Modeling the Hourly Distribution of Population at a High Spatiotemporal Resolution Using Subway Smart Card Data: A Case Study in the Central Area of Beijing

The accurate estimation of the dynamic changes in population is a key component in effective urban planning and emergency management. We developed a model to estimate hourly dynamic changes in population at the community level based on subway smart card data. The hourly population of each community in six central districts of Beijing was calculated, followed by a study of the spatiotemporal patterns and diurnal dynamic changes of population and an exploration of the main sources and sinks of the observed human mobility. The maximum daytime population of the six central districts of Beijing was approximately 0.7 million larger than the night-time population. The administrative and commercial districts of Dongcheng and Xicheng had high values of population ratio of day to night of 1.35 and 1.22, respectively, whereas Shijingshan, a residential district, had the lowest value of 0.84. Areas with a high population ratio were mainly concentrated in Dongcheng, Xicheng, West Chaoyang, and Southeast Haidian. The daytime population distribution showed a hierarchical spatial pattern of planar centers and second scattered centers as opposed to multiple scattered centers during the night-time. This was because most people moved inward from the areas with a low–high to high–low population ratio of day to night from night-time to daytime, which can be explained by the process of commuting between residential areas and workplaces. Several distinctive phenomena (e.g., the distribution of new industrial parks, the so-called old residential areas, and colleges and universities) in the development of China are reflected by the spatiotemporal pattern of the distribution of population. The general consistency of the population ratios of day to night, population distribution, population variation of typical communities, and population mobility pattern with previous research suggests that the subway smart card data has potential in analyzing dynamic diurnal variations of urban population. This method can be easily duplicated to calculate hourly dynamic changes in population at the community level. These results can be used to estimate the potential hourly number of evacuees under different temporal scenarios of disasters and to support future urban planning in Beijing.

[1]  A. Tatem,et al.  Dynamic population mapping using mobile phone data , 2014, Proceedings of the National Academy of Sciences.

[2]  R. Sleeter,et al.  ESTIMATING DAYTIME AND NIGHTTIME POPULATION DENSITY FOR COASTAL COMMUNITES , 2006 .

[3]  Philip N Fulton ESTIMATING THE DAYTIME POPULATION WITH THE URBAN TRANSPORTATION PLANNING PACKAGE , 1984 .

[4]  Xiaolu Gao,et al.  Modeling the spatial distribution of urban population during the daytime and at night based on land use: A case study in Beijing, China , 2015, Journal of Geographical Sciences.

[5]  Krzysztof Janowicz,et al.  Where is also about time: A location-distortion model to improve reverse geocoding using behavior-driven temporal semantic signatures , 2015, Comput. Environ. Urban Syst..

[6]  Cui Chengyin,et al.  Identifying Commuting Pattern of Beijing Using Bus Smart Card Data , 2012 .

[7]  A Akkerman The urban household pattern of daytime population change , 1995, The Annals of regional science.

[8]  Shaowen Wang,et al.  Exploring Multi-Scale Spatiotemporal Twitter User Mobility Patterns with a Visual-Analytics Approach , 2016, ISPRS Int. J. Geo Inf..

[9]  Ling Yin,et al.  Understanding the Representativeness of Mobile Phone Location Data in Characterizing Human Mobility Indicators , 2017, ISPRS Int. J. Geo Inf..

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

[11]  Yu Liu,et al.  Towards Estimating Urban Population Distributions from Mobile Call Data , 2012 .

[12]  Krzysztof Janowicz,et al.  How where is when? On the regional variability and resolution of geosocial temporal signatures for points of interest , 2015, Comput. Environ. Urban Syst..

[13]  J. Harvey POPULATION ESTIMATION MODELS BASED ON INDIVIDUAL TM PIXELS , 2002 .

[14]  César A. Hidalgo,et al.  Unique in the Crowd: The privacy bounds of human mobility , 2013, Scientific Reports.

[15]  Xiangzheng Deng,et al.  Numerical Simulation of Population Distribution in China , 2003 .

[16]  S. Roddis,et al.  Construction of Daytime Activity Profiles from Household Travel Survey Data , 1998 .

[17]  Caroline O. Buckee,et al.  The impact of biases in mobile phone ownership on estimates of human mobility , 2013, Journal of The Royal Society Interface.

[18]  Ryosuke Shibasaki,et al.  Comparative Perspective of Human Behavior Patterns to Uncover Ownership Bias among Mobile Phone Users , 2016, ISPRS Int. J. Geo Inf..

[19]  G. Breese,et al.  The Daytime Population of the Central Business District of Chicago. , 1950 .

[20]  Qinghua Li,et al.  Discover Patterns and Mobility of Twitter Users - A Study of Four US College Cities , 2017, ISPRS Int. J. Geo Inf..

[21]  C. Aubrecht,et al.  Integrating population dynamics into mapping human exposure to seismic hazard , 2012 .

[22]  A. Bolignano,et al.  A dynamic urban air pollution population exposure assessment study using model and population density data derived by mobile phone traffic , 2016 .

[23]  Chien-Chih Yu,et al.  Personalized Location-Based Recommendation Services for Tour Planning in Mobile Tourism Applications , 2009, EC-Web.

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

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

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

[27]  D. Foley,et al.  THE DAILY MOVEMENT OF POPULATION INTO CENTRAL BUSINESS DISTRICTS. , 1952 .

[28]  Marc Barthelemy,et al.  Corrigendum: Influence of sociodemographic characteristics on human mobility , 2015, Scientific reports.

[29]  Christian Schneider,et al.  Spatiotemporal Patterns of Urban Human Mobility , 2013 .

[30]  Yongxi Gong,et al.  Exploring the spatiotemporal structure of dynamic urban space using metro smart card records , 2017, Comput. Environ. Urban Syst..

[31]  Liu Jinquan Fine Grid Dynamic Features of Population Distribution in Shenzhen , 2010 .

[32]  R. Ahas,et al.  Location based services—new challenges for planning and public administration? , 2005 .

[33]  Wei Guo,et al.  Analyzing Urban Human Mobility Patterns through a Thematic Model at a Finer Scale , 2016, ISPRS Int. J. Geo Inf..

[34]  Xiangzheng Deng,et al.  Surface modelling of human population distribution in China , 2005 .

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

[36]  Jiajun Liu,et al.  Understanding Human Mobility from Twitter , 2014, PloS one.

[37]  Soong Moon Kang,et al.  Structure of Urban Movements: Polycentric Activity and Entangled Hierarchical Flows , 2010, PloS one.

[38]  Tao Zhang,et al.  Understanding Spatiotemporal Patterns of Human Convergence and Divergence Using Mobile Phone Location Data , 2016, ISPRS Int. J. Geo Inf..

[39]  Caroline O. Buckee,et al.  Heterogeneous Mobile Phone Ownership and Usage Patterns in Kenya , 2012, PloS one.

[40]  Sérgio Freire Introducing a temporal component in spatial vulnerability analysis , 2012 .

[41]  Carlo Ratti,et al.  Money on the Move: Big Data of Bank Card Transactions as the New Proxy for Human Mobility Patterns and Regional Delineation. The Case of Residents and Foreign Visitors in Spain , 2014, 2014 IEEE International Congress on Big Data.

[42]  Mitchel Langford,et al.  The use of remotely sensed data for spatial disaggregation of published census population counts , 2001, IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas (Cat. No.01EX482).

[43]  Ryosuke Shibasaki,et al.  Reviews of Geospatial Information Technology and Collaborative Data Delivery for Disaster Risk Management , 2015, ISPRS Int. J. Geo Inf..

[44]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[45]  Budhendra L. Bhaduri Population Distribution During the Day , 2008, Encyclopedia of GIS.

[46]  Abraham Akkerman,et al.  The origin-destination matrix as an indicator of intrahousehold travel allocation , 2004 .

[47]  B. Bhaduri,et al.  LandScan USA: a high-resolution geospatial and temporal modeling approach for population distribution and dynamics , 2007 .

[48]  Valerie Paz-Soldan,et al.  Using GPS Technology to Quantify Human Mobility, Dynamic Contacts and Infectious Disease Dynamics in a Resource-Poor Urban Environment , 2013, PloS one.

[49]  Yinhai Wang,et al.  Uncovering urban human mobility from large scale taxi GPS data , 2015 .

[50]  C. Fischer "Urbanism as a Way of Life" , 1972 .

[51]  T. McPherson,et al.  ESTIMATING DAYTIME AND NIGHTTIME POPULATION DISTRIBUTIONS IN U.S. CITIES FOR EMERGENCY RESPONSE ACTIVITIES. , 2003 .

[52]  Donald L. Foley Urban Daytime Population: A Field for Demographic-Ecological Analysis , 1954 .

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

[54]  Caroline O. Buckee,et al.  The Use of Census Migration Data to Approximate Human Movement Patterns across Temporal Scales , 2013, PloS one.