

[62]  Philippe Jacquart,et al.  On making causal claims: A review and recommendations , 2010 .

[63]  Iis P. Tussyadiah.  An Assessment of Contagion on Social Networking Sites , 2012, ENTER.

[64]  A. Kaplan,et al.  Users of the world, unite! The challenges and opportunities of Social Media , 2010 .

[65]  Piet Daas,et al.  Big Data as a Source for Official Statistics , 2015 .

[66]  L. Anselin.  Local Indicators of Spatial Association—LISA , 2010 .

[67]  R. Law,et al.  Progress in information technology and tourism management: 20 years on and 10 years after the Internet - the state of eTourism research. , 2008 .

[68]  Michael A. Nielsen,et al.  Reinventing Discovery: The New Era of Networked Science , 2011 .

[69]  Piet Daas,et al.  Official statistics and Big Data , 2014 .

[70]  Scott Tonidandel,et al.  Big Data Methods , 2018 .

[71]  Yuichi Mori,et al.  Handbook of computational statistics : concepts and methods , 2004 .

[72]  S. Halford,et al.  Big Data: Methodological Challenges and Approaches for Sociological Analysis , 2014 .

[73]  M. Goodchild.  GIScience, Geography, Form, and Process , 2004 .

[74]  M. Haklay.  Neogeography and the Delusion of Democratisation , 2013 .

[75]  D. Boyd,et al.  CRITICAL QUESTIONS FOR BIG DATA , 2012 .

[76]  Michael S. Bernstein,et al.  Designing and deploying online field experiments , 2014, WWW.

[77]  Michael F. Goodchild,et al.  The quality of big (geo)data , 2013 .