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Simulating Urban Resilience: Disasters, Dynamics and (Synthetic) Data

  • A. Yair GrinbergerEmail author
  • Michal Lichter
  • Daniel Felsenstein
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

An agent based (AB) simulation model of urban dynamics following a disaster is presented. Data disaggregation is used to generate ‘synthetic’ data with accurate socio-economic profiling. Entire synthetic populations are extrapolated at the building scale from survey data. This data is coupled with the AB model. The disaggregated baseline population allows for the bottom-up formulation of the behavior of an entire urban system. Agent interactions with each other and with the environment lead to change in residence and workplace, land use and house prices. The case of a hypothetical earthquake in the Jerusalem CBD is presented as an illustrative example. Dynamics are simulated for a period up to 3 years, post-disaster. Outcomes are measured in terms of global resilience measures, effects on residential and non-residential capital stock and population dynamics. The visualization of the complex outputs is illustrated using dynamic web-mapping.

Keywords

House Price Census Tract Traffic Load Residential Building Centripetal Force 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Thanks to two referees for valuable comments on an early draft.

References

  1. Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.CrossRefGoogle Scholar
  2. Axtell, R. (2000). Why Agents? On the varied motivations for agent computing in the social sciences (Working Paper 17). Washington, DC: Center on Social and Economic Dynamics. http://www2.econ.iastate.edu/tesfatsi/WhyAgents.RAxtell2000.pdf. Accessed March 1, 2015.
  3. Batty, M., Hudson-Smith, A., Milton, R., & Crooks, A. (2010). Map mashups, web 2.0 and the GIS revolution. Annals of GIS, 16(1), 1–13.CrossRefGoogle Scholar
  4. Beenstock, M., Felsenstein, D., & Ben Zeev, N. (2011). Capital deepening and regional inequality: An empirical analysis. Annals of Regional Science, 47(3), 599–617.CrossRefGoogle Scholar
  5. Campanella, T. J. (2008). Urban resilience and the recovery of New Orleans. Journal of the American Planning Association, 72(2), 141–146.CrossRefGoogle Scholar
  6. Carenno, M. L., Cardona, O. D., & Barbat, A. H. (2012). New methodology for urban seismic risk assessment from a holistic perspective. Bulletin of Earthquake Engineering, 10, 547–565.CrossRefGoogle Scholar
  7. Chen, Y., Li, X., Wang, S., & Liu, X. (2012). Defining agents’ behavior based on urban economic theory to simulate complex urban residential dynamics. International Journal of Geographic Information Systems, 26(7), 1155–1172.CrossRefGoogle Scholar
  8. Chen, X., & Zhan, F. B. (2008). Agent-based modeling and simulation of urban evacuation: Relative effectiveness of simultaneous and staged evacuation strategies. Journal of the Operational Research Society, 59(1), 25–33.CrossRefGoogle Scholar
  9. Crooks, A. T., & Castle, C. J. E. (2012). The Integration of agent-based modeling and geographical information for geospatial simulation. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agent-based models of geographical systems (pp. 219–251). Dordrecht: Springer.CrossRefGoogle Scholar
  10. Crooks, A. T., & Wise, S. (2013). GIS and agent based models for humanitarian assistance. Computers, Environment and Urban Systems, 41, 100–111.CrossRefGoogle Scholar
  11. Dawson, R. J., Peppe, R., & Wang, M. (2011). An agent-based model for risk-based flood incident management. Natural Hazards, 59(1), 167–189.CrossRefGoogle Scholar
  12. Folke, C., Carpenter, S., Elmqvist, T., Gunderson, L., Holling. C.S., & Walker, B. (2002). Resilience and sustainable development: building adaptive capacity in a world of transformations. AMBIO: A Journal of the Human Environment, 31(5), 437–440.Google Scholar
  13. Fujita, M., & Thisse, J. F. (2002). Economics of agglomeration: Cities, industrial location and regional growth. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  14. Godschalk, D. R. (2003). Urban hazard mitigation: creating resilient cities. Natural Hazards Review, 4(3), 136–143.CrossRefGoogle Scholar
  15. Kwan, M. P., & Lee, J. (2005). Emergency response after 9/11: The potential of real-time 3D GIS for quick emergency response in micro-spatial environments. Computers, Environment and Urban Systems, 29(2), 93–113.CrossRefGoogle Scholar
  16. Lancaster, K. J. (1966). A new approach to consumer theory. Journal of Political Economy, 74, 132–157.CrossRefGoogle Scholar
  17. Lichter, M., & Felsenstein, D. (2012). Assessing the cost of sea-level rise and extreme flooding at the local level: A GIS-based approach. Ocean and Coastal Management, 59, 47–62.CrossRefGoogle Scholar
  18. Müller, B. (2011). Urban and regional resilience—a new catchword or a consistent concept for research and practice? In B. Müller (Ed.), German annual of spatial research and policy 2010 (pp. 1–13). Berlin-Heidelberg: Springer.CrossRefGoogle Scholar
  19. Narzisi, G., Mysore, V., Rekow, D., Triola, M., Halcomb, L., Portelli, I., et al. (2006). Complexities, catastrophes and cities: unraveling emergency dynamics. In H. Schärfe, P. Hitzler, & P. Øhrstrøm (Eds.) International conference on complex systems, Boston, MA, June 2006 (Vol. 4068). Lecture notes in computer science (Lecture notes in artificial intelligence). Berlin, Heidelberg: Springer.Google Scholar
  20. Oliveira, M. G. S., Vovsha, P., Wolf, J., Birotker, Y., Givon, D., & Paasche, J. (2011). Global positioning system-assisted prompted recall household travel survey to support development of advanced travel model in Jerusalem, Israel. Transportation Research Record: Journal of the Transportation Research Board, 2246(1), 16–23.CrossRefGoogle Scholar
  21. Olshanky, R. B., Hopkins, L. D., & Johnson, L. (2012). Disaster and recovery: Processes compressed in time. Natural Hazards Review, 13(3), 173–178.CrossRefGoogle Scholar
  22. Prasad, N., Ranghieri, F., Shah, F., Trohanis, Z., Kessler, E., & Sinha, R. (2009). Climate resilient cities: A primer on reducing vulnerabilities to disasters. Washington, DC: World Bank publications.Google Scholar
  23. Reichert, P., & Mieleitner, J. (2009). Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time-dependent parameters. Water Resources Research, 45(10), W10402. doi: 10.1029/2009WR007814.CrossRefGoogle Scholar
  24. Rose, A. (2009). Economic resilience to disasters (Community and Regional Resilience Research Report 8). Oak Ridge, TN: Oak Ridge National Laboratory. http://research.create.usc.edu/published_papers/75. Accessed March 1, 2015.
  25. Salamon, A., Katz, O., & Crouvi, O. (2010). Zones of required investigation for earthquake-related hazards in Jerusalem. Natural Hazards, 53(2), 375–406.CrossRefGoogle Scholar
  26. Schelling, T. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1(2), 143–186.CrossRefGoogle Scholar
  27. Simon, H. (1952). A behavioral model of rational choice. Quarterly Journal of Economics, 69(1), 99–118.CrossRefGoogle Scholar
  28. Stein, R. M., Duenas-Osorio, L., & Subramanian, D. (2010). Who evacuates when hurricanes approach? The role of risk, information and location. Social Science Quarterly, 91(3), 816–834.CrossRefGoogle Scholar
  29. UNISDR (United Nations International Strategy for Disaster Reduction). (2012). How to make cities more resilient—a handbook for local government leaders. Geneva, Switzerland: UNISDR.Google Scholar
  30. Whitehead, J. C., Edwards, B., Van Willigen, M., Maiolo, J., Wilson, K., & Smith, K. T. (2000). Heading for higher ground: Factors affecting real and hypothetical hurricane evacuation behavior. Environmental Hazards, 2, 133–142.CrossRefGoogle Scholar
  31. Zimmerman, B., Nawn, D., Wang, Y., Kuhlman, B., Sochats, K., Luangesorn, L., et al. (2010). Dynamic model generation for agent-based emergency response simulation. ESRI International User Conference, Center for National Preparedness, University of Pittsburgh.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • A. Yair Grinberger
    • 1
    Email author
  • Michal Lichter
    • 1
  • Daniel Felsenstein
    • 1
  1. 1.The Hebrew University of JerusalemJerusalemIsrael

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