Householdlevel housing sequential recovery
Householdlevel housing sequential recovery#
Description
This analysis computes the series of household recovery states given a population dislocation dataset, a transition probability matrix (TPM) and an initial state vector.
The computation operates by segregating household units into five zones as a way of assigning social vulnerability. Using this vulnerability in conjunction with the TPM and the initial state vector, a Markov chain computation simulates most probable states to generate a stage history of housing recovery changes for each household.
The output of the computation is the history of housing recovery changes for each household unit in CSV format.
Contributors
Science: Elaina Sutley, Sara Hamideh
Implementation: Nathanael Rosenheim, Santiago NúñezCorrales, and NCSA INCORE Dev Team
Related publications
Sutley, E.J. and Hamideh, S., 2020. Postdisaster housing stages: a Markov chain approach to model sequences and duration based on social vulnerability. Risk Analysis, 40(12), pp.26752695.
Input parameters
key name 
type 
name 
description 



Result name 
Name of the result dataset. 


Seed 
Initial value to seed the random number generator to ensure replication of the Markov Chain path’ 


Time step 
A size of the analysis time step. 


Time duration 
Total duration. 
Input datasets
key name 
type 
name 
description 



Population dislocation 
Population dislocation results. 


Probability matrix 
A transition probability matrix that specifies 


Mass probability 
Initial mass probability function for stage 0 of the Markov Chain. 
Output datasets
key name 
type 
parent key 
name 
description 




Results 
A dataset containing results (format: CSV) 
(* required)
Execution
code snippet:
# Create Housing recovery sequential analysis instance
hrs = HousingRecoverySequential(client)
# Load input dataset
hrs.load_remote_input_dataset("population_dislocation_block", population_dislocation_block)
hrs.load_remote_input_dataset("tpm", tpm)
hrs.load_remote_input_dataset("initial_stage_probability", initial_stage_probability)
# Specify the result name
result_name = "INCORE_housingrecovery"
# Set analysis parameters
hrs.set_parameter("result_name", result_name)
hrs.set_parameter("seed", 1238)
hrs.set_parameter("t_delta", t_delta)
hrs.set_parameter("t_final", t_final)
# Run Housing recovery analysis
hrs.run_analysis()
full analysis: housing_household_recovery.ipynb