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Predicting Spatiotemporal Distribution of Transient Occupants in Urban Areas

  • Toshihiro Osaragi
  • Takeshi Hoshino
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

In order to discuss in detail the environment and urban systems, it is necessary to consider not only static physical objects like buildings, but also the spatiotemporal aspects like the distribution of population.This paper aims to construct models that describe the spatiotemporal distribution of population in urban areas. The models are composed of parameters describing the number of persons per unit floor-area of buildings, which varies according to the time fluctuation factors and the location factors, and are calibrated using a person trip survey data and GIS data.We discuss the characteristics of the spatiotemporal distribution of population and the accuracy of the models, and demonstrate that the proposed models can benefit all phases of urban planning, which include risk assessment and disaster management.

Keywords

Spatiotemporal distribution Transient occupant Disaster management Person trip survey 

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Notes

Acknowledgments

The authors would like to acknowledge the valuable comments and useful suggestions from anonymous reviewers to improve the content and clarity of the paper. This research is part of an effort supported by a Grant-in-Aid (21310105) from the Japan Ministry of Education, Culture, Sports, Science and Technology Health (MEXT) and a Labor Sciences Research Grant, Scientific Research (B).

References

  1. Aubrecht C, Köstl M, Steinnocher K (2011) Population Exposure and Impact Assessment: Benefits of Modeling Urban Land Use in Very High Spatial and Thematic Detail, Computational Vision and Medical Image Processing Computational Methods in Applied Sciences, Springer, 19, pp. 75-89.Google Scholar
  2. Aubrecht C, Steinnocher K, Hollaus M, Wagner W (2009) Integrating earth observation and GIScience for high resolution spatial and functional modeling of urban land use, Computers, Environment and Urban Systems 33, pp. 15-25.Google Scholar
  3. Bracken, I., & Martin, D. (1989) The generation of spatial population distributions from census centroid data. Environment and Planning, A/21, pp. 537–543.Google Scholar
  4. Chen, K. (1998) Correlations between census dwelling data and remotely sensed data. In Proceedings: SIRC 98 – 10th annual colloquium of the spatial information research centre. Dunedin, New Zealand.Google Scholar
  5. Chen, K. (2002) An approach to linking remotely sensed data and areal census data. International Journal of Remote Sensing, 23(1), pp. 37–48.Google Scholar
  6. Freire S, Aubrecht C (2010) Towards improved risk assessment: Mapping the spatio-temporal distribution of human exposure to earthquake hazard in the Lisbon metropolitan area, Gi4DM2010, Turin Italy.Google Scholar
  7. Osaragi T (2009) Estimating spatio-temporal distribution of railroad users and its application to disaster prevention planning, Lecture Notes in Geoinformation and Cartography, Advances in GIScience, Eds. M.Sester et al., Springer, pp. 233-250.Google Scholar
  8. Osaragi T, Shimada R (2009) Spatio-temporal distribution of automobile for disaster prevention planning, Journal of Architectural Planning and Engineering, 641, pp. 1561-1568.Google Scholar
  9. Osaragi T, Tanaka S (2011) Simulation model of individual decision making and behavior for returning home after a devastating earthquake, the 12th International Conference on Computers in Urban Planning and Urban Management.Google Scholar
  10. Sim, S. (2005). A proposed method for disaggregating census data using object-oriented image classification and GIS. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVI (Part 8/W27).Google Scholar
  11. Steinnocher, K., Weichselbaum, J., & Köstl, M. (2006) Linking remote sensing and demographic analysis in urbanised areas. In P. Hostert, A. Damm, & S. Schiefer (Eds.), Proceedings: First workshop of the EARSeL SIG on urban remote sensing. Berlin, Germany.Google Scholar
  12. Tsuji M (1981) Survey of de facto population in central commercial districts, Study on methods to estimate de facto population in small districts part 1 (in Japanese). Journal of Architectural Planning and Engineering 309, pp. 158-166.Google Scholar
  13. Tsuji M (1982) Survey of de facto population by indoor population aggregating method, Study on methods to estimate de facto population in small districts part 2 (in Japanese). Journal of Architectural Planning and Engineering 315, pp. 133-143.Google Scholar
  14. Tsuji M (1984) Estimation of de facto population by indoor population aggregating method based on employee’s data, Study on methods to estimate de facto population in small districts part 3 (in Japanese). Journal of Architectural Planning and Engineering 337, pp. 106-113.Google Scholar
  15. Tsuji M, Sahashi J (1991) Survey of the de facto population in an underground shopping mall (in Japanese). Journal of Architectural Planning and Engineering 425, pp. 37-45.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Mechanical and Environmental Informatics, Graduate School of Information Science and EngineeringTokyo Institute of TechnologyTokyoJapan

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