Social media and urban mobility: Using twitter to calculate home-work travel matrices

Abstract The proliferation of Big Data is beneficial to the study of mobility patterns in cities. This work investigates the use of social media as an efficient tool for urban mobility studies. In this case, the social network Twitter has been used, due to its wealth of spatial and temporal data and the possibility of accessing data free of charge. Using a database of geotagged tweets in the Madrid Metropolitan Area over a two-year period, this article describes the steps followed in the preparation and cleansing of the initial data and the visualisation of the results in Geographic Information Systems in the form of home-work matrices. The Origin-Destination matrices obtained were then compared with the official data provided by the Madrid Transport Consortium from the 2014 Synthetic Mobility Survey. The results of this comparison demonstrate that the level of precision offered by Twitter as a source of geographic information is adequate and efficient, thereby permitting a more in-depth analysis of flows between different zones of interest in the study area.

[1]  Ping Zhang,et al.  Quantifying and visualizing jobs-housing balance with big data: A case study of Shanghai , 2017 .

[2]  Olle Järv,et al.  Enhancing spatial accuracy of mobile phone data using multi-temporal dasymetric interpolation , 2017, Int. J. Geogr. Inf. Sci..

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

[4]  Javier Gutiérrez,et al.  New spatial patterns of mobility within the metropolitan area of Madrid: Towards more complex and dispersed flow networks , 2007 .

[5]  Shaowen Wang,et al.  Depicting urban boundaries from a mobility network of spatial interactions: a case study of Great Britain with geo-located Twitter data , 2017, Int. J. Geogr. Inf. Sci..

[6]  Marta C. González,et al.  The path most traveled: Travel demand estimation using big data resources , 2015, Transportation Research Part C: Emerging Technologies.

[7]  Bo Wang,et al.  Delineation of an urban agglomeration boundary based on Sina Weibo microblog ‘check-in’ data: A case study of the Yangtze River Delta , 2017 .

[8]  Hillel Bar-Gera,et al.  Evaluation of a Cellular Phone-Based System for Measurements of Traffic Speeds and Travel Times: A Case Study from Israel , 2007 .

[9]  Luis Miguel Romero Pérez,et al.  Traffic Flow Estimation Models Using Cellular Phone Data , 2012, IEEE Transactions on Intelligent Transportation Systems.

[10]  Luca Simeone,et al.  Visualizing the Data City: Social Media as a Source of Knowledge for Urban Planning and Management , 2014 .

[11]  Carlo Ratti,et al.  Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data , 2013, PloS one.

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

[13]  Yu Liu,et al.  The promises of big data and small data for travel behavior (aka human mobility) analysis , 2016, Transportation research. Part C, Emerging technologies.

[14]  Alan M. MacEachren,et al.  Geo-Located Tweets. Enhancing Mobility Maps and Capturing Cross-Border Movement , 2015, PloS one.

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

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

[17]  Enrique Frías-Martínez,et al.  Uncovering the spatial structure of mobility networks , 2015, Nature Communications.

[18]  María Henar Salas-Olmedo,et al.  The use of public spaces in a medium-sized city: from Twitter data to mobility patterns , 2017 .

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

[20]  Yao Shen,et al.  Urban function connectivity: Characterisation of functional urban streets with social media check-in data , 2016 .

[21]  Johan Wideberg,et al.  Deriving origin destination data from a mobile phone network , 2007 .

[22]  O. Järv,et al.  Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones , 2010 .

[23]  Juan Carlos García Palomares,et al.  Nuevas fuentes y retos para el estudio de la movilidad urbana , 2017 .

[24]  Pu Wang,et al.  Development of origin–destination matrices using mobile phone call data , 2014 .

[25]  A. Stefanidis,et al.  Harvesting ambient geospatial information from social media feeds , 2011, GeoJournal.

[26]  Shaowen Wang,et al.  A scalable framework for spatiotemporal analysis of location-based social media data , 2014, Comput. Environ. Urban Syst..

[27]  Antonio Lima,et al.  Interdependence and predictability of human mobility and social interactions , 2012, Pervasive Mob. Comput..

[28]  José J. Ramasco,et al.  Exploring the potential of phone call data to characterize the relationship between social network and travel behavior , 2015 .

[29]  Filipa Pajević,et al.  Catch Me if You Can: Workplace Mobility and Big Data , 2017, New Urban Geographies of the Creative and Knowledge Economies.

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

[31]  T. Rashidi,et al.  Exploring the capacity of social media data for modelling travel behaviour: Opportunities and challenges , 2017 .

[32]  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..

[33]  Paul A. Longley,et al.  Geo-temporal Twitter demographics , 2016, Int. J. Geogr. Inf. Sci..

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

[35]  Marta C. González,et al.  Origin-destination trips by purpose and time of day inferred from mobile phone data , 2015 .

[36]  Zbigniew Smoreda,et al.  Passive Mobile Phone Dataset to Construct Origin-destination Matrix: Potentials and Limitations , 2015 .

[37]  Enrique Frías-Martínez,et al.  Cross-Checking Different Sources of Mobility Information , 2014, PloS one.

[38]  Pablo Martí,et al.  Using locative social media and urban cartographies to identify and locate successful urban plazas , 2017 .