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This article introduces bayesian spatial smoothing models for disease mapping—a specific application of small area estimation where the full universe of data is known—to a wider audience of public health professionals using firearm suicide as a motivating example. Besag, York, and Mollié (BYM) Poisson spatial and space–time smoothing models were fitted to firearm suicide counts for the years 2014-2018. County raw death rates in 2018 ranged from 0 to 24.81 deaths per 10 000 people. However, the highest mortality rate was highly unstable, based on only 2 deaths in a population of approximately 800, and 80.5% of contiguous US counties experienced fewer than 10 firearm suicide deaths and were thus suppressed. Spatially smoothed county firearm suicide mortality estimates ranged from 0.06 to 4.05 deaths per 10 000 people and could be reported for all counties. The space–time smoothing model produced similar estimates with narrower credible intervals as it allowed counties to gain precision from adjacent neighbors and their own counts in adjacent years. bayesian spatial smoothing methods are a useful tool for evaluating spatial health disparities in small geographies where small numbers can result in highly variable rate estimates, and new estimation techniques in R software have made fitting these models more accessible to researchers. [ABSTRACT FROM AUTHOR]

The cancer atlas edited by several countries is the main resource for the analysis of the geographic variation of cancer risk. Correlating the observed spatial patterns with known or hypothesized risk factors is time-consuming work for epidemiologists who need to deal with each cancer separately, breaking down the patterns according to sex and race. The recent literature has proposed to study more than one cancer simultaneously looking for common spatial risk factors. However, this previous work has two constraints: they consider only a very small (2-4) number of cancers previously known to share risk factors. In this article, we propose an exploratory method to search for latent spatial risk factors of a large number of supposedly unrelated cancers. The method is based on the singular value decomposition and nonnegative matrix factorization, it is computationally efficient, scaling easily with the number of regions and cancers. We carried out a simulation study to evaluate the method's performance and apply it to cancer atlas from the USA, England, France, Australia, Spain, and Brazil. We conclude that with very few latent maps, which can represent a reduction of up to 90% of atlas maps, most of the spatial variability is conserved. By concentrating on the epidemiological analysis of these few latent maps a substantial amount of work is saved and, at the same time, high-level explanations affecting many cancers simultaneously can be reached., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Understanding how individual pollution sources contribute to ambient sulfate pollution is critical for assessing past and future air quality regulations. Since attribution to specific sources is typically not encoded in spatial air pollution data, we develop a mechanistic model which we use to estimate, with uncertainty, the contribution of ambient sulfate concentrations attributable specifically to sulfur dioxide (SO2) emissions from individual coal-fired power plants in the central United States. We propose a multivariate Ornstein–Uhlenbeck (OU) process approximation to the dynamics of the underlying space-time chemical transport process, and its distributional properties are leveraged to specify novel probability models for spatial data that are viewed as either a snapshot or time-averaged observation of the OU process. Using US EPA SO2 emissions data from 193 power plants and state-of-the-art estimates of ground-level annual mean sulfate concentrations, we estimate that in 2011—a time of active power plant regulatory action—existing flue-gas desulfurization (FGD) technologies at 66 power plants reduced population-weighted exposure to ambient sulfate by 1.97 μg/m3 (95% CI: 1.80–2.15). Furthermore, we anticipate future regulatory benefits by estimating that installing FGD technologies at the five largest SO2-emitting facilities would reduce human exposure to ambient sulfate by an additional 0.45 μg/m3 (95% CI: 0.33–0.54). for this article are available online. [ABSTRACT FROM AUTHOR]

While various studies explore the relationship between individual sources of climate variability and extreme precipitation, there is a need for improved understanding of how these physical phenomena simultaneously influence precipitation in the observational record across the contiguous United States. In this work, we introduce a single framework for characterizing the historical signal (anthropogenic forcing) and noise (natural variability) in seasonal mean and extreme precipitation. An important aspect of our analysis is that we simultaneously isolate the individual effects of seven modes of variability while explicitly controlling for joint inter-mode relationships. Our method utilizes a spatial statistical component that uses in situ measurements to resolve relationships to their native scales; furthermore, we use a data-driven procedure to robustly determine statistical significance. In Part I of this work we focus on natural climate variability: detection is mostly limited to DJF and SON for the modes of variability considered, with the El Niño/Southern Oscillation, the Pacific–North American pattern, and the North Atlantic Oscillation exhibiting the largest influence. Across all climate indices considered, the signals are larger and can be detected more clearly for seasonal total versus extreme precipitation. We are able to detect at least some significant relationships in all seasons in spite of extremely large (> 95%) background variability in both mean and extreme precipitation. Furthermore, we specifically quantify how the spatial aspect of our analysis reduces uncertainty and increases detection of statistical significance while also discovering results that quantify the complex interconnected relationships between climate drivers and seasonal precipitation. [ABSTRACT FROM AUTHOR]

Ecological occupancy modeling has historically relied on high-quality, low-quantity designedsurvey data for estimation and prediction. In recent years, there has been a large increase in the amount of high-quantity, unknown-quality opportunistic data. This has motivated research on how best to combine these two data sources in order to optimize inference. Existing methods can be infeasible for large datasets or require opportunistic data to be located where designed-survey data exist. These methods map species occupancies, motivating a need to properly evaluate covariate effects (e.g., land cover proportion) on their distributions. We describe a spatial estimation method for supplementarily including additional opportunistic data using mediation analysis concepts. The opportunistic data mediate the effect of the covariate on the designed-survey data response, decomposing it into a direct and indirect effect. A component of the indirect effect can then be quickly estimated via regressing the mediator on the covariate, while the other components are estimated through a spatial occupancy model. The regression step allows for use of large quantities of opportunistic data that can be collected in locations with no designed-survey data available. Simulation results suggest that the mediated method produces an improvement in relative MSE when the data are of reasonable quality. However, when the simulated opportunistic data are poorly correlated with the true spatial process, the standard, unmediated method is still preferable. A spatiotemporal extension of the method is also developed for analyzing the effect of deciduous forest land cover on red-eyed vireo distribution in the southeastern United States and find that including the opportunistic data do not lead to a substantial improvement. Opportunistic data quality remains an important consideration when employing this method, as with other data integration methods. [ABSTRACT FROM AUTHOR]

Gridded data products, for example interpolated daily measurements of precipitation from weather stations, are commonly used as a convenient substitute for direct observations because these products provide a spatially and temporally continuous and complete source of data. However, when the goal is to characterize climatological features of extreme precipitation over a spatial domain (e.g., a map of return values) at the native spatial scales of these phenomena, then gridded products may lead to incorrect conclusions because daily precipitation is a fractal field and hence any smoothing technique will dampen local extremes. To address this issue, we create a new "probabilistic" gridded product specifically designed to characterize the climatological properties of extreme precipitation by applying spatial statistical analysis to daily measurements of precipitation from the Global Historical Climatology Network over the contiguous United States. The essence of our method is to first estimate the climatology of extreme precipitation based on station data and then use a data-driven statistical approach to interpolate these estimates to a fine grid. We argue that our method yields an improved characterization of the climatology within a grid cell because the probabilistic behavior of extreme precipitation is much better behaved (i.e., smoother) than daily weather. Furthermore, the spatial smoothing innate to our approach significantly increases the signal-to-noise ratio in the estimated extreme statistics relative to an analysis without smoothing. Finally, by deriving a data-driven approach for translating extreme statistics to a spatially complete grid, the methodology outlined in this paper resolves the issue of how to properly compare station data with output from earth system models. We conclude the paper by comparing our probabilistic gridded product with a standard extreme value analysis of the Livneh gridded daily precipitation product. Our new data product is freely available on the Harvard Dataverse (https://bit.ly/2CXdnuj). [ABSTRACT FROM AUTHOR]

Wildfires pose a danger to both ecologies and communities. To this end, many large-scale analyses of wildfire patterns and behavior rely on the aggregation of point data to polygons, typically those based on distinct disparate ecological areas. However, the sizes, shapes, and orientations of the polygons to which data are aggregated are not neutral factors in the resulting analysis. The influence of the aggregation polygons on calculated results is known as the modifiable areal unit problem (MAUP), which is well-documented in the spatial statistics literature. Despite the documentation of the MAUP, relatively few wildfire studies consider the effects of the MAUP on their results. Here, wildfire data from the Western United States are aggregated to twenty-five different sets of polygons. Variation by fishnet polygon area and shape are measured via summary statistics and a spatio-temporal trend analysis. Variation is also quantified between well-established hierarchical nested ecoregion polygons via summary statistics. Lastly, best practices for mitigating the effects of the MAUP on future wildfire studies are recommended. [ABSTRACT FROM AUTHOR]

To study possibly nonlinear relationship between housing price index (HPI) and consumer price index (CPI) for individual states in the USA, accounting for the temporal lag interactions of the housing price in a given state and spatio‐temporal lag interactions between states could improve the accuracy of estimation and forecasting. There lacks, however, methodology to objectively identify and estimate such spatio‐temporal lag interactions. In this article, we propose a semiparametric data‐driven nonlinear time series regression method that accounts for lag interactions across space and over time. A penalized procedure utilizing adaptive Lasso is developed for the identification and estimation of important spatio‐temporal lag interactions. Theoretical properties for our proposed methodology are established under a general near epoch dependence structure and thus the results can be applied to a variety of linear and nonlinear time series processes. For illustration, we analyze the US housing price data and demonstrate substantial improvement in forecasting via the identification of nonlinear relationship between HPI and CPI as well as spatio‐temporal lag interactions. [ABSTRACT FROM AUTHOR]

Massive social media data produced from microblog platforms provide a new data source for studying human dynamics at an unprecedented scale. Meanwhile, population bias in geotagged Twitter users is widely recognized. Understanding the demographic and socioeconomic biases of Twitter users is critical for making reliable inferences on the attitudes and behaviors of the population. However, the existing global models cannot capture the regional variations of the demographic and socioeconomic biases. To bridge the gap, we modeled the relationships between different demographic/socioeconomic factors and geotagged Twitter users for the whole contiguous United States, aiming to understand how the demographic and socioeconomic factors relate to the number of Twitter users at county level. To effectively identify the local Twitter users for each county of the United States, we integrate three commonly used methods and develop a query approach in a high-performance computing environment. The results demonstrate that we can not only identify how the demographic and socioeconomic factors relate to the number of Twitter users, but can also measure and map how the influence of these factors vary across counties. [ABSTRACT FROM AUTHOR]

Highlights • Propose a multivariate spatial approach to model crash count by injury severity. • Results of Moran's I tests confirm the ability of the proposed model to account for multivariate spatial autocorrelation. • Diagnose autocorrelation and convergence of MCMC chains. • Unobserved heterogeneity is found to be mostly attributed to spatial factors instead of non-spatial ones. Abstract Conventional safety models rely on the assumption of independence of crash data, which is frequently violated. This study develops a novel multivariate conditional autoregressive (MVCAR) model to account for the spatial autocorrelation of neighboring sites and the inherent correlation across different crash types. Manhattan, which is the most densely populated urban area of New York City, is used as the study area. Census tracts are used as the basic geographic units to capture crash, transportation, land use, and demo-economic data. The specification of the proposed multivariate model allows for jointly modeling counts of various crash types that are classified according to injury severity. Results of Moran's I tests show the ability of the MVCAR model to capture the multivariate spatial autocorrelation among different crash types. The MVCAR model is found to outperform the others by presenting the lowest deviance information criterion (DIC) value. It is also found that the unobserved heterogeneity was mostly attributed to spatial factors instead of non-spatial ones and there is a strong shared geographical pattern of risk among different crash types. [ABSTRACT FROM AUTHOR]

Institutions such as firms, universities, and governments drive migration. Yet we know little about the different “pull powers” of institutions; that is, their ability to draw new members from distant and diverse locales. In this case study, we examine various pull power statistics of institutions of higher education as they draw student-athletes to their campuses. We collected data on nearly 160,000 student-athletes from more than 1,600 university team rosters at 128 schools over various years. Because roster records include hometowns, we use these data to quantify the distances and variety of hometowns from which universities attract students. We use descriptive statistics such as the mean distance traveled, count of unique hometowns, percentage of international student-athletes, and a new distance decay “apex” method to rank schools by their pull power. Results show that western U.S. and private schools tend to pull students from more distant and diverse locales, although many exceptions exist. Results also show that certain sports are likely to furnish more international than domestic students and that teams create pipelines that source multiple athletes from a single country. Finally, pull power is not found to correlate with endowment and athletic expenditure statistics. This analysis provides a fresh perspective on the movement of student-athletes, modern-day chain migration, and the value of institutions in catalyzing migration. [ABSTRACT FROM AUTHOR]

Open space and recreation land protection can provide a variety of community benefits. Limited government resources, however, may preclude land acquisition and enhancement activities. State governments have thus turned to “bond referenda” as a mechanism to increase financial resource capacity to provide public goods. The purpose of this study is to explore two groups of factors (resident characteristics and land use types) related to voter support for an environmental bond in the state of Rhode Island, USA. A spatial regression model is used to explore these relationships. The results show that voting districts with high-density residential land use and a non-white population are more supportive of the environmental bond referendum, as are voting districts with more open space that is publicly accessible and that have recreational amenities. Voting districts with a high percentage of natural habitat land are less supportive of the bond referendum. The findings suggest government agencies should pay more attention to the equitable distribution of public goods and to providing more open space land that is public accessible. [ABSTRACT FROM AUTHOR]

This article examines the intraurban geography of craft breweries in ten cities across the United States. First, through an exhaustive literature review, we outline both supply- and demand-side factors that might cause craft breweries to cluster. Second, we empirically test whether these establishments tend to cluster within cities using spatial statistical techniques. Many communities are attempting to support the establishment of more craft breweries as a way to boost tourism and economic development. The findings from this article aid in this discussion by providing insights into how craft brewers locate and the factors that could influence their location decision behavior. Our findings suggest that craft brewers do in fact cluster. There are both supply and demand factors responsible. On the supply side, the collaborative environment within the industry and the artisan nature of the industry's products allows for benefits of clustering to outweigh the costs associated with this behavior. On the demand side, the emergence of “brewery districts” allows individual brewers to enjoy the reputation benefits associated with the district in terms of increased foot traffic from locals and visitors looking to sample a variety of beers. [ABSTRACT FROM AUTHOR]

This article advances strategic management by developing a data-driven simulation method to analyze how the characteristics of a group influence the characteristics of the group's components. We apply our method to the underrepresentation of women in the top management of S&P 1,500 firms. Although extant research suggests that the presence of women in top management could be self-reinforcing, we theorize and provide strong evidence that women face an implicit quota, whereby a firm's leadership makes an effort to have a small number of women in top management, but makes less effort to have, or even resists having, larger numbers of women. In consequence, the presence of a woman on a top management team reduces the likelihood that another woman occupies a position on that team. [ABSTRACT FROM AUTHOR]

The purpose of this study was to reduce the dimensionality of a set of neighborhood-level variables collected on participants in the Multi-Ethnic Study of Atherosclerosis (MESA) while appropriately accounting for the spatial structure of the data. A common spatial factor analysis model in the Bayesian setting was utilized in order to properly characterize dependencies in the data. Results suggest that use of the spatial factor model can result in more precise estimation of factor scores, improved insight into the spatial patterns in the data, and the ability to more accurately assess associations between the neighborhood environment and health outcomes. [ABSTRACT FROM AUTHOR]

It is widely known that prolonged exposure to high levels of traffic noise has several health effects. While scholarship in environmental justice has explored the environmental equity hypothesis in a wide range of areas, whether the spatial distribution of traffic noise is equitable among different racial and socioeconomic groups has rarely been explored, especially in the United States. This article addresses this lacuna by examining this relationship in the Twin Cities Metro Region, Minnesota. Traffic data from the Minnesota Department of Transportation were used to model the propagation of traffic noise over the study area and aircraft noise contour lines were added to account for aircraft noise. Inequities associated with exposure to chronic traffic noise were investigated using selected demographic and socioeconomic variables from the U.S. Census 2000. Statistical analysis was based on a regression model that addressed spatial autocorrelation. Results indicate that there is an association between noise levels and household income, median household value, the percentage of non-white residents, and the percentage of the population less than 18 years of age. [ABSTRACT FROM AUTHOR]

The infant mortality rate is a fundamental measure of population health used internationally. In the United States, the infant mortality rate is higher than what would be expected for a country of its affluence. We present an analysis of US county infant mortality rates using modern Bayesian spatial statistical methodologies. Our key predictors in our statistical analysis are residential racial and poverty segregation, measured by the dissimilarity, interaction and spatial proximity indexes. We use both Exploratory Spatial Data Analysis methods and Hierarchical Bayesian spatial regression models to examine the influences of these segregation measures on the infant mortality rate for each county, net of income inequality, degree of rurality and relative socioeconomic deprivation. The spatial measures of racial segregation suggest that when blacks live in close proximity to each other, this tends to increase the infant mortality rate. The results for poverty segregation suggest the same pattern, when poor populations live in close proximity to one another this is generally detrimental to the county infant mortality rate. However, interaction between blacks and whites and poor and non-poor residents of an area is protective for infant mortality. [ABSTRACT FROM AUTHOR]

We develop statistical tools for regression analysis of nominal categorical data on a spatial lattice that are becoming increasingly abundant because of the advances of geographic information systems in environmental science. In a generalized linear mixed model framework, we model the response variable by a multinomial distribution. There are two additive components in the linear predictor: a linear regression on covariates and a spatial random effect such that the spatial dependence in the random effect is induced by a multivariate conditional autoregressive model. Bayesian hierarchical modeling is used for statistical inference, and Markov chain Monte Carlo algorithms are devised to obtain posterior samples. The methodology is applied to analyze a northern Wisconsin land cover data set in a study that assesses the relationship between forest landscape structure and past social conditions, expanding the analytical tools available in landscape ecology and environmental history. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Wireless networks have become an important part of our daily lives. However, while the networking research community has made great progress in developing the communication technologies themselves, the underlying dynamics of deployment and use of wireless technologies is still relatively poorly understood. This is especially true for user-deployed technologies such as Wi-Fi hotspots, as well as the large-scale use of radio spectrum. This situation is already starting to cause difficulties in the wireless networking research community due to the arising lack of network deployment models for performance evaluation of new wireless technologies. Also the governmental regulators planning for new policy frameworks are lacking models and hard data on how existing networks and devices use the radio spectrum made available to them by the current regulatory regime. These issues are very topical globally, and are being actively pursued by the Federal Communications Commission (FCC) in the USA, as well as its European and Asian counterparts. In this paper we discuss our work on applying spatial statistics techniques for constructing models for structure of wireless networks and the way they use the radio spectrum. We focus specifically on key research challenges that would be of particular interest to the wireless communications community. [Copyright &y& Elsevier]

The density of human populations in nearshore areas is more than three times the global average and is one example of why monitoring sensitive nearshore environments is essential. In this paper we outline a method for map-based monitoring of nearshore flora and epifauna using the seabed imaging and mapping system (SIMS) and geographic analysis. This system uses underwater video and sidescan sonar to systematically inventory and classify nearshore habitats. Species presence and abundance were mapped in 2006 and 2009 for a coastal area of British Columbia, Canada, and represented in a geographic information system (GIS). Spatial statistics were applied to maps of change in species abundance, and hot spots of floral and epifaunal change were identified. While minimal overall change within species groups occurs over 3 years, local areas of significant change were found near the marina entrance and the Washington State ferry terminal, where marine boat traffic may be affecting vegetation. The use of spatial statistics with this method reduces the effects of seasonal variability, minimizes impact of data errors, and identifies statistically significant hot spots of change. We have also demonstrated that SIMS generates suitable data for change detection and monitoring. [ABSTRACT FROM AUTHOR]

In 2006, bacterial pathogens were the leading cause of water quality concerns in the U.S. With more than 300 water bodies in the state of Texas failing to meet water quality standards because of bacteria, managing bacteria pollution commanded the attention of regulatory agencies, researchers, and stakeholders across Texas. In order to assess, monitor, and manage water quality, it was necessary to characterize the sources of pathogens within the watershed. The objective of this study was to develop a spatially explicit method to estimate potential E. coli loads in Plum Creek watershed in east central Texas. Locations of contributing non-point and point sources in the watershed were defined using Geographic Information Systems (GIS). By distributing livestock, wildlife, wastewater treatment plants, septic systems, and pet sources, the bacterial load in the watershed was spatially characterized. Contributions from each source were quantified by applying source specific bacterial production rates, and ranking of each contributing source was assessed for the entire watershed. Cluster and discriminant analyses were used to identify similar regions within the watershed for selecting appropriate best management practices. Based on the statistical analysis and the spatially explicit method, four clusters of subwatersheds were found and characterized. The analysis provided a basis for development of spatially explicit identification of best management practices (BMPs) to be applied within the Watershed Protection Plan (WPP). [ABSTRACT FROM AUTHOR]

Bus rapid transit (BRT) is growing in popularity as a lower-cost alternative to light rail transit. Although the impacts of rail transportation on residential property values is well-explored, the impact of BRT on property values remains less well-understood, particularly in the United States where BRT infrastructure is more heterogeneous than the rest of the world. This paper addresses this gap by evaluating and comparing the before-and-after effect of 11 BRT systems on nearby property values in ten metropolitan areas across the United States. We used a quasi-experimental approach and hedonic spatial error models with propensity score matching to measure change in residential property transaction prices within walking distance of a BRT station. Overall model results show a mix of appreciation, depreciation, and no change in residential properties value across different BRT systems. Multi-family properties nearby BRTs with on-street dedicated lanes generally experienced the most appreciation while single-family properties around off-street busway systems experienced depreciation. BRT-lite systems without dedicated lanes associate with property appreciation in relatively dense and congested metropolitan areas with developed transit networks and high ridership. Our model results emphasize the ability of BRT to improve transit accessibility in these regions and to provide an attractive alternative to driving. Furthermore, the lack of property appreciation around busways indicates these systems may not provide nearby residents with an amenity bonus greater than its nuisance effects. Our study informs stakeholders and public officials about the broad effects of BRT on land values and invites researchers to continue investigating the role of walkability, nuisance effects, and individual BRT amenities on residential property values. [ABSTRACT FROM AUTHOR]

Understanding the complexity of urban expansion requires an analysis of the factors influencing the spatial and temporal processes of rural-urban land conversion. This study aims at building a statistical land conversion model to assist in understanding land use change patterns. Specifically, GIS coupled with a logistic regression model and exponential smoothing techniques is used for exploring the effects of various factors on land use change. These factors include population density, slope, proximity to roads, and surrounding land use, and their influence on land use change is studied for generating a predictive model. Methods to reduce spatial autocorrelation in a logistic regression framework are also discussed. Primarily, an optimal sampling scheme that can eliminate spatial autocorrelation while maintaining adequate samples to allow the model to achieve the comparable accuracy as the spatial autoregressive model is developed. Since many of the previous studies on modeling the spatial complexity of urban growth ignored temporal complexity, a modified exponential smoothing technique is employed to produce a smoothed model from a series of bi-temporal models obtained from different time periods. The proposed model is validated using the multi-temporal land use data in New Castle County, DE, USA. It is demonstrated that our approach provides an effective option for multi-temporal land use change modeling and the modeling results help interpret the land use change patterns. [ABSTRACT FROM AUTHOR]

Understanding human development at the international, regional, and national level is essential to the understanding of the needs and wants of vast populations around the world. Since the late 1980's the United Nation's Development Program (UNDP) has published hundreds of reports on development at a variety of geographic extents. This project aims to apply the United Nation's Human Development Index (HDI) to the United States in order to create a sub-national HDI for the measurement of US counties. Due to the inherent spatial methodology in the development of the HDI, this project further introduces the use of geographic information systems tools in hopes of better capturing the results of the development index and employs spatial statistics as a way of identifying clusters of high and low development. The results report high regional, state, and county variation in all of the measures and outcomes associated with the sub-national HDI. [ABSTRACT FROM AUTHOR]

The article focuses on the transmissions of racoon rabies throughout a heterogeneous landscape. It states that landscape attributes can contribute as obstructions or entrances to the wide dispersal of several infectious diseases, and perceive the manner on how geographic structure affects the widespread disease. Moreover, an investigation was carried out on the space-time diffusion of a racoon rabbit eruption in several U.S. states, which quantified the effect of landscape features.

The article offers information on the research conducted by Michelle Sims and colleagues on improving the spatial patterns in fisheries bycatch in the U.S. Researchers discovered that fisheries bycatch or incidental take of large vertebrates such as sea turtles, seabirds and marine mammals are disadvantageous to conservation and fisheries management issue. To address the issue, it is suggested to use Bayesian hierarchical spatial models because it provide more reliable estimates of bycatch rates, assess similarity of spatial patterns between taxa and identify areas of elevated risk of bycatch.

Since the U.S. Environmental Protection Agency began widespread monitoring of PM2.5 (particulate matter <2.5 p in diameter) concentration levels in the late 1990s, the epidemiological community has performed several observational studies directly relating PM2.5 concentration to various health endpoints including mortality and morbidity. However, recent research suggests that human exposure to the constituents of PM2.5 may differ significantly from ambient (or outdoor) PM2.5 concentration measured by monitors because people spend a great deal of time in environments, such as various indoor environments, where they are partially shielded from ambient sources of PM and are exposed to nonambient sources of PM. Recent research has provided some ways to include exposure information, but little has been done to determine the impact of including such information in a statistical model. To address this concern, we develop a three-stage Bayesian hierarchical model based on the Poisson regression model that is traditionally used to characterize the relationship between PM2.5 concentration and health endpoints. Our approach includes a spatial model relating monitor readings to average county PM2.5 concentration and an exposure simulator that links average ambient PM2.5 concentration to average personal exposure using activity pattern data. We apply our model to a study population in North Carolina and explore the impact of various exposure modeling assumptions on the conclusions that can be drawn about the link between PM2.5 exposure and cardiovascular mortality. [ABSTRACT FROM AUTHOR]

Evolutionary improvements in Geographic Information Systems (GIS) now routinely allow the management and mapping of spatial-temporal information. In response, the development of statistical models to combine information of different types and spatial support is of vital importance to environmental science. In this paper we develop a hierarchical spatial statistical model for environmental indicators of stream and river systems in the United States Mid-Atlantic Region by combining information from separate monitoring surveys, available contextual information on hydrologic units and remote sensing information. These models are used to estimate the indicators throughout the riverine system based on information from multiple sources and aggregate scales. The analysis is based on information underlying the Landscape Atlas of the mid-Atlantic region produced by the US Environmental Monitoring and Assessment Program (EMAP). We also combine information from two overlapping separate monitoring surveys, the EMAP Stream and River Survey and the Maryland Biological Streams Survey. We present a general framework for comparative distributional analysis based on the concept of a relative spatial distribution. As an application, the spatial model is used to predict spatial distributions and relative spatial distributions for a watershed. [ABSTRACT FROM AUTHOR]

Traditional box plots, described by Tukey in 1977, can be biased representations of geographic or spatial variables, since they do not take into account the areas associated with the elements of geographic variables. To help solve this problem, we propose and evaluate a spatially (areally) weighted box plot, a "geographic box plot," that can be used to describe a wide variety of geographic variables. Much of our discussion of box plots also applies to "traditional" and "geographic" histograms and percentile plots. Traditional and geographic box plots of one hypothetical and two real-world geographic variables are constructed and compared to illustrate our points, as well as to illustrate the often marked differences between traditional and geographic box plots of the same geographic variables. The real-world geographic variables considered here are human population density (in the year 2000) within the contiguous 48 United States and five-year-average (January 2001-December 2005) air temperature over the Arctic land surface. Our traditional box plot of population density over represented the small, high-density states, whereas our corresponding geographic box plot correctly depicted the geographic distribution of population density. A geographic box plot correctly accounted for the reduction in spherical grid-node areas with latitude over the Arctic land surface, whereas a traditional box plot of the spherical grid-node values did not. Our hope is that geographers and other scientists will make refinements to and find many more applications for our geographic box plots. [ABSTRACT FROM AUTHOR]

In this study we evaluated residential location as a potential determinant for exposure to organochlorine compounds. We investigated the geographic distribution characteristics of organochlorine levels in approximately 1,374 blood samples collected in 1974 from residents of a community with a potential organochlorine source. Street addresses of Washington County, Maryland, residents were obtained and geocoded in a geographic information system. We used multivariate linear regression models to characterize the blood organochlorine levels of these residents that had been analyzed as part of previous studies using both environmental- and individual-level covariates. This was done to evaluate if the geographic distribution of blood levels in participants was related to the environmental source in the community. Model inference was based on generalized least squares to account for residual spatial variation. A significant inverse relationship was found between blood dieldrin levels and residential distance from the potential source. For every mile of distance from the source, blood dieldrin levels decreased 1.6 ng/g in study participants (p-value = 0.042), adjusting for age, sex, education level, smoking status, and drinking water source. 1,1-Dichloro-2,2-bis(p-chlorophenyl)ethylene (DDE) levels in the blood did not change significantly based on residential distance from the source, taking the same covariates into account. However, these results are limited by the inability to account for several potential confounders. This study demonstrates that spatially distributed covariates may play an important role in individual exposure patterns. Spatial information may enable researchers to detect a potential exposure pattern that may not be revealed with only nonspatial variables. [ABSTRACT FROM AUTHOR]

Conventional multivariate regression can hide important local variations in the relationships among independent and dependent variables in models of environmental equity. Geographically weighted regression (GWR), in combination with choropleth mapping, can reveal this spatial nonstationarity and shed light on its form. We use GWR, in combination with conventional univariate and multivariate statistics, to model the density of air toxic releases in New Jersey, as listed in the U.S. Environmental Protection Agency's Toxic Release Inventory (TRI). The GWR analysis shows that the relationships among race, class, employment, urban concentration, and land use with air toxic release density in New Jersey vary significantly over space. Generally, there is a positively significant relationship of minorities with air toxic releases over a large swath of urban and suburban New Jersey, although this pattern is not evident for all urban areas. Northeast New Jersey, the most densely populated part of the state, contains areas of both significantly positive and negative relationships between concentrations of minorities and air toxic releases. The association of minorities with concentrations of air toxic releases, where observed, is often mediated by other factors, though the role of these mediating factors also varies from place to place. In some of these areas the minority–air-toxic-release association is mediated by high poverty rates, in other areas, by the presence of industrial, commercial, and transportation land uses. [ABSTRACT FROM AUTHOR]

Understanding the role of scale is critical to ecologists' ability to make appropriate measurements, to "scale up" from local, short-term experiments to larger areas and longer times, to formulate models of community structure, and to address important conservation problems. Although these issues have received increased attention in recent years, empirical measurements of the scales of ecologically important variables are still rare. Here, we measure the spatial and temporal scales of variability of 15 ecologically relevant physical and biological processes in the wave-swept intertidal zone at Mussel Point, near Hopkins Marine Station in California. We analyze temporal variability in wave height, ocean temperature, upwelling intensity, solar irradiance, and body temperature for periods ranging from ten minutes to fifty years. In addition, we measure spatial variation in shoreline topography, wave force, wave-induced disturbance, body temperature, species diversity, recruitment, primary productivity, and the abundances of grazers, predators, and the competitive dominant occupier of space. Each of these spatial variables is measured along three horizontal transects in the upper intertidal zone: a short transect (44 m long with sampling locations spaced at ∼0.5-m intervals), a medium transect (175 m long with sampling locations spaced at ∼1.7- intervals), and a long transect (334 m long with sampling locations spaced at ∼3.4-m intervals). Six different methods are used to quantify the scale of each variable. Distinct scales are evident in all but one of our temporal variables, demonstrating that our methods for quantifying scale can work effectively with relatively simple, periodic phenomena. However, our spatial results reveal basic problems that arise when atternpting to measure the scale of variability for more complex phenomena. For a given variable and length of transect, different methods of calculating scale seldom agree, and in some cases estimates differ by more than an order of magnitude. For a given variable and method of calculating spatial scale, measurements are sensitive to the length of a transect; the longer the transect, the larger the estimate of scale. We propose that the "1/f noise" nature of the data can explain both the variability among methods for calculating scale and the length dependence of spatial scales of variation, and that the 1/f noise character of the data may be driven by the fractal geometry of shoreline topography. We conclude that it may not be possible to define a meaningful spatial scale of variation in this system. As an alternative to the boiled-down concept of "scale," we suggest that it is more appropriate to examine explicitly the pattern in which variability changes with the extent of measurement (e.g., the spectrum). Knowledge of this pattern can provide useful ecological scaling "rules" even when a well-defined scale (or hierarchy of scales) cannot be discerned. [ABSTRACT FROM AUTHOR]

Serial sectioning and imaging with a flatbed scanner yielded the three-dimensional size and spatial distribution of garnet porphyroblasts in two garnet schists and one staurolite-bearing schist from the Everett Formation, north-west Connecticut. The dominant garnet-producing reaction in all samples was chlorite+quartz=garnet+H2O. The appearance of staurolite, and additional garnet growth in the staurolite-bearing sample, was due to the reaction chloritoid=garnet+staurolite+chlorite. Statistical measures of garnet spatial distributions, using the pair correlation function (PCF), indicate that garnet crystals are weakly to strongly clustered at length scales between 2 and 10 mm. Such clustered nucleation may reflect minor bulk compositional variations. Covariance measures between garnet size and nearest-neighbour distance, using the mark covariance function (MCF), suggest a very weak correlation between crystal size and nearest-neighbour distance for length scales of 2 mm or less. These statistical data suggest that if diffusional gradients were present around growing garnet crystals, they did not influence nucleation and growth patterns at length scales greater than c. 2 mm. Compositional maps, through the garnet centres, show that the smaller crystals have lower Mn core compositions relative to larger crystals, consistent with progressive nucleation during pro-grade metamorphism. Radius-rate plots calculated from compositional X-ray maps show similar growth rates for garnet crystals of different size, consistent with an interface-controlled growth model for garnet. The presence of minor diffusional gradients around growing garnet cannot be entirely dismissed, but the lack of observable reaction rims, the clustered spatial distribution and the radius-rate data are most consistent with an interface-controlled garnet growth model. [ABSTRACT FROM AUTHOR]

The examination of human cancer occurrence from a spatial perspective can yield useful information that aids in the search for factors that influence cancer risk. The spatial implications of theoretical models of carcinogenesis suggest an approach for empirically testing theories relating to this problem. The spatial analysis of empirical cancer data can yield clues leading to the development and testing of causal hypotheses. Different models of the relation- ship between cancer risk and environmental factors yield different expected spatial patterns for two forms of skin cancer, malignant melanoma and other skin cancers. The empirical patterns are in basic agreement with those expected. Both lung and skin cancers exhibit complex pat- terns of spatial variation among U.S. counties. One-dimensional spatial analysis is used in this study to decompose these patterns by spatial scale in order to assess the nature of possible environmental risk-affecting agents. Large-area variation (or regional trend) is important in both lung and skin cancer patterns, but small-area patterning (or neighborhood effects) are important only for lung cancer. Significant differences between the patterns for males and females exist; for example, female lung cancer rates seem to be influenced by nearby urban places whereas male rates are strongly influenced only by larger urban places. [ABSTRACT FROM AUTHOR]

Tropical cyclones devastate coastlines around the world. The United States and surrounding areas experienced catastrophic extreme events in recent hurricane seasons. Understanding extreme hurricanes and how they change in a warming ocean environment is of the utmost importance. This study makes use of the historical, positive relationship between average summer sea surface temperatures (SSTs) and maximum hurricane wind speeds across the North Atlantic Basin from 1854–2018. Geographically weighted regression shows how the relationship between hurricane winds and SSTs varies across space. Each localized slope is used to increase historical wind speeds to represent winds in a three-degree Celsius warmer-than-average sea surface. The winds are then used to estimate the maximum intensity of the thirty-year hurricane (one with a 3.3% annual probability of occurrence) across the hexagonal grid using extreme value statistics. Viewing the results spatially allows for geographic patterns to emerge in the overall risk of major hurricane occurrence in warm SST environments. This study showcases the difference in the historical extreme compared to the potential future extreme in the hopes to better inform those charged with making important, life-saving decisions along the U.S. and neighboring coasts. [ABSTRACT FROM AUTHOR]

In air pollution epidemiology, measurements of relevant exposure concentrations are typically made at point locations, resulting in spatial misalignment between the exposure data and health outcomes aggregated at the area level. To obtain values that match the spatial units of the health data, observations can be averaged directly or prediction models developed. We present a framework for evaluating the error in aggregating exposure concentrations to the area unit. We present estimators of mean squared error that can be used for model selection. We find that exposure prediction models, even when misspecified, outperform monitor averages in settings with realistic numbers of monitors and that important reductions in error of the health effect estimate can be obtained when restricting to areas with a monitor. In an analysis of long‐term particulate matter concentrations across the United States, we estimated the error of the prediction model approach to be less than that of the monitor averaging approach on average across counties. We present health effect estimates about particulate matter exposure and pediatric asthma morbidity in the Medicaid population using each approach. Our findings support the use of a prediction model for estimating area‐wide averages, even when restricting to areas that contain a monitor. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to describe and characterize the spatial and temporal clustering patterns of suicide in the ten states with the greatest suicide burden in the United States from 1999 to 2016., Methods: All suicide deaths from January 1, 1999 to December 31, 2016 in the United States were identified using data from the Wide-ranging Online Data for Epidemiologic Research (WONDER) dataset. The ten states with the highest age-adjusted suicide rates were Montana, Alaska, Wyoming, New Mexico, Nevada, Utah, Idaho, Colorado, Arizona, and Oklahoma. A spatiotemporal scan statistic using a discrete Poisson model was employed to retrospectively detect spatiotemporal suicide clusters., Results: From 1999 to 2016, a total of 649,843 suicides were recorded in the United States. Nineteen statistically significant spatiotemporal suicide mortality clusters were identified in the states with the greatest suicide rates, and 13.53% of the suicide cases within these states clustered spatiotemporally. The risk ratio of the clusters ranged from 1.45 to 3.64 (p < 0.001). All states had at least one cluster, with three clusters spanning multiple states, and four clusters were found in Arizona. While there was no clear secular trend in the average size of suicide clusters, the number of clusters increased from 1999 to 2016., Conclusions: Hot spots for suicidal behavior in the United States warrant public health intervention and continued surveillance. As suicide rates in the US continue to increase annually, public health efforts could be maximized by focusing on regions with substantial clustering.

Potential climate-change-related impacts to agriculture in the upper Midwest pose serious economic and ecological risks to the U.S. and the global economy. On a local level, farmers are at the forefront of responding to the impacts of climate change. Hence, it is important to understand how farmers and their farm operations may be more or less vulnerable to changes in the climate. A vulnerability index is a tool commonly used by researchers and practitioners to represent the geographical distribution of vulnerability in response to global change. Most vulnerability assessments measure objective adaptive capacity using secondary data collected by governmental agencies. However, other scholarship on human behavior has noted that sociocultural and cognitive factors, such as risk perceptions and perceived capacity, are consequential for modulating people's actual vulnerability. Thus, traditional assessments can potentially overlook people's subjective perceptions of changes in climate and extreme weather events and the extent to which people feel prepared to take necessary steps to cope with and respond to the negative effects of climate change. This article addresses this knowledge gap by: (1) incorporating perceived adaptive capacity into a vulnerability assessment; (2) using spatial smoothing to aggregate individual-level vulnerabilities to the county level; and (3) evaluating the relationships among different dimensions of adaptive capacity to examine whether perceived capacity should be integrated into vulnerability assessments. The result suggests that vulnerability assessments that rely only on objective measures might miss important sociocognitive dimensions of capacity. Vulnerability indices and maps presented in this article can inform engagement strategies for improving environmental sustainability in the region., (© 2017 Society for Risk Analysis.)