Vector representation of migration streams among selected state economic areas during 1955 to 1960

This paper uses in-migration, out-migration, and net migration vectors to measure and portray the migration streams occurring between April 1, 1955, and April 1, 1960, for all persons five years old and over on April 1, 1960, among 38 contiguous state economic areas (SEA's) in Oklahoma, Colorado, Kansas, Missouri, Arkansas, Texas, and New Mexico. Both rectangular and polar coordinates were employed in computing three different types of vector representations. First, migration vectors for each SEA, expressed in total people-miles of in-movement, out-movement, and net movement represent the resultant direction of movement and the total people-miles of movement. Second, in- and out-migration vectors of the total people-miles of movement, with the widths of the vectors drawn proportional to the number of migrants, show the total size of each migration stream for each SEA, the resultant direction of movement, and the distance moved in total people-miles of movement. p ]Third, in- and out-migration vectors of the average distances moved to and from each SEA, with the widths of the vectors drawn proportional to the number of migrants, show the magnitude of movement, the average distance moved per migrant, and the resultant direction of movement. This study shows that migration vectors based upon polar coordinates are more accurate than the migration vectors based upon rectangular coordinates. Also, the in- and out-migration vectors are superior to net migration vectors in representing migratory streams among geographic areas.     

Predicting losses of residential structures in the state of Florida by the public hurricane loss evaluation model

As an environmental phenomenon, hurricanes cause significant property damage and loss of life in coastal areas almost every year. Although a number of commercial loss projection models have been developed to predict the property losses, only a handful of studies are available in the public domain to predict damage for hurricane prone areas. The state of Florida has developed an open, public model for the purpose of probabilistic assessment of risk to insured residential property associated with wind damage from hurricanes. The model comprises three components; viz. the atmospheric science component, the engineering component and the actuarial science component. The atmospheric component includes modeling the track and intensity life cycle of each simulated hurricane within the Florida threat area. Based on historical hurricane statistics, thousands of storms are simulated allowing determination of the wind risk for all residential Zip Code locations in Florida. The wind risk information is then provided to the engineering and actuarial components to model damage and average annual loss, respectively. The actuarial team finds the county-wise loss and the total loss for the entire state of Florida. The computer team then compiles all information from atmospheric science, engineering and actuarial components, processes all hurricane related data and completes the project. The model was submitted to the Florida Commission on Hurricane Loss Projection Methodology for approval and went through a rigorous review and was revised as per the suggestions of the commission. The final model was approved for use by the insurance companies in Florida by the commission. At every stage of the process, statistical procedures were used to model various parameters and validate the model. This paper presents a brief summary of the main components of the model (meteorology, vulnerability and actuarial) and then focuses on the statistical validation of the same.     

Authors’ responses to the discussion on “Predicting losses of residential structures in the state of Florida by the Public Hurricane Loss Evaluation Model”

Hurricanes threaten the Florida coast line every year and are capable of causing catastrophic losses. The Public Hurricane Loss Model was developed in response to the need for having an open transparent model to predict the above losses. The results were summarized in the paper “Predicting Losses of Residential Structures in the State of Florida by the Public Hurricane Loss Model” which was subsequently a subject of discussion by experts in Meteorology, Engineering, Actuarial Sciences and Statistics. The present paper presents a response to the discussions on the above mentioned article.