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.
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Notes
- 1.
We do not employ a shortest-path algorithm for movement routes but use a computationally less-demanding model where agents move at each step to the not-already-visited node closest to the destination (in aerial distance; loops are removed from the path). This also represents satisficing behavior.
- 2.
A statistical area (SA) is a uniform administrative spatial unit defined by the Israeli Central Bureau of Statistics (CBS). It corresponds to a census tract and has a relatively homogenous population of roughly 3000 persons.
- 3.
This arbitrary number was chosen in order to balance between computing loads and convergence of results.
- 4.
This is also apparent in change to the average standard deviation of traffic loads (agents per meter). Over time, the average s.d. decreases by 73.58 % in relation to pre-shock state, suggesting a more even dispersal of traffic loads.
- 5.
We use an Inverse Distance Weighting procedure. The parameters used are: pixels of 10 × 10 m, 100 m search radius and 2nd order power function.
- 6.
Neighborhood is defined using a queen contingency matrix.
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Thanks to two referees for valuable comments on an early draft.
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Grinberger, A.Y., Lichter, M., Felsenstein, D. (2015). Simulating Urban Resilience: Disasters, Dynamics and (Synthetic) Data. In: Geertman, S., Ferreira, Jr., J., Goodspeed, R., Stillwell, J. (eds) Planning Support Systems and Smart Cities. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-18368-8_6
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