Population dislocation#
Description
This analysis computes population dislocation based on a particular hazard. First, housing units, with detailed characteristics (tenure, household size, occupied, or vacant) are allocated to the address points (buildings). This is done by calling the Housing unit allocation analysis. After the housing units are allocated, the hazard event defined by calling Fragility and Hazard services would determine the value loss for each structure which would be the input for the dislocation calculation. The dislocation is calculated from four probabilities of dislocation based on a random beta distribution of the four damage factors presented by Bai et al. 2009. These four damage factors correspond to value loss. The sum of the four probabilities multiplied by the four probabilities of damage states is used as the probability for dislocation. Since the process to determine which households are dislocated is probabilistic an integer value being imported to seed the random number generator determines if a household dislocates.
Additionally, the Block Group characteristics, percentages of African-American and Hispanic population are taken into account.
The output is a CSV file with dislocated households and related variables.
Contributors
Science: Walter Gillis Peacock, Nathanael Rosenheim
Implementation: Nathanael Rosenheim, Yong Wook Kim, Gowtham Naraharisetty, Michal Ondrejcek, Chen Wang , and NCSA IN-CORE Dev Team
Related publications
Lin, Y.S., Peacock, W.G., Lu, J.C., and Zhang, Y. (2008). Household dislocation algorithm 3: A logistic regression approach 08-05R. Retrieved from hrrc.arch.tamu.edu/publications/research reports/08-05R Dislocation Algorithm 3.pdf
Rosenheim, N., Guidotti, R., Gardoni, P., and Peacock, W.G. (2019). Integration of detailed household and housing unit characteristic data with critical infrastructure for post-hazard resilience modeling. Sustainable and Resilient Infrastructure DOI: 10.1080/23789689.2019.1681821
Bai, J., Hueste, M.B.D. and Gardoni, P. (2009), Probabilistic Assessment of Structural Damage due to Earthquakes for Buildings in Mid-America Journal of Structural Engineering 135(10) DOI: 10.1061/(ASCE)0733-9445(2009)135:10(1155)
Input Parameters
key name |
type |
name |
description |
---|---|---|---|
|
|
Result name |
Name of the result dataset. |
|
|
Seed |
Initial value to seed the random number generator. |
|
|
Choice Dislocation Flag |
Flag to calculate choice dislocation. |
|
|
Choice Dislocation cutoff |
Damage state Probability cutoff for choice dislocation. |
|
|
Choice Dislocation Damage State |
Damage state for choice dislocation. |
|
|
Unsafe Occupancy Flag |
Flag to calculate unsafe occupancy. |
|
|
Unsafe Occupancy cutoff |
Damage state Probability cutoff for unsafe occupancy. |
|
|
Unsafe Occupancy Damage State |
Damage state for unsafe occupancy. |
Input Datasets
key name |
type |
name |
description |
---|---|---|---|
|
|
Building damage |
A building damage dataset. |
|
Housing allocation |
A housing unit allocation dataset. |
|
|
Block group data |
A block group racial distribution dataset. |
|
|
Loss parameters |
A table with value loss beta distribution parameters. |
Output Datasets
key name |
type |
name |
description |
---|---|---|---|
|
Results |
A dataset containing results (format: CSV). |
(* required)
Execution
code snippet:
# Create population dislocatin
pop_dis = PopulationDislocation(client)
# Load input dataset
pop_dis.load_remote_input_dataset("building_dmg", building_dmg)
pop_dis.load_remote_input_dataset("housing_unit_allocation", housing_unit_alloc)
pop_dis.load_remote_input_dataset("block_group_data", bg_data)
pop_dis.load_remote_input_dataset("value_loss_param", value_loss)
# Specify the result name
result_name = "IN-CORE_1bv6_population_dislocation"
seed = 1111
# Set analysis parameters
pop_dis.set_parameter("result_name", result_name)
pop_dis.set_parameter("seed", seed)
# Run Population dislocation analysis
pop_dis.run_analysis()
Jupyter notebook: populationdislocation.ipynb