Human mobility,urban structure analysis,and spatial community detection from mobile phone data
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Human mobility,urban structure analysis,and spatial community detection from mobile phone data

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In the age of Big Data, the widespread use of location-awareness devices has made it possible to collect spatio-temporal individual trajectory datasets for analyzing human activity patterns in both ...

In the age of Big Data, the widespread use of location-awareness devices has made it possible to collect spatio-temporal individual trajectory datasets for analyzing human activity patterns in both physical space and cyberspace. Aggregation of such data can also support the urban computing studies and the understanding of urban dynamics and spatial networks. The research results can be utilized by urban managers to understand the dynamic spatial interaction patterns between different parts of the city in real-time and may guide them to conduct the optimized transportation infrastructures based on projected demand.

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Human mobility,urban structure analysis,and spatial community detection from mobile phone data Presentation Transcript

  • 1. Song Gao Email: sgao@geog.ucsb.edu University of California, Santa Barbara Human mobility, urban structure analysis, and spatial community detection from mobile phone data http://stko.geog.ucsb.edu Big Geo-Data Age
  • 2. Open Questions 1). Does distance still constricts human mobility in geographic space? 2). Whether the information communication technology (ICT) increase or decline the probability of physical movements of urban residents in daily life? 3). What is spatio-temporal patterns of phone call activities in urban space? How to explore? 4). Does the phone call interaction follow Tobler’s first law (TFL) of geography?
  • 3. Background Location Awareness Devices(Mobile Phone、GPS) Large scale spatio-temporal datasets Ratti, 2010MIT SENSEable City Lab
  • 4. Urban Computing sense city dynamics to enable a city-wide computing as so to serve people and cities. Yu Zheng (2012), Microsoft Research Asia It is emerging as a concept where sensor, device, person, vehicle, building, and street in the urban areas can be used as components to
  • 5. Background Individual Level  Human mobility (Nature, Science, PNAS)  Trajectory data mining(ACM,IJGIS)  Community Detection(Complex Networks)
  • 6. Background Aggregate (Regional Level)  Dynamic urban landscape  Spatial interactions between sub-regions  Transportation demands estimation
  • 7. Information, Communication, Technology & Space, Place & Social Community Networks Human Mobility Urban Structure Space is opportunity, Place is understood reality. • Population distribution • Movements • Mobile landscape • Functional region • Flow • ……
  • 8. Data Descriptions Mon Tue Wed Thur Fri Sat Sun 11.19 10.89 10.92 10.70 11.01 9.82 9.44 Song Gao, April, 2013 approximate 10 million records a day
  • 9. Human Mobility
  • 10. Spatio-temporal patterns can be found with a large amount of trajectories (X,Y, T) GIS visualization and analysis applied to represent and model individual dynamics Human Mobility Song Gao, April, 2013
  • 11. Geo-visualizing Space-time path Frequency of occurrence Kang C., Gao S. et al. Analyzing and Geo-visualizing Individual Human Mobility Patterns Using Mobile Call Records. 2010 Song Gao, April, 2013 Credit: Song
  • 12. The variability of mobility in space-time Regular Irregular Song Gao, April, 2013
  • 13. The distribution of the ROG covered with 869,992 mobile phone users. Radius of gyration Song Gao, April, 2013
  • 14. Urban Structure
  • 15. Aggregate approach (Hourly) --Celli (volume00, volume01, volume02,…… volume23) The scale of the urban area, may including the city and some inner suburbs, to highlight interesting metropolitan dynamics Calculate the kernel density Urban Structure Song Gao, April, 2013
  • 16. Spatio-temporal patterns AM 03-04 AM 06-07 AM 09-10 PM 15-16 PM 18-19 PM 21-22 Song Gao, April, 2013 Mobile Landscape
  • 17. LandScan Global Population Data -- 1KM resolution Song Gao, April, 2013 Correlation with population distribution
  • 18. AM 06-07 AM 09-10 PM 15-16 PM 18-19 PM 21-22 Song Gao, April, 2013 Correlation with population distribution r= 0.714 r= 0.697 r= 0.632 r= 0.748 r= 0.785
  • 19. Spatial Interaction Network
  • 20. Spatial Networks describe the networks in which the nodes are embedded in a geographical space Goal: to explore telecommunication flow in geographic space and to understand how the spatial context affect such interactions Community in spatial networks Song Gao, April, 2013
  • 21. Motivation Whether interaction structure, friendship likelihoods reveal political boundaries, physical barriers, or social divide Song Gao, April, 2013
  • 22. Spatial effects on networks (1) Spatial constraints on the distribution of nodes embedded in geographical locations; (2) Physical networks like roads and railways, which are affected by spatial topology; (3) Restrictions on long-distance links due to economic costs. Community in spatial networks Song Gao, April, 2013
  • 23. Two networks of spatial interactions  G_TeleFlow (V, E) be a weighted-undirected network graph of phone call flows where Thiessen polygons of mobile base stations are transformed into nodes (V) while interactions among stations are represented by weighted edges (E).  G_MoveFlow (V, E) be a weighted undirected network graph of human movements and let Mijt represent the total movement flow between cell i and cell j during time interval t, including movement flows both from i to j and from j to i. Song Gao, April, 2013
  • 24. Distance Decay of Spatial Interactions Cumulative probability function of distance distributions in two interaction networks: 89.47% phone-call interactions and 90.98% movements occur across distances less than 20 km Song Gao, April, 2013
  • 25. Distance Decay of Spatial Interactions the power-law fit with a decay parameter β=1.45 G_TeleFlow G_MoveFlow   dP the power-law fit with a decay parameter β=1.60 Song Gao, April, 2013
  • 26. Community Detection Algorithm The nodes of the network can be grouped into sets of nodes so that each community is densely connected internally.  Modularity maximization  Minimum-cut method  Hierarchical clustering  Girvan–Newman algorithm  Clique based methods Song Gao, April, 2013 Modularity is defined as the sum of differences between the fraction of edges falling within communities and the expected value of the same quantity under the random null model.
  • 27. Incorporating Gravity Model (Gao et al. 2013, Transactions in GIS) The fraction format of gravity-modularity for detecting communities:
  • 28. Community detection results of networks of call interaction (G_TeleFlow) Day Node Edge Number Avg Size Modularity Monday 609 41960 10 61 0.528 Tuesday 608 40902 10 61 0.533 Wednesday 609 40649 10 61 0.538 Thursday 609 56070 8 76 0.405 Friday 608 54091 8 76 0.422 Saturday 605 48673 8 75 0.438 Sunday 607 46506 8 75 0.446
  • 29. Song Gao, April, 2013 Urban Community detection results MAXID-----: 616 NUMNODES--: 609 NUMEDGES--: 41960 TOTALWT---: 934561 NUMGROUPS-: 10 MINSIZE---: 27 MEANSIZE--: 60.9 MAXSIZE---: 120 MAXQ------: 0.527837 STEP------: 599
  • 30. Examples of differentiated geographical context of isolated regions in spatial communities
  • 31. Examples of differentiated geographical context of isolated regions in spatial communities Cell A locates in the overpass intersection of ring highway and the airport expressway which is near a large residential suburb area of this city, and a high volume of call interaction make it merged to the northern spatial community (yellow) of official cells.
  • 32. Examples of differentiated geographical context of isolated regions in spatial communities Cell B has been grouped into the same distant community on Monday, Thursday and Friday, whereas it aggregates into nearby spatial adjacent community on weekends. It corresponds to a set of governmental buildings which has strong connections with eastern cells (green) of central business district on weekdays.
  • 33. Examples of differentiated geographical context of isolated regions in spatial communities Cell C has a strong link to the southern cells (red) during the whole week and they are assigned to the same community. Cell C locates nearby a industrial place which covers a wood processing plant, food brewery, and wholesale market. There may be business communications that make these cells aggregated into the same community.
  • 34. Examples of differentiated geographical context of isolated regions in spatial communities Cell D covers a local famous farm and implies a business connection to the city community. In order to identify whether physical movements also exist between these spatially separated cells, we will refer to the partition results of the network of movements.
  • 35. G_MoveFlow
  • 36. Relation between Telecommunication and Movement ICT & Mobility: -替代(Substitution) -增强(Stimulation) -缓和(Modificaiton) a causal relationship? Mon Tue Wed Thur Fri Sat Sun R2 0.857 0.852 0.852 0.848 0.852 0.857 0.865 Correlation coefficients between phone call interaction and movements Song Gao, April, 2013
  • 37. Conclusion and Discussion 1). Does distance still constricts human mobility in geographic space? -- Yes, it is. 2). Does the information communication technology (ICT) increase or decline the probability of physical movements of urban residents in daily life? – Statistically yes, but not sure whether causally 3). What is spatio-temporal patterns of phone call activities in urban space? How to explore? -- Dynamic mobile landscape 4). Does the phone call interaction follow Tobler’s first law (TFL) of geography? -- To some degree, Yes 5). A combined qualitative-quantitative framework to identify phone-call interaction patterns in spatial networks
  • 38.  Gao et al. 2013 Discovering spatial interaction communities from mobile phone data. Transactions in GIS 17(3)  Gao et al. 2013 Understanding urban traffic flow characteristics: A rethinking of betweenness centrality. Environment and Planning B: Planning and Design 40(1)  Kang et al. 2013 Inferring properties and revealing geographical impacts of inter-city mobile communication network of China using a subnet data set. International Journal of Geographical Information Science 27(3)  Kang et al. 2012 Towards Estimating Urban Population Distributions from Mobile Call Data. Journal of Urban Technology 19(4)  Kang et al. 2012 Intra-urban human mobility patterns: An urban morphology perspective. Physica A: Statistical Mechanics and its Applications 391(4)  Liu Y et al. 2012 Understanding intra-urban trip patterns from taxi trajectory data. Journal of Geographical Systems 14(4)  Liu Y et al. 2012 Urban land uses and traffic ‘source-sink areas’: Evidence from GPS- enabled taxi data in Shanghai. Landscape and Urban Planning 106 References  Yuan et al. 2012 Correlating mobile phone usage and travel behavior – a case study of Harbin, China. Computers, Environment and Urban Systems 36(2)  Walsh et al. 2011 Spatial structure and dynamics of urban communities.  Ratti et al. 2010 Redrawing the map of Great Britain from a network of human interactions. Plos One 5(12)  Guo, D. 2009 Flow Mapping and Multivariate Visualization of Large Spatial Interaction Data", IEEE Transactions on Visualization and Computer Graphics, 15(6)