Geographically intelligent disclosure control for flexible aggregation of census data

This paper describes a geographically intelligent approach to disclosure control for protecting flexibly aggregated census data. Increased analytical power has stimulated user demand for more detailed information for smaller geographical areas and customized boundaries. Consequently it is vital that improved methods of statistical disclosure control are developed to protect against the increased disclosure risk. Traditionally methods of statistical disclosure control have been aspatial in nature. Here we present a geographically intelligent approach that takes into account the spatial distribution of risk. We describe empirical work illustrating how the flexibility of this new method, called local density swapping, is an improved alternative to random record swapping in terms of risk-utility

Alternative sampling and estimation methods for multispecies trawl surveys

Thesis (Ph.D.) University of Alaska Fairbanks, 2004Multispecies demersal trawl surveys are used in the United States and internationally to estimate the relative abundance of commercial and non-commercial fish species. Their usefulness for estimating species' abundance is often limited by the variance associated with estimates. This study implemented and evaluated alternative sampling and estimation methods, with the goal to incorporate additional sources of information for increased precision of individual species' estimates from multispecies trawl surveys. First, habitat characteristics and past spatial distributions of four flatfish species' density were incorporated into a multispecies trawl survey design conducted in Kalsin and Middle Bays, Kodiak Island, Alaska. Stratification by depth and percent sand produced estimates of relative abundance with lower CV s than those from unstratified sampling. Additional decreases in relative precision were generally not achieved by estimating the relative abundance of multiple species from regions of species-specific suboptimal habitat. Second, a poststratification technique was used to incorporate species-specific habitat characteristics and previous distributions of species' density into the estimation of species' abundance from the Kalsin and Middle Bays' trawl survey. Poststratification by habitat gave estimates with lower variance and/or less design-bias than an unstratified estimator for all species in all years. Poststratification by habitat and fish density produced estimates with the least design-bias for all species in all years and the lowest variance when stratum sample sizes were sufficient. Third, mixed model linear regression (MMLR), empirical Bayes (EB) and hierarchical Bayes (HB) estimation methods were used to incorporate historical trends of yellowfin sole, Limanda aspera biomass from the eastern Bering Sea trawl survey into annual biomass estimates. Using MMLR, EB, and HB methods resulted in biomass estimates that were less anomalous than survey estimates with respect to a linear regression trend. Estimates for all three methods had lower CV s than surveys in most years. The results of this thesis suggest that incorporating additional information into survey design and estimation can decrease the variability of survey estimates and/or correct for possible bias. Methods that can incorporate additional information, therefore, have the potential to improve survey assessments for management use.Introduction -- Multispecies survey designs with habitat and fish density information -- Using poststratification to improve multispecies survey assessments : case study of juvenile flatfishes -- A comparison of models for incorporating multiple years of information into annual estimates of biomass from multispecies trawl surveys -- Conclusions

Network dependence in multi-indexed data on international trade flows

Faced with the problem that conventional multidimensional fixed effects models only focus on unobserved heterogeneity, but ignore any potential cross-sectional dependence due to network interactions, we introduce a model of trade flows between countries over time that allows for network dependence in flows, based on sociocultural connectivity structures. We show that conventional multidimensional fixed effects model specifications exhibit cross-sectional dependence between countries that should be modeled to avoid simultaneity bias. Given that the source of network interaction is unknown, we propose a panel gravity model that examines multiplenetwork interaction structures, using Bayesian model probabilities to determine those most consistent with the sample data. This is accomplished with the use of computationally efficient Markov Chain Monte Carlo estimation methods that produce a Monte Carlo integration estimate of the log-marginal likelihood that can be used for model comparison. Application of the model to a panel of trade flows points to network spillover effects, suggesting the presence of network dependence and biased estimates from conventional trade flow specifications. The most important sources of network dependence were found to be membership in trade organizations, historical colonial ties, common currency, and spatial proximity of countries.Series: Working Papers in Regional Scienc

Revisiting Guerry's data: Introducing spatial constraints in multivariate analysis

Standard multivariate analysis methods aim to identify and summarize the main structures in large data sets containing the description of a number of observations by several variables. In many cases, spatial information is also available for each observation, so that a map can be associated to the multivariate data set. Two main objectives are relevant in the analysis of spatial multivariate data: summarizing covariation structures and identifying spatial patterns. In practice, achieving both goals simultaneously is a statistical challenge, and a range of methods have been developed that offer trade-offs between these two objectives. In an applied context, this methodological question has been and remains a major issue in community ecology, where species assemblages (i.e., covariation between species abundances) are often driven by spatial processes (and thus exhibit spatial patterns). In this paper we review a variety of methods developed in community ecology to investigate multivariate spatial patterns. We present different ways of incorporating spatial constraints in multivariate analysis and illustrate these different approaches using the famous data set on moral statistics in France published by Andr\'{e}-Michel Guerry in 1833. We discuss and compare the properties of these different approaches both from a practical and theoretical viewpoint.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS356 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Digitizing mass spectrometry data to explore the chemical diversity and distribution of marine cyanobacteria and algae.

Natural product screening programs have uncovered molecules from diverse natural sources with various biological activities and unique structures. However, much is yet underexplored and additional information is hidden in these exceptional collections. We applied untargeted mass spectrometry approaches to capture the chemical space and dispersal patterns of metabolites from an in-house library of marine cyanobacterial and algal collections. Remarkably, 86% of the metabolomics signals detected were not found in other available datasets of similar nature, supporting the hypothesis that marine cyanobacteria and algae possess distinctive metabolomes. The data were plotted onto a world map representing eight major sampling sites, and revealed potential geographic locations with high chemical diversity. We demonstrate the use of these inventories as a tool to explore the diversity and distribution of natural products. Finally, we utilized this tool to guide the isolation of a new cyclic lipopeptide, yuvalamide A, from a marine cyanobacterium