SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Finding Pearls in London's Oysters
16 references·14 citing·36 second hop
Finding Pearls in London's Oysters (2016)Finding Pearls in London's OystersVariability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data (2016)Variability in Regularity: Mi…Predicting traffic volumes and estimating the effects of shocks in massive transportation systems (2015)Predicting traffic volumes an…Everyday space–time geographies: using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn (2015)Everyday space–time geographi…Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing (2015)Combining smart card data and…Validating travel behavior estimated from smartcard data (2014)Validating travel behavior es…Detecting the dynamics of urban structure through spatial network analysis (2014)Detecting the dynamics of urb…Automated Inference of Linked Transit Journeys in London Using Fare-Transaction and Vehicle Location Data (2013)Automated Inference of Linked…Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile (2012)Estimation of a disaggregate …A Tale of Many Cities: Universal Patterns in Human Urban Mobility (2012)A Tale of Many Cities: Univer…Structure of Urban Movements: Polycentric Activity and Entangled Hierarchical Flows (2011)Structure of Urban Movements:…Smart card data use in public transit: A literature review (2011)Smart card data use in public…Understanding individual human mobility patterns (2009)Understanding individual huma…Eigenplaces: Segmenting Space through Digital Signatures (2009)Eigenplaces: Segmenting Space…Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network (2009)Eigenplaces: Analysing Cities…Understanding individual human mobility patterns (2008)Understanding individual huma…Analysing the Variability of Transit Users Behaviour with Smart Card Data (2006)Analysing the Variability of …Unraveling the network effects in station ridership growth patterns under metro network expansion (2025)Unraveling the network effect…Understanding the intra-day and intra-week ridership patterns of urban rail transit stations in London using a fuzzy clustering approach (2024)Understanding the intra-day a…The temporal distribution of ridership in metro stations from land-use perspective (2024)The temporal distribution of …Multi-view metro station clustering based on passenger flows: a functional data-edged network community detection approach (2023)Multi-view metro station clus…Smart and Sentient Retail High Streets (2022)Smart and Sentient Retail Hig…RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study (2022)RFM Model and K-Means Cluster…Classification of the metropolitan subway stations and spheres of influence of main commercial areas in Seoul (2022)Classification of the metropo…Visualizing Digital Traces for Sustainable Urban Management: Mapping Tourism Activity on the Virtual Public Space (2021)Visualizing Digital Traces fo…Job-worker spatial dynamics in Beijing: Insights from Smart Card Data (2018)Job-worker spatial dynamics i…EXPLORING SIMILARITIES AND VARIATIONS OF HUMAN MOBILITY PATTERNS IN THE CITY OF LONDON (2018)EXPLORING SIMILARITIES AND VA…Evaluating transit-served areas with non-traditional data: An exploratory study of Shenzhen, China (2018)Evaluating transit-served are…Challenges to representing the population from new forms of consumer data (2018)Challenges to representing th…Exploring the potential of open big data from ticketing websites to characterize travel patterns within the Chinese high-speed rail system (2017)Exploring the potential of op…Catch Me if You Can: Workplace Mobility and Big Data (2017)Catch Me if You Can: Workplac…The origin of bursts and heavy tails in human dynamics (2005)The scaling laws of human travel (2006)Power-Law Distributions in Empirical Data (2009)A survey of results on mobile phone datasets analysis (2015)Understanding Human Mobility from Twitter (2015)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)Power-law distributions in empirical data (2018)The Spatial Economy (1999)From mobile phone data to the spatial structure of cities (2014)Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data (2014)Revealing travel patterns and city structure with taxi trip data (2015)Property, Privacy, and Personal Data (2004)WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? (1970)Urban Spatial Structure (2010)Friendship and mobility (2011)Rethinking big data: A review on the data quality and usage issues (2015)Discovering the spatio-temporal impacts of built environment on metro ridership using smart card data (2019)Identifying urban spatial structure and urban vibrancy in highly dense cities using georeferenced social media data (2019)Revealing travel patterns and city structure with taxi trip dataCellular Census: Explorations in Urban Data Collection (2007)Eigenbehaviors: identifying structure in routine (2009)Does Urban Mobility Have a Daily Routine? Learning from the Aggregate Data of Mobile Networks (2010)Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome (2010)Principal Component Analysis on Spatial Data: An Overview (2012)Modelling the scaling properties of human mobility (2010)A universal model for mobility and migration patterns (2012)Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns (2017)Deciphering the spatial structure of China's megacity region: A new bay area—The Guangdong-Hong Kong-Macao Greater Bay Area in the making (2018)The Geography of Transport Systems (2020)Big data and analytics in hospitality and tourism: a systematic literature review (2021)Limits of Predictability in Human Mobility (2010)Route choice stickiness of public transport passengers: Measuring habitual bus ridership behaviour using smart card data (2017)Using mobility data as proxy for measuring urban vitality (2018)Future directions in human mobility science (2023)Mining smart card data for transit riders’ travel patterns (2013)Clustering Smart Card Data for Urban Mobility Analysis (2016)
this work references cited by second hop

References

  • 2008Understanding individual human mobility patterns6,071 cited
  • 2011Smart card data use in public transit: A literature review930 cited
  • 2012A Tale of Many Cities: Universal Patterns in Human Urban Mobility628 cited
  • 2012Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile528 cited
  • 2014Detecting the dynamics of urban structure through spatial network analysis523 cited
  • 2009Understanding individual human mobility patterns484 cited
  • 2011Structure of Urban Movements: Polycentric Activity and Entangled Hierarchical Flows457 cited
  • 2015Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing265 cited
  • 2009Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network262 cited
  • 2016Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data179 cited
  • 2015Everyday space–time geographies: using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn163 cited
  • 2013Automated Inference of Linked Transit Journeys in London Using Fare-Transaction and Vehicle Location Data163 cited
  • 2014Validating travel behavior estimated from smartcard data156 cited
  • 2009Eigenplaces: Segmenting Space through Digital Signatures

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

115 cited
  • 2015Predicting traffic volumes and estimating the effects of shocks in massive transportation systems97 cited
  • 2006Analysing the Variability of Transit Users Behaviour with Smart Card Data86 cited
  • Cited by

    • 2018Job-worker spatial dynamics in Beijing: Insights from Smart Card Data66 cited
    • 2017Exploring the potential of open big data from ticketing websites to characterize travel patterns within the Chinese high-speed rail system28 cited
    • 2017Catch Me if You Can: Workplace Mobility and Big Data25 cited
    • 2021Visualizing Digital Traces for Sustainable Urban Management: Mapping Tourism Activity on the Virtual Public Space24 cited
    • 2022Smart and Sentient Retail High Streets24 cited
    • 2022RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study21 cited
    • 2023Multi-view metro station clustering based on passenger flows: a functional data-edged network community detection approach16 cited
    • 2018Evaluating transit-served areas with non-traditional data: An exploratory study of Shenzhen, China16 cited
    • 2018Challenges to representing the population from new forms of consumer data11 cited
    • 2025Unraveling the network effects in station ridership growth patterns under metro network expansion8 cited
    • 2022Classification of the metropolitan subway stations and spheres of influence of main commercial areas in Seoul8 cited
    • 2024The temporal distribution of ridership in metro stations from land-use perspective6 cited
    • 2024Understanding the intra-day and intra-week ridership patterns of urban rail transit stations in London using a fuzzy clustering approach4 cited
    • 2018EXPLORING SIMILARITIES AND VARIATIONS OF HUMAN MOBILITY PATTERNS IN THE CITY OF LONDON4 cited