Many studies have considered the economic, social, and psychological effects of hurricanes, earthquakes, floods, tornadoes, and other natural disasters, but few have considered their demographic effects. In this paper we describe and evaluate a method for measuring the effects on Hurricane Andrew on the housing stock and population distribution in Dade County, Florida
Hurricane Andrew blasted through the southern tip of Florida in August 1992, damaging or destroying tens of thousands of homes and forcing hundreds of thousands of persons to move at least temporarily to different places of residence. The hurricane not only disrupted the lives of many Floridians, but destroyed the statistical basis for producing local population estimates in South Florida as well. These estimates are used for many types of decision-making, from the distribution of state revenue-sharing dollars to choosing sites for fast-food restuarants.
Counts of the U.S. Hispanic population are available every ten years from the decennial census, but for the years following or between censuses, estimates have to be created using data and techniques that are expected to track changes in that population over time. Such estimates are a recent development and there is currently no standard methodology that has been widely used, carefully documented, and rigorously tested. In this article, we describe an experimental methodology for estimating the Hispanic population of states and counties.
This article is a review of–and response to–a special issue of Mathematical Population Studies that focused on the relative performance of simpler vs. more complex population projection models.
While market researchers hunt for new niches of robust consumers and communities vie for educated, affluent residents, Florida has them and may not know it. They are the temporary residents known as snowbirds. Because official ties are often with other states, they elude Florida data catchers. Socioeconomic data available from a survey by the University of Florida's Bureau of Economic and Business Research reveal their characteristics and habits.
Net migration has been widely criticized as a theoretical concept and as a measure of population movement. Many of these criticisms are valid: net migration reflects a residual rather than a true migration process, it often masks large gross migration flows, it cannot account for differences in the characteristics of origin and destination populations, it cannot be used for rates in a probabilistic sense, and it can lead to misspecified causal models and unrealistic population projections.
The housing unit method is the most commonly used method for making small-area population estimates in the United States and is widely used in other countries as well. These estimates are used for a variety of budgeting, planning, and analytical purposes in both the public and private sectors; consequently, detailed evaluations of their accuracy are essential. In this study, we evaluate the precision and bias of April 1, 2000 population estimates for counties and subcounty areas in Florida.
Business demography encompasses the application of demographic concepts, data, and techniques to the practical concerns of business decision makers. This loosely organized field includes—but is not limited to—site selection, sales forecasting, financial planning, market assessment, consumer profiles, target marketing, litigation support, and labor force analysis. Specific applications have evolved over time, reflecting changes in data sources, computer technology, statistical techniques, and the business environment itself.
Spurred by new business applications and government programs, the demand for small-area demographic data and analysis has grown tremendously in recent decades. To meet that demand, analysts have drawn on an expanding set of data sources, statistical techniques, and computer applications. The result has been improved data quality across a broad spectrum of variables and geographic areas, enhancing both the usefulness and the importance of small-area analyses.
A number of studies have dealt with the use of time series models to develop confidence intervals for population forecasts. Most have focused solely on national-level models and only a few have considered the accuracy of the resulting forecasts. In this study, we take this research in a new direction by constructing time series models for several states in the United States and evaluating the resulting population forecasts.