Travel analytics: Understanding how destination choice and business clusters are connected based on social media data ☆
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Kristina Lerman | Luciano Gallegos | Arthur Huang | Kristina Lerman | Arthur Huang | Luciano Gallegos
[1] Christopher M. Danforth,et al. The Geography of Happiness: Connecting Twitter Sentiment and Expression, Demographics, and Objective Characteristics of Place , 2013, PloS one.
[2] O. Järv,et al. Understanding monthly variability in human activity spaces: A twelve-month study using mobile phone call detail records , 2014 .
[3] Jie Yin,et al. Using Social Media to Enhance Emergency Situation Awareness , 2012, IEEE Intelligent Systems.
[4] Bruno Gonçalves,et al. Crowdsourcing Dialect Characterization through Twitter , 2014, PloS one.
[5] J. Granjeiro,et al. Nanometer Scale Titanium Surface Texturing Are Detected by Signaling Pathways Involving Transient FAK and Src Activations , 2014, PloS one.
[6] Norman M. Sadeh,et al. The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City , 2012, ICWSM.
[7] M. Batty. The Size, Scale, and Shape of Cities , 2008, Science.
[8] Satish V. Ukkusuri,et al. Urban activity pattern classification using topic models from online geo-location data , 2014 .
[9] M. Porter. Clusters and the new economics of competition. , 1998, Harvard business review.
[10] Joseph L. Schofer,et al. Role of Social Media in Communicating Transit Disruptions , 2014 .
[11] Stanley Lieberson,et al. Measuring Population Diversity , 1969 .
[12] Wenwen Li,et al. Using geolocated Twitter data to monitor the prevalence of healthy and unhealthy food references across the US , 2014 .
[13] X. Gabaix. Zipf's Law for Cities: An Explanation , 1999 .
[14] M. Barthelemy,et al. From mobile phone data to the spatial structure of cities , 2014, Scientific Reports.
[15] Yu Liu,et al. Pervasive location acquisition technologies: Opportunities and challenges for geospatial studies , 2012, Comput. Environ. Urban Syst..
[16] Jure Leskovec,et al. Friendship and mobility: user movement in location-based social networks , 2011, KDD.
[17] Satish V. Ukkusuri,et al. Location Contexts of User Check-Ins to Model Urban Geo Life-Style Patterns , 2015, PloS one.
[18] Carlo Ratti,et al. Understanding individual mobility patterns from urban sensing data: A mobile phone trace example , 2013 .
[19] Mizuki Morita,et al. Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter , 2011, EMNLP.
[20] Satish V. Ukkusuri,et al. Use of Social Media Data to Explore Crisis Informatics , 2014 .
[21] Yuri Queiroz Abreu Torres,et al. Digital Narratives: Mapping Contemporary Use of Urban Open Spaces through Geo-social Data , 2014 .
[22] L. Anselin. Local Indicators of Spatial Association—LISA , 2010 .
[23] Lan Mu,et al. GIS analysis of depression among Twitter users , 2015 .
[24] David M Levinson,et al. Axis of travel: Modeling non-work destination choice with GPS data , 2015 .
[25] Lun Wu,et al. Intra-Urban Human Mobility and Activity Transition: Evidence from Social Media Check-In Data , 2014, PloS one.
[26] Satish V. Ukkusuri,et al. Understanding urban human activity and mobility patterns using large-scale location-based data from online social media , 2013, UrbComp '13.
[27] Megha Agrawal,et al. Characterizing Geographic Variation in Well-Being Using Tweets , 2013, ICWSM.
[28] Mark E. J. Newman,et al. Power-Law Distributions in Empirical Data , 2007, SIAM Rev..
[29] Catherine T. Lawson,et al. A GPS/GIS method for travel mode detection in New York City , 2012, Comput. Environ. Urban Syst..
[30] Carlo Ratti,et al. Geo-located Twitter as proxy for global mobility patterns , 2013, Cartography and geographic information science.
[31] Ben Shneiderman,et al. Tweeting Apart: Applying Network Analysis to Detect Selective Exposure Clusters in Twitter , 2013 .
[32] Alexei Pozdnoukhov,et al. Temporal decomposition and semantic enrichment of mobility flows , 2013, LBSN '13.
[33] Hamed Heydari,et al. MabsBase: A Mycobacterium abscessus Genome and Annotation Database , 2013, PloS one.
[34] C. E. SHANNON,et al. A mathematical theory of communication , 1948, MOCO.
[35] Shaowen Wang,et al. A scalable framework for spatiotemporal analysis of location-based social media data , 2014, Comput. Environ. Urban Syst..
[36] Kristina Lerman,et al. Geography of Emotion: Where in a City are People Happier? , 2015, WWW.
[37] Norbert Brändle,et al. Supporting large-scale travel surveys with smartphones – A practical approach , 2014 .
[38] J. Wolf,et al. Impact of Underreporting on Mileage and Travel Time Estimates: Results from Global Positioning System-Enhanced Household Travel Survey , 2003 .
[39] Stephen Greaves,et al. Household travel surveys: Where are we going? , 2007 .
[40] Jiajun Liu,et al. Understanding Human Mobility from Twitter , 2014, PloS one.
[41] Jeffrey Nichols,et al. Where Is This Tweet From? Inferring Home Locations of Twitter Users , 2012, ICWSM.
[42] Satish V. Ukkusuri,et al. A novel transit rider satisfaction metric: Rider sentiments measured from online social media data , 2013 .
[43] Krzysztof Janowicz,et al. Can Twitter data be used to validate travel demand models , 2015 .
[44] Kyumin Lee,et al. You are where you tweet: a content-based approach to geo-locating twitter users , 2010, CIKM.
[45] Pere Colet,et al. Tweets on the Road , 2014, PloS one.
[46] Liang Liu,et al. Estimating Origin-Destination Flows Using Mobile Phone Location Data , 2011, IEEE Pervasive Computing.
[47] Moshe Ben-Akiva,et al. Future Mobility Survey , 2013 .
[48] Vanessa Frías-Martínez,et al. Spectral clustering for sensing urban land use using Twitter activity , 2014, Engineering applications of artificial intelligence.
[49] Kees Maat,et al. Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands , 2009 .
[50] Enrique Frías-Martínez,et al. Comparing and modelling land use organization in cities , 2015, Royal Society Open Science.
[51] Cecilia Mascolo,et al. An Empirical Study of Geographic User Activity Patterns in Foursquare , 2011, ICWSM.
[52] Nirajan Shiwakoti,et al. International Study of Current and Potential Social Media Applications in Unplanned Passenger Rail Disruptions , 2014 .