Crowdsourced Data Mining for Urban Activity: Review of Data Sources, Applications, and Methods

Abstract The penetration of devices integrated with location-based services and internet services has generated massive data about the everyday life of citizens and tracked their activities happeni...

[1]  Daniele Quercia,et al.  Mining Urban Deprivation from Foursquare: Implicit Crowdsourcing of City Land Use , 2014, IEEE Pervasive Computing.

[2]  Satish V. Ukkusuri,et al.  Location Contexts of User Check-Ins to Model Urban Geo Life-Style Patterns , 2015, PloS one.

[3]  Ali A. Alesheikh,et al.  Ranking the City: The Role of Location-Based Social Media Check-Ins in Collective Human Mobility Prediction , 2017, ISPRS Int. J. Geo Inf..

[4]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[5]  Cyrus Shahabi,et al.  Crowd sensing of traffic anomalies based on human mobility and social media , 2013, SIGSPATIAL/GIS.

[6]  Michael Batty,et al.  Smart Cities, Big Data , 2012 .

[7]  Ling Chen,et al.  Event detection from flickr data through wavelet-based spatial analysis , 2009, CIKM.

[8]  Soheila Ashkezari-Toussi,et al.  Emotional maps based on social networks data to analyze cities emotional structure and measure their emotional similarity , 2019, Cities.

[9]  Maxi San Miguel,et al.  Influence of sociodemographics on human mobility , 2015 .

[10]  Huan Liu,et al.  Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose , 2013, ICWSM.

[11]  Xiaolu Zhou,et al.  Analyzing and visualizing the spatial interactions between tourists and locals: A Flickr study in ten US cities , 2018 .

[12]  Bin Jiang,et al.  Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information , 2016, ISPRS Int. J. Geo Inf..

[13]  Christopher M. Danforth,et al.  The Geography of Happiness: Connecting Twitter Sentiment and Expression, Demographics, and Objective Characteristics of Place , 2013, PloS one.

[14]  Alexander Zipf,et al.  Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks , 2016, Int. J. Geogr. Inf. Sci..

[15]  Miaoyi Li,et al.  Revealing the relationship between spatio-temporal distribution of population and urban function with social media data , 2016 .

[16]  Satish V. Ukkusuri,et al.  Inferring Urban Land Use Using Large-Scale Social Media Check-in Data , 2014 .

[17]  Michael Batty,et al.  Big data, smart cities and city planning , 2013, Dialogues in human geography.

[18]  Qingquan Li,et al.  Activity identification from GPS trajectories using spatial temporal POIs' attractiveness , 2010, LBSN '10.

[19]  Xiangyu Wang,et al.  Are all cities with similar urban form or not? Redefining cities with ubiquitous points of interest and evaluating them with indicators at city and block levels in China , 2018, Int. J. Geogr. Inf. Sci..

[20]  Matthew Zook,et al.  Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb , 2013 .

[21]  Norman M. Sadeh,et al.  The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City , 2012, ICWSM.

[22]  Antti Jylhä,et al.  Design challenges in motivating change for sustainable urban mobility , 2014, Comput. Hum. Behav..

[23]  Alexander Zipf,et al.  A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management , 2015, Int. J. Geogr. Inf. Sci..

[24]  Bin Guo,et al.  Personalized Travel Package With Multi-Point-of-Interest Recommendation Based on Crowdsourced User Footprints , 2016, IEEE Transactions on Human-Machine Systems.

[25]  Xiaoping Liu,et al.  Classifying urban land use by integrating remote sensing and social media data , 2017, Int. J. Geogr. Inf. Sci..

[26]  Ingmar Weber,et al.  Using Twitter Data to Estimate the Relationship between Short-term Mobility and Long-term Migration , 2017, WebSci.

[27]  Yeran Sun,et al.  Investigation of Travel and Activity Patterns Using Location-based Social Network Data: A Case Study of Active Mobile Social Media Users , 2015, ISPRS Int. J. Geo Inf..

[28]  Yutaka Matsuo,et al.  Earthquake shakes Twitter users: real-time event detection by social sensors , 2010, WWW '10.

[29]  Uncovering urban mobility patterns and impact of spatial distribution of places on movements , 2017 .

[30]  Aditya G. Parameswaran,et al.  Challenges in Data Crowdsourcing , 2016, IEEE Transactions on Knowledge and Data Engineering.

[31]  Nicholas Jing Yuan,et al.  Indigenization of Urban Mobility , 2014, ArXiv.

[32]  Christian Schneider,et al.  Spatiotemporal Patterns of Urban Human Mobility , 2012, Journal of Statistical Physics.

[33]  Geoffrey J. D. Hewings,et al.  A Reassessment of urban structure and land-use patterns: distance to CBD or network-based? — Evidence from Chicago , 2018 .

[34]  Maximilian Walther,et al.  Geo-spatial Event Detection in the Twitter Stream , 2013, ECIR.

[35]  Angel Martín,et al.  Analysis of Twitter messages using big data tools to evaluate and locate the activity in the city of Valencia (Spain) , 2019, Cities.

[36]  Xiaoping Liu,et al.  Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method , 2017 .

[37]  Carlos Granell,et al.  Beyond data collection: Objectives and methods of research using VGI and geo-social media for disaster management , 2016, Comput. Environ. Urban Syst..

[38]  Yong Gao,et al.  Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data , 2013, PloS one.

[39]  Ying Long,et al.  Evaluating cities' vitality and identifying ghost cities in China with emerging geographical data , 2017 .

[40]  Jinhua Zhao,et al.  C-IMAGE: city cognitive mapping through geo-tagged photos , 2016 .

[41]  Andrew Hudson-Smith,et al.  Advances in Crowdsourcing: Surveys, Social Media and Geospatial Analysis: Towards a Big Data Toolkit , 2015 .

[42]  Piyushimita Thakuriah,et al.  Seeing Cities Through Big Data: Research, Methods and Applications in Urban Informatics , 2016 .

[43]  Jorge Gonçalves,et al.  Practical simulation of virtual crowds using points of interest , 2016, Comput. Environ. Urban Syst..

[44]  L. Chapman,et al.  Investigating the Emotional Responses of Individuals to Urban Green Space Using Twitter Data: A Critical Comparison of Three Different Methods of Sentiment Analysis , 2018 .

[45]  Ross Purves,et al.  Exploring place through user-generated content: Using Flickr tags to describe city cores , 2010, J. Spatial Inf. Sci..

[46]  Paul A. Longley,et al.  The geography of Twitter topics in London , 2016, Comput. Environ. Urban Syst..

[47]  A. Condeço-Melhorado,et al.  City dynamics through Twitter: Relationships between land use and spatiotemporal demographics , 2018 .

[48]  Xiao Liang,et al.  A general law of human mobility , 2015, Science China Information Sciences.

[49]  Carlo Ratti,et al.  Quantifying urban attractiveness from the distribution and density of digital footprints , 2009, Int. J. Spatial Data Infrastructures Res..

[50]  Alexander Zipf,et al.  An Advanced Systematic Literature Review on Spatiotemporal Analyses of Twitter Data , 2015, Trans. GIS.

[51]  Makarand Hastak,et al.  Social network analysis: Characteristics of online social networks after a disaster , 2018, Int. J. Inf. Manag..

[52]  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 .

[53]  Wenwen Zhang,et al.  How Should Urban Planners Be Trained to Handle Big Data , 2017 .

[54]  Xiaoping Liu,et al.  Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model , 2017, Int. J. Geogr. Inf. Sci..

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

[56]  Vanessa Frías-Martínez,et al.  Spectral clustering for sensing urban land use using Twitter activity , 2014, Engineering applications of artificial intelligence.

[57]  Matthew Zook,et al.  Social Media and the City: Rethinking Urban Socio-Spatial Inequality Using User-Generated Geographic Information , 2015 .

[58]  Kyumin Lee,et al.  Exploring Millions of Footprints in Location Sharing Services , 2011, ICWSM.

[59]  Francis Harvey,et al.  To Volunteer or to Contribute Locational Information? Towards Truth in Labeling for Crowdsourced Geographic Information , 2013 .

[60]  F. Ren,et al.  Check-in behaviour and spatio-temporal vibrancy: An exploratory analysis in Shenzhen, China , 2018, Cities.

[61]  Neha Gupta,et al.  Understanding happiness in cities using Twitter: Jobs, children, and transport , 2016, 2016 IEEE International Smart Cities Conference (ISC2).

[62]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

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

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

[65]  Xingjian Liu,et al.  Automated identification and characterization of parcels (AICP) with OpenStreetMap and Points of Interest , 2013, ArXiv.

[66]  Paul A. Longley,et al.  The Geotemporal Demographics of Twitter Usage , 2015 .

[67]  Hao Li,et al.  Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China , 2016 .

[68]  Jie Li,et al.  Rethinking big data: A review on the data quality and usage issues , 2016 .

[69]  Colin Robertson,et al.  A multi-scale approach to exploring urban places in geotagged photographs , 2015, Comput. Environ. Urban Syst..

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

[71]  Carlo Ratti,et al.  Scaling of City Attractiveness for Foreign Visitors through Big Data of Human Economical and Social Media Activity , 2015, 2015 IEEE International Congress on Big Data.

[72]  Christopher M. Danforth,et al.  Happiness and the Patterns of Life: A Study of Geolocated Tweets , 2013, Scientific Reports.

[73]  Jun Hu,et al.  What Is New in Our City? A Framework for Event Extraction Using Social Media Posts , 2015, PAKDD.

[74]  Daniele Quercia,et al.  Tracking "gross community happiness" from tweets , 2012, CSCW.

[75]  Lun Wu,et al.  Intra-Urban Human Mobility and Activity Transition: Evidence from Social Media Check-In Data , 2014, PloS one.

[76]  Christopher Farah,et al.  µ-shapes: Delineating urban neighborhoods using volunteered geographic information , 2016, J. Spatial Inf. Sci..

[77]  Haoying Han,et al.  Evaluating the effectiveness of urban growth boundaries using human mobility and activity records , 2015 .

[78]  Michael Batty,et al.  Environment and Planning B: Planning and Design , 1996 .

[79]  Alessandro Bozzon,et al.  Social Glass: A Platform for Urban Analytics and Decision-making Through Heterogeneous Social Data , 2015, WWW.

[80]  M. Goodchild,et al.  Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr , 2013 .

[81]  Itzhak Benenson,et al.  The Data Revolution: Big Data, Open Data, Data Infrastructures and their Consequences. By Rob Kitchin, London: Sage, 2014. , 2016 .

[82]  Xiang Li,et al.  Explore Spatiotemporal and Demographic Characteristics of Human Mobility via Twitter: A Case Study of Chicago , 2015, ArXiv.

[83]  Barry A. T. Brown,et al.  Data and the City , 2016, CHI.

[84]  M. Goodchild,et al.  Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice , 2012 .

[85]  Anthony Stefanidis,et al.  #Earthquake: Twitter as a Distributed Sensor System , 2013, Trans. GIS.

[86]  Howard D. White,et al.  Author cocitation: A literature measure of intellectual structure , 1981, J. Am. Soc. Inf. Sci..

[87]  Eugene Ch'ng,et al.  The new eye of smart city: Novel citizen Sentiment Analysis in Twitter , 2016, 2016 International Conference on Audio, Language and Image Processing (ICALIP).

[88]  Carlo Ratti,et al.  Urban magnetism through the lens of geo-tagged photography , 2015, EPJ Data Science.

[89]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.

[90]  Yunhuai Liu,et al.  Crowdsourcing based social media data analysis of urban emergency events , 2017, Multimedia Tools and Applications.

[91]  Yang Yue,et al.  Measurements of POI-based mixed use and their relationships with neighbourhood vibrancy , 2017, Int. J. Geogr. Inf. Sci..

[92]  Ying Long,et al.  Transformations of urban studies and planning in the big/open data era: a review , 2016 .

[93]  Krzysztof Janowicz,et al.  Extracting urban functional regions from points of interest and human activities on location‐based social networks , 2017, Trans. GIS.

[94]  Barbara Hofer,et al.  Demography of Twitter Users in the City of London: An Exploratory Spatial Data Analysis Approach , 2014, CARTOCON.

[95]  Dieter Pfoser,et al.  Crowdsourcing urban form and function , 2015, Int. J. Geogr. Inf. Sci..

[96]  Kazutoshi Sumiya,et al.  Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection , 2010, LBSN '10.

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

[98]  Xiaolu Zhou,et al.  Crowdsourcing functions of the living city from Twitter and Foursquare data , 2016 .

[99]  Jonathan I. Dingel,et al.  How Segregated Is Urban Consumption? , 2017, Journal of Political Economy.

[100]  Justin B. Hollander,et al.  Big data and shrinking cities: How twitter can help determine urban sentiments , 2017 .

[101]  Cecilia Mascolo,et al.  Exploiting Foursquare and Cellular Data to Infer User Activity in Urban Environments , 2013, 2013 IEEE 14th International Conference on Mobile Data Management.

[102]  David Stuart,et al.  The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences , 2015, Online Inf. Rev..

[103]  Carlo Ratti,et al.  Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis , 2006 .

[104]  Yu Shi,et al.  Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs , 2019, Comput. Environ. Urban Syst..

[105]  Johannes Schlesinger,et al.  Using Crowd-Sourced Data to Quantify the Complex Urban Fabric - OpenStreetMap and the Urban-Rural Index , 2015, OpenStreetMap in GIScience.

[106]  An Luo,et al.  Detecting Urban Polycentric Structure from POI Data , 2019, ISPRS Int. J. Geo Inf..

[107]  Shaowen Wang,et al.  Latent spatio-temporal activity structures: a new approach to inferring intra-urban functional regions via social media check-in data , 2016, Geo spatial Inf. Sci..

[108]  Ludo Waltman,et al.  Text mining and visualization using VOSviewer , 2011, ArXiv.

[109]  I. Dowman,et al.  Theme issue “State-of-the-art in photogrammetry, remote sensing and spatial information science” , 2016 .

[110]  M. Barthelemy,et al.  Human mobility: Models and applications , 2017, 1710.00004.

[111]  Javier Gutiérrez,et al.  Tourists' digital footprint in cities: comparing big data sources , 2017, Tourism Management.

[112]  Satish V. Ukkusuri,et al.  Urban activity pattern classification using topic models from online geo-location data , 2014 .

[113]  Francisco C. Pereira,et al.  Mining point-of-interest data from social networks for urban land use classification and disaggregation , 2015, Comput. Environ. Urban Syst..

[114]  David L. Tulloch.  Crowdsourcing geographic knowledge: volunteered geographic information (VGI) in theory and practice , 2014, Int. J. Geogr. Inf. Sci..

[115]  Qunying Huang,et al.  Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us? , 2016, Int. J. Geogr. Inf. Sci..

[116]  Vijayan Sugumaran,et al.  Participatory sensing-based semantic and spatial analysis of urban emergency events using mobile social media , 2016, EURASIP J. Wirel. Commun. Netw..

[117]  Alberto Cardoso,et al.  How Volunteered Geographic Information can be Integrated Into Emergency Management Practice? First Lessons Learned from an Urban Fire Simulation in the City of Coimbra , 2016, ISCRAM.

[118]  Cecilia Mascolo,et al.  Tracking urban activity growth globally with big location data , 2015, Royal Society Open Science.

[119]  Cecilia Mascolo,et al.  A Tale of Many Cities: Universal Patterns in Human Urban Mobility , 2011, PloS one.

[120]  Jacinto Estima,et al.  Investigating the Potential of OpenStreetMap for Land Use/Land Cover Production: A Case Study for Continental Portugal , 2015, OpenStreetMap in GIScience.

[121]  Brent J. Hecht,et al.  A Tale of Cities: Urban Biases in Volunteered Geographic Information , 2014, ICWSM.

[122]  Ramesh Raskar,et al.  Deep Learning the City: Quantifying Urban Perception at a Global Scale , 2016, ECCV.

[123]  Chaogui Kang,et al.  Social Sensing: A New Approach to Understanding Our Socioeconomic Environments , 2015 .