spatialprobit (version 0.9-8)

Katrina: New Orleans business recovery in the aftermath of Hurricane Katrina

Description

This dataset has been used in the LeSage et al. (2011) paper entitled "New Orleans business recovery in the aftermath of Hurricane Katrina" to study the decisions of shop owners to reopen business after Hurricane Katrina. The dataset contains 673 observations on 3 streets in New Orleans and can be used to estimate the spatial probit models and to replicate the findings in the paper.

Usage

data(Katrina)

source

The raw data was obtained from the Royal Statistical Society dataset website http://www.blackwellpublishing.com/rss/Volumes/Av174p4.htm and brought to RData format.

Details

The Katrina.raw dataset contains the data found on the website before some of the variables are recoded. For example, the socio-economic status of clientele is coded as 1-5 in the raw data, but only 3 levels will be used in estimation: 1-2 = low status customers, 3 = middle, 4-5 = high status customers. Hence, with "middle" as the reference category, Katrina contains 2 dummy variables for low status customers and high status customers. The dataset Katrina is the result of these recoding operations and can be directly used for model estimation.

References

J. P. LeSage, R. K. Pace, N. Lam, R. Campanella and X. Liu (2011), New Orleans business recovery in the aftermath of Hurricane Katrina Journal of the Royal Statistical Society A, 174, 1007--1027

Examples

data(Katrina)
attach(Katrina)
table(y1) # 300 of the 673 firms reopened during 0-3 months horizon, p.1016
table(y2) # 425 of the 673 firms reopened during 0-6 months horizon, p.1016
table(y3) # 478 of the 673 firms reopened during 0-12 months horizon, p.1016
detach(Katrina)

# plot observations in New Orleans map
if (require(ggmap)) {
  qmplot(long, lat, data = Katrina, maptype="roadmap", source="google")
}

# replicate LeSage et al. (2011), Table 3, p.1017
require(spdep)
 
# (a) 0-3 months time horizon
# LeSage et al. (2011) use k=11 nearest neighbors in this case
nb <- knn2nb(knearneigh(cbind(Katrina$lat, Katrina$long), k=11))
listw <- nb2listw(nb, style="W")
W1 <- as(as_dgRMatrix_listw(listw), "CsparseMatrix")

fit1 <- sarprobit(y1 ~ flood_depth + log_medinc + small_size + large_size +
  low_status_customers +  high_status_customers + 
  owntype_sole_proprietor + owntype_national_chain, 
  W=W1, data=Katrina, ndraw=600, burn.in = 100, showProgress=TRUE)
summary(fit1)
  
# (b) 0-6 months time horizon
# LeSage et al. (2011) use k=15 nearest neighbors
nb <- knn2nb(knearneigh(cbind(Katrina$lat, Katrina$long), k=15))
listw <- nb2listw(nb, style="W")
W2 <- as(as_dgRMatrix_listw(listw), "CsparseMatrix")

fit2 <- sarprobit(y2 ~ flood_depth + log_medinc + small_size + large_size +
  low_status_customers + high_status_customers + 
  owntype_sole_proprietor + owntype_national_chain, 
  W=W2, data=Katrina, ndraw=600, burn.in = 100, showProgress=TRUE)
summary(fit2)  

# (c) 0-12 months time horizon
# LeSage et al. (2011) use k=15 nearest neighbors as in 0-6 months
W3 <- W2
fit3 <- sarprobit(y3 ~ flood_depth + log_medinc + small_size + large_size +
  low_status_customers + high_status_customers + 
  owntype_sole_proprietor + owntype_national_chain, 
  W=W3, data=Katrina, ndraw=600, burn.in = 100, showProgress=TRUE)
summary(fit3)

# replicate LeSage et al. (2011), Table 4, p.1018
# SAR probit model effects estimates for the 0-3-month time horizon
impacts(fit1)  

# replicate LeSage et al. (2011), Table 5, p.1019
# SAR probit model effects estimates for the 0-6-month time horizon
impacts(fit2)

# replicate LeSage et al. (2011), Table 6, p.1020
# SAR probit model effects estimates for the 0-12-month time horizon
impacts(fit3)