New Orleans Business Recovery in the Aftermath of Hurricane Katrina

LeSage, James P.; Pace, R. Kelley; Lam, Nina Siu‐Ngan; Campanella, Richard; Liu, Xingjian

doi:10.1111/j.1467-985x.2011.00712.x

Cited by 137 publications

(135 citation statements)

References 17 publications

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“…LeSage and Pace [13] and LeSage et al [14] discuss how changes of a covariate at location i will affect the output at location j. To measure these effects, they define direct and indirect impacts.…”

Section: Impactsmentioning

confidence: 99%

“…See LeSage and Pace [13], LeSage et al [14] for details on how the impacts and effects are derived. They also provide some computational hints on how to compute the direct and indirect effects.…”

Section: Impactsmentioning

confidence: 99%

“…LeSage et al [14] provide a Spatial Probit model to estimate the probability of re-opening a business in New Orleans after hurricane Katrina. They model the outcome y i as follows:…”

Section: Non-gaussian Modelsmentioning

confidence: 99%

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Approximate Bayesian inference for spatial econometrics models

Bivand

Gómez‐Rubio

Rue³

2014

Spatial Statistics

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In this paper we explore the use of the Integrated Laplace Approximation (INLA) for Bayesian inference in some widely used models in Spatial Econometrics. Bayesian inference often relies on computationally intensive simulation methods, such as Markov Chain Monte Carlo. When only marginal inference is needed, INLA provides a fast and accurate estimate of the posterior marginals of the parameters in the model. Furthermore, we have compared the results provided by these models to those obtained with a more general class of Generalised Linear Models with random effects. In these models, spatial autocorrelation is modelled by means of correlated Gaussian random effects.We also discuss a procedure to extend the class of models that the R-INLA software can fit. This approach is based on conditioning on one or more parameters so that the resulting models can be fitted with R-INLA across sets of values of the fixed parameters. The posterior marginals of these parameters of interest are then obtained by combining the marginal likelihoods (which are conditioned on the values of the parameters fixed) of the fitted models and a prior on these parameters. This approach can also be used to fit even more general models.Finally, we discuss the use of all these models on two datasets based on median housing prices for census tracts in Boston and the probability of business re-opening in New Orleans in the aftermath of hurricane Katrina.

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Section: Impactsmentioning

confidence: 99%

Section: Impactsmentioning

confidence: 99%

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Approximate Bayesian inference for spatial econometrics models

Bivand

Gómez‐Rubio

Rue³

2014

Spatial Statistics

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“…This region has been devastated by storm surges, floods, and hurricanes. At least five hurricanes (Katrina, Rita, Gustav, Ike, and Isaac) hit this region in the past decade (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), which caused significant loss of human lives and damages to properties [39][40][41][42][43][44][45]. The most destructive natural disaster in the U.S. history, Hurricane Katrina, crossed this region and caused severe destruction in August 2005.…”

Section: Study Areamentioning

confidence: 99%

Assessing Community Resilience to Coastal Hazards in the Lower Mississippi River Basin

Cai

Lam

Zou

et al. 2016

Water

Self Cite

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This paper presents an assessment of community resilience to coastal hazards in the Lower Mississippi River Basin (LMRB) region in southeastern Louisiana. The assessment was conducted at the census block group scale. The specific purpose of this study was to provide a quantitative method to assess and validate the community resilience to coastal hazards, and to identify the relationships between a set of socio-environmental indicators and community resilience. The Resilience Inference Measurement (RIM) model was applied to assess the resilience of the block groups. The resilience index derived was empirically validated through two statistical procedures: K-means cluster analysis of exposure, damage, and recovery variables to derive the resilience groups, and discriminant analysis to identify the key indicators of resilience. The discriminant analysis yielded a classification accuracy of 73.1%. The results show that block groups with higher resilience were concentrated generally in the northern part of the study area, including those located north of Lake Pontchartrain and in East Baton Rouge, West Baton Rouge, and Lafayette parishes. The lower-resilience communities were located mostly along the coastline and lower elevation area including block groups in southern Plaquemines Parish and Terrebonne Parish. Regression analysis between the resilience scores and the indicators extracted from the discriminant analysis suggests that community resilience was significantly linked to multicomponent capacities. The findings could help develop adaptation strategies to reduce vulnerability, increase resilience, and improve long-term sustainability for the coastal region.

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“…This may seem counterintuitive, as spillover effects would seem more similar to second-order effects that should be smaller in magnitude than would direct effects. Because the scalar summary measures of the indirect effects provided by [66] combine spatial spillovers in all other areas to produce a single numerical value for the indirect effect estimate, this is not unusual in diverse empirical applications (cf. [26]) particularly if spatial observations are large, as in the present study (1098 + 290 − 1 = 1387).…”

Section: Regression Resultsmentioning

confidence: 99%

The Impact of the Comprehensive Rural Village Development Program on Rural Sustainability in Korea

2018

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An imperative challenge emerges from the demand to apply the scientific method in the assessment of recent agricultural and rural policies throughout the world. The objective of the present study was to conduct an ex-post quantitative evaluation of the Comprehensive Rural Village Development Program (CRVDP), a representative rural development policy operated by the Ministry of Agriculture, Food and Rural Affairs, a central government agency in South Korea. The primary purpose of this program is to ensure sustainable rural society. This study found a moderate but significant positive impact of the policy in enhancing the standard of living in rural areas. The present paper concludes with suggesting some policy implications, limitations and future directions of policy evaluation studies.

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New Orleans Business Recovery in the Aftermath of Hurricane Katrina

Cited by 137 publications

References 17 publications

Approximate Bayesian inference for spatial econometrics models

Approximate Bayesian inference for spatial econometrics models

Assessing Community Resilience to Coastal Hazards in the Lower Mississippi River Basin

The Impact of the Comprehensive Rural Village Development Program on Rural Sustainability in Korea

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