1. Introduction of “People flow project” --- Understanding of dynamic change about people in the city Yoshihide Sekimoto, Project Associate Prof. Center for Spatial Information Science, The University of Tokyo 1

2. Center for Spatial Information Science, the University of Tokyo History of CSIS 1988 “National cartography museum” Recommendation by the Science Council of Japan 1996 National committee for a research center for GIS 1998 CSIS established 2005 Moved to new Kashiwa campus 2006 Re-launch of CSIS as a National (inter-university) joint-research facility Spatial data sharing system Spatial data clearinghouse CSIS Catalogue service Address matching service Launch of Joint research program using spatial data platform Academic Portal of GIS GISSchool Design studio for GI Spatial data platform for joint research 2007 1998 History Enhancement of Center functions as joint facility Geospatially-enabled society

3. Needs of People flow data Big earthquake Flood disaster to underground mall Events like Festival Outdoor advertising Prevention of secondary disaster in the complex urban space Stimulation of the economy by people gathering Needs of time-based location information of many people are increasing…

4. People flow project since 2008 in CSIS introduces some technologies and results about people flow People flow project in CSIS 4 http://pflow.csis.u-tokyo.ac.jp

5. Sekimoto et al. (2011)* had proposed reconstruction method using large-scale fragmentary social survey data Existing our research 5 Reconstruction of macroscopic people flow in Central Tokyo using person trip (PT) survey data * Y. Sekimoto et al. PFLOW: Reconstruction of people flow by recycling large-scale fragmentary social survey data, IEEE Pervasive Computing, Vol.10(4) pp.27-35, 2011.

6. 3D visualization 6 3D visualization with 1-km2 mesh

7. Data source: questionnaire (e.g. Person trip data) 7 Main part of PT survey sheet (from the Tokyo Metropolitan Region Transportation Planning Commission web site "http://www.tokyo-pt.jp/data/file/tebiki.pdf")

8. Spatio-temporal interpolation from OD data XXXX 8 a) Geocoded OD of each sub trip b) Route choice along road/railway topology c) Interpolation at each 1 minute-intervals

9. Reconstruction accuracy 9

10. JICA-PT data （ Hanoi ） 10

11. JICA-PT data （ Manila ） 11

12. Project structure 12 People flow in real world PT data ・・ Observation data Observe Estimation ＋ Activity model of each people Aggregated distribution Disaggregated moving model

13. Classification of each data source 13 Real-time property Quality of sample PT data

14. Data source: Twitter 14 From Dr. Fujita in CSIS Time Tweet per hour Daily fluctuation for two weeks

15. Data source: Four square 15 http://www.weeplaces.com Four square mapping data of one person for two years in SF

16. Data source: mobile phone base station 16 Mobile Spatial Statistics （ From NTTDocomo web site: http://www.nttdocomo.co.jp/corporate/disclosure/mobile_spatial_statistics/ ） http://www.nttdocomo.co.jp/corporate/disclosure/mobile_spatial_statistics/ Operational data De-identification Aggregation Privilege Aggregated population Mobile Spatial Statistics

17. Data source: mobile phone GPS data 17 Density map from Auto-GPS data （ ZENRIN DataCom CO.,LTD. http://lab.its-mo.com/densitymap/ ）

18. Data assimilation technology using observation data Data assimilation is integration of model and observation data (based on e.g. Recursive Baysian Estimation…) 18 Red: No assimulation Blue: Assimulation Time(hour) RMSE of the number of people Total RMSE of stations between observed and estimated data Total RMSE of roads between observed and estimated data

19. Global approach (JICA-PT data + OpenStreetMap) 19 City Population (million) Survey year Number of people Number of trips Ratios of various modes of main transportation (2-wheeler/car/taxi/bus/rail) Manila9.451996231,889471,0352%/10%/25%/58%/4% Kuala Lumpur1.39199780,560218,46029%/44%/2%/23%/2% Damascus3.08199838,49081,6984%/25%/15%/56%/0% Managua1.20199824,85454,1382%/25%/4%/69%/0% Bucharest2.15199867,509143,3110%/19%/0%/27%/54% Phnom Penh1.15200018,66440,36989%/11% *1 /-/- Chengdu3.09200031,18870,19981%/10%/4%/4%/0% Belem1.78200024,04359,52915%/13%/2%/70%/0% Jakarta2.102000423,2371,083,2802%/0%/42%/56%/0% Tripoli0.3320013,6087,61535%/29%/17%/19%/0% …………… …

20. Many joint researches through “People Flow Data Set” 【 Transportation 】 # Research on improving the efficiency of urban transport systems using portable personal mobility.(iTransport Lab, Ltd.) # A simulation of tourist flow patterns in the Sendai metropolitan area using the People Flow Analysis Platform. Masayoshi Tanishita (Chuo University) # Utilization of statistical data in urban transport planning. (Ritsumeikan Asia Pacific University Department of Asian Pacific Studies) 【 spatio-temporal analysis 】 # Detection of patterns in travel routes using position information and travel times (Kobe University Graduate School of Engineering) # Development of a spatio-temporal data model for analysis of spatio-temporal behavior using GIS. (Tokyo Metropolitan University) 【 Risk analysis 】 # A model for the transmission of novel infectious diseases. (University of Tokyo Institute of Industrial Science) # An investigation of of disaster risk using GIS. (Aichi Institute of Technology Department of Environmental Engineering) 【 Personal information and security 】 # On the anonymization of personal information and its two-dimensional use (Information Grand Voyage Project). (Mitsubishi Research Institute, Inc.) 【 Environment 】 # Development of a scenario for fine spatial output and changes in land use via unified system analysis. (National Institute for Environmental Studies) 【 Marketing 】 # A study of consumer respiration models using person-trip data. (Fine Analysis, LLC) 20

21. Influenza 21 Day 3 Day 27 Day 46 Aihara & Suzuki lab in IIS, Univ. of Tokyo

22. Thank you ! sekimoto@csis.u-tokyo.ac.jp 22