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Frequency analysis of tornadoes is very important for risk analysis and disaster control. In this paper, the annual frequency of tornadoes that occurred in the United States from 1967 to 2016 is analyzed. The simple analysis shows that frequencies of tornadoes of different sites are spatially correlated and over-dispersed. To explain the two characteristics of the data, the Bayesian hierarchical model is proposed. For comparison purposes, the Bayesian model with negative binomial distribution, Poisson distribution, Polya distribution, and first-order, non-negative, integer-valued autoregressive model with Bell innovations(BL-INAR(1)) are considered to fit the frequency of tornado. The distribution parameters of all sites are assumed to be spatially correlated, and the corresponding Bayesian hierarchical models were established. MCMC (Markov Chain Monte Carlo) method is applied to parameter estimations and relative statistical inference. By comparison of the analysis results, the negative binomial distribution is recommended to analyze the overdispersion and spatial correlation among the sites of the data. The comparison between the simulated frequencies based on the proposed model and the actual frequencies also verifies that the proposed method is a better model for the data. [ABSTRACT FROM AUTHOR]

One of the continuing challenges in wildlife ecology and management is the ability to obtain reliable estimates of species' distributions at large spatial extents. Multi-scale occupancy models using a cluster sampling design offer the opportunity to increase the resolution of estimates and model processes occurring at multiple spatial scales, increasing the efficiency of large-scale monitoring and mitigating the tradeoff between extent and grain. However, accounting for spatial correlation among subsamples in a way that allows for the addition of covariates remains an issue. Using tracking transect surveys for carnivores as an example, we describe and evaluate a hierarchical, multi-scale occupancy model that integrates existing approaches to estimate occupancy at multiple spatial scales simultaneously, and uses a conditional autoregressive (CAR) process to account for spatial correlation in use between subsamples. We evaluated 3 versions of the model under a single-survey and a multi-survey sampling design: a non-spatial model, amodel that accounted for spatial correlation in use between transect segments, and a model that also accounted for spatial correlation in the detection process. Simulations showed that accounting for spatial correlation gave better estimates of transect-level occupancy under both sampling designs, whereas accurate estimates of segment-level use required a multi-survey design. When applied to historical snow track data, the differences in estimates among models followed the same pattern found in the simulations. The multi-survey design was able to detect equivalent declines in segment use with much less survey effort than the single-survey design. The modeling framework presented here offers researchers and managers a powerful tool for monitoring populations at large spatial extents while being able to detect ecologically important dynamics at finer spatial scales. [ABSTRACT FROM AUTHOR]

This paper discusses measurement of the main dimensions of the urban environment that have been proposed as relevant to explaining geographic variations in obesity and inactivity. It considers urban sprawl, food access and exercise access as latent constructs, defined by sets of observed indicators for areas. In an application to 993 US metropolitan counties, the paper shows how these latent constructs may be incorporated in an ecological (area-scale) model, which recognizes spatial aspects in the patterning of both outcomes and environmental factors. Urban sprawl and area socioeconomic status emerge from regression modelling as leading influences on obesity and inactivity. [ABSTRACT FROM AUTHOR]

The Agricultural Act of 2014 solidified insurance as the cornerstone of U.S. agricultural policy. The Congressional Budget Office (2014) estimates that this act will increase spending on agricultural insurance programs by $5.7 billion to a total of $89.8 billion over the next decade. In light of the sizable resources directed toward these programs, accurate rating of insurance contracts is of the utmost importance to producers, private insurance companies, and the federal government. Unlike most forms of insurance, agricultural insurance is plagued by a paucity of spatially correlated data. A novel interpretation of Bayesian Model Averaging is used to estimate a set of possibly similar densities that offers greater efficiency if the set of densities are similar while seemingly not losing any if the set of densities are dissimilar. Simulations indicate that finite sample performance—in particular small sample performance—is quite promising. The proposed approach does not require knowledge of the form or extent of any possible similarities, is relatively easy to implement, admits correlated data, and can be used with either parametric or nonparametric estimators. We use the proposed approach to estimate U.S. crop insurance premium rates for area-type programs and develop a test to evaluate its efficacy. An out-of-sample game between private insurance companies and the federal government highlights the policy implications for a variety of crop-state combinations. Consistent with the simulation results, the performance of the proposed approach with respect to rating area-type insurance—in particular small sample performance—remains quite promising. [ABSTRACT FROM AUTHOR]

Composite morbidity indices summarize geographic inequalities in disease, and are used to distribute resources. A spatial latent variable approach is developed for such an index, focusing on lung cancer in 3,141 U.S. counties. The model incorporates multiple indicators (cancer deaths and incidence), but also allows for population risk variables (area socio-economic, environmental, and smoking indicators) that affect lung cancer, and for missingness among indicators or risk variables. Selection of significant causes is illustrated, including nonadaptive and adaptive selection. To reflect geographic clustering in lung cancer, the latent morbidity index is spatially correlated, although the level of correlation is data determined. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, we use continuous urban structure instead of zonal model, try to calculate unbiased excess commuting with joint distribution of homes and workplaces developed by , and describe the relationship between urban structure and commuting distance explicitly and theoretically for generalized home–workplace assignment pattern. We simplify the quadrivariate distribution model to a model with three important parameters: the spread of homes, the spread of workplaces, and the spatial correlation of homes and workplaces. Then we show that excess commuting and capacity utilization are expressed by the imbalance and the spatial correlation of jobs–housing structure in a theoretical context, moreover it explicitly evaluates targeting US and Japanese/Korean cities. [Copyright &y& Elsevier]

This study investigated the potential impact of soil moisture perturbations on the statistical spread of an ensemble forecast for three different synoptic events during the summer of 2006. Soil moisture was perturbed from a control simulation to generate a 12 member ensemble with six drier and six moister soils. The impacts on the near-surface atmospheric conditions and on precipitation were analysed. It was found, as previous studies have confirmed, that soil moisture can change the spatial and temporal distribution of precipitation and of the overlying circulation. It was found that regardless of the conditions in synoptic forcing, temperature, relative humidity and horizontal wind field exhibited a spatial correlation coefficient (R) close to one with respect to the control simulation. Vertical velocity, however, showed a marked decrease in R down to 0.4 as the precipitation activity increased. For vertical velocity, however, this quantity grew to near 1.0 consistent with R near zero and standard deviations very close to that of the control. These results suggested a more complex picture in which soil moisture perturbations played a major role in modifying precipitation and the near-surface circulation but did not broaden the statistical spread of trajectories in phase space of all variables. [ABSTRACT FROM AUTHOR]

Abstract: In this article, we study the behavior of the stock prices of a subset of eight U.S. industries from the late 1800''s to the Great Depression. In particular, we focus on the potential presence of volatility shifts, the persistence of volatility, and on the degree of co-movement of stock returns prior to and during the Great Depression. Our findings show that stock markets became particularly volatile toward the mid 1930''s, but that the persistence of volatility tended to decrease around the same time period. In that regard, we find little evidence that such behavior is driven by trading volume. In addition, we conclude that the overall correlation across the different industries was relatively more significant in statistical terms from 1921 to part of the Great Depression (1929–1931; 1933–1934 and 1936). [Copyright &y& Elsevier]

Abstract: The ability of the US Environmental Protection Agency''s Community Multi-scale Air Quality (CMAQ) model to simulate the wet deposition of ammonium during 8-week winter and summer periods in 2001 is evaluated using observations from the National Acid Deposition Program (NADP) monitoring sites. The objective of this study is to ascertain the effects of precipitation simulations and emissions on CMAQ simulations of deposition. In both seasons, CMAQ tends to underpredict the deposition amounts. Based on the co-located measurements of ammonium wet deposition and precipitation at the NADP sites and on estimated precipitation amounts for each grid cell, Bayesian statistical methods are used to estimate ammonium wet deposition over all grid cells in the study region. To assess the effect of precipitation on the CMAQ simulations, our statistical method is run twice for each time period, using the simulated precipitation information provided to CMAQ and precipitation estimates based on data collected by the cooperative observer network. During the winter period when stratiform-type precipitation dominates, precipitation amounts do not seem to be a major factor in CMAQ''s ability to simulate the wet deposition of ammonium. However, during the summer period when precipitation is mainly generated by convective processes, small portions of the region are identified in which problems with precipitation simulations may be adversely affecting CMAQ''s estimates. [Copyright &y& Elsevier]

Variations in the extreme percentiles of empirical hourly PM2.5 concentration distributions from a unique high-density network of 20 stations within New York City are statistically analyzed. Significant diurnal, seasonal and day-of-week variations are noted, with the highest concentrations typically found between 7:00 and 9:00 a.m., during summer, and on weekdays. The lowest concentrations are generally found during early morning hours (4:00–6:00 a.m.), in winter and on weekends. The amplitudes of these seasonal and diurnal cycles vary with percentile, with less pronounced cycles for the lowest and in some cases highest percentiles. The diurnal and day-of-week patterns suggest that although anthropogenic factors may be primarily responsible for the observed diurnal cycle, meteorological conditions also have some influence.There is little spatial variation in concentration across the city. Highly significant between-station correlations are obtained for all seasons. However, lower correlation is found in winter. Meteorologically, the highest PM2.5 concentrations occur with moderate southwesterly winds and high temperatures and humidity during summer. These conditions are related to the westward expansion of the Bermuda high-pressure system. Calm winds are conducive to the highest winter particulate concentrations. Relatively strong northerly winds are typically associated with the lowest PM2.5 concentrations. It appears that regional-scale processes dominate day-to-day changes in particulate concentrations across the city. [Copyright &y& Elsevier]

• Provided a new insight to model the safety state transition processes. • Compare the fatality risk state transition between different exposures. • Evidence supports trends towards a homogeneity of fatality risk states in a neighborhood. • The strength and duration of spatial effect on temporal dynamics of fatality risk states is investigated. The rate of road traffic fatalities has long served as a regular indicator to evaluate and compare road safety performance for different administrative divisions. This article introduces a novel method known as the Markov chain spatial model to incorporate the spatial effects into the temporal dynamic of the fatality rates. Compared to the traditional Markov chain model, the proposed spatial Markov chain model can quantify the influence of neighboring sites explicitly in the transition process. A case study using a long duration dataset, from 1975 to 2015 in the 48 lower states of the United Sates, was conducted to illustrate the proposed model. The fatality rates were measured as the number of traffic fatalities per 100 million vehicle miles or per 10,000 residents. The results show that the probability of transition for one state between different levels of traffic fatality risks depends largely on the context of its surrounding neighbors. Another important finding is that relative to the estimates of traditional Markov chain models, states surrounded by neighborhoods with relatively low fatality rates take a longer time to transform to a higher level of fatality risk in the spatial Markov chain model. On the other hand, those with high-risk neighborhoods takes less time to deteriorate. These findings confirm that it is imperative to incorporate spatial effects when modeling the temporal dynamic of safety indicators to assess and monitor the safety trends in the areas of interest. [ABSTRACT FROM AUTHOR]

Many countries, including the USA, publish predicted numbers of cancer incidence and death in current and future years for the whole country. These predictions provide important information on the cancer burden for cancer control planners, policymakers and the general public. Based on evidence from several empirical studies, the joinpoint (segmented-line linear regression) model (JPM) has been adopted by the American Cancer Society to estimate the number of new cancer cases in the USA and in individual states since 2007. Recently, cancer incidence in smaller geographic regions such as counties, and local policy makers are increasingly interested with Federal Information Processing Standard code regions. The natural extension is to directly apply the JPM to county-level cancer incidence data. The direct application has several drawbacks and its performance has not been evaluated. To address the concerns, we developed a spatial random-effects JPM for county-level cancer incidence data. The proposed model was used to predict both cancer incidence rates and counts at the county level. The standard JPM and the proposed method were compared through a validation study. The proposed method outperformed the standard JPM for almost all cancer sites, especially for moderate or rare cancer sites and for counties with small population sizes. As an application, we predicted county-level prostate cancer incidence rates and counts for the year 2011 in Connecticut., (Published 2013. This article is a US Government work and is in the public domain in the USA.)