Bayesian Spatial Modeling Approach Research Articles

Assessing drivers of estuarine debris using a Bayesian spatial modelling approach (INLA-SPDE)

Estuaries are under increasing pressure from urbanization, including through the release of domestic wastes such as anthropogenic debris. Growing concern over environmental contamination has led to management interventions, aimed at reducing the further release of debris. Effective strategies, however, require an understanding of the factors influencing the abundance and distribution of debris. Estuaries pose a challenge to assess debris trends due to the myriad of factors that influence debris patterns. Here we present a Bayesian approach to assess spatial patterns in debris across four estuaries of New South Wales, Australia. We provide an overview of debris composition and employ Integrated Nested Laplace Approximation (INLA) alongside Stochastic Partial Differential Equation (SPDE) to produce three models testing predictors of debris abundance: (i) non spatial, (ii) a model accounting for spatial autocorrelation and (iii) accounting for spatial autocorrelation and introducing the estuarine foreshore as a barrier. Data was sourced from cleanups reported within the Australian Marine Debris Initiative (AMDI), a national citizen science program coordinated by the Tangaroa Blue Foundation. The data, from 169 sampling events between 2012 and 2017 was used. All models tested predictors against the abundance of hard plastic, expanded plastic, plastic bags, and plastic drink bottles. Plastic was the dominant component, ranging from 69% to 82% of total debris within the study estuaries. Important predictors differed across debris types and included ‘distance to estuary mouth’, access to foreshore, population, and site substrate. Specific factors differed in importance when accounting for spatial autocorrelation, demonstrating the need for spatial approaches when assessing debris trends within estuaries. The present Bayesian approach could be used to better understand drivers of estuarine debris and inform interventions for effective management.

Under age five children survival times in Nigeria: a Bayesian spatial modeling approach

BackgroundNigeria is among the top five countries in the world with the highest under-five mortality rates. In addition to the general leading causes of under-five mortality, studies have shown that disparity in sociocultural values and practices across ethnic groups in Nigeria influence child survival, thus there is a need for scientific validation. This study quantified the survival probabilities and the impact of socioeconomic and demographic factors, proximate and biological determinants, and environmental factors on the risk of under-five mortality in Nigeria.MethodsThe Kaplan-Meier survival curve, Nelson Aalen hazard curve, and components survival probabilities were estimated. The Exponential, Gamma, Log-normal, Weibull, and Cox hazard models in a Bayesian mixed effect hierarchical hazard modeling framework with spatial components were considered, and the Deviance and Watanabe Akaike information criteria were used to select the best model for inference. A 5% level of significance was assumed throughout this work. The 2018 Nigeria Demographic and Health Survey dataset was used, and the outcome variable was the time between birth and death or birth and the date of interview for children who were alive on the day of the interview.ResultsFindings show that the probability of a child dying within the first two months is 0.04, and the probability of a boy child dying before attaining age five is 0.106, while a girl child is 0.094 probability. Gender, maternal education, household wealth status, source of water and toilet facility, residence, mass media, frequency of antenatal and postnatal visits, marital status, place of delivery, multiple births, who decide healthcare use, use of bednet are significant risk factors of child mortality in Nigeria. The mortality risk is high among the maternal age group below 24 and above 44years, and birth weight below 2.5Kg and above 4.5Kg. The under-five mortality risk is severe in Kebbi, Kaduna, Jigawa, Adamawa, Gombe, Kano, Kogi, Nasarawa, Plateau, and Sokoto states in Nigeria.ConclusionThis study accentuates the need for special attention for the first two months after childbirth as it is the age group with the highest expected mortality. A practicable way to minimize death in the early life of children is to improve maternal healthcare service, promote maternal education, encourage delivery in healthcare facilities, positive parental attitude to support multiple births, poverty alleviation programs for the less privileged, and a prioritized intervention to Northern Nigeria.

Neighborhood context mediates probability of host tree mortality in a severe bark beetle outbreak

AbstractUnderstanding drivers of disturbances across scales is critical as environmental constraints change in a warming climate. Outbreaks of native bark beetles (Curculionidae: Scolytinae) are key natural disturbances that shape the structure and function of conifer forests across the northern hemisphere. While drivers of bark beetle outbreaks have been studied extensively at spatial scales ranging from stands to continents, within‐stand processes governing individual tree mortality in an outbreak are less well understood. Here, we use a spatially explicit long‐term monitoring dataset of a lodgepole pine (Pinus contorta var. latifolia) forest (>9000 individually mapped trees in three 2‐ha plots) impacted by a severe mountain pine beetle (Dendroctonus ponderosae) outbreak to explore interactions among fine scale drivers of beetle‐caused tree mortality. Using a Bayesian spatial modeling approach, we evaluated how tree scale and tree neighborhood scale characteristics interact with tree size to mediate host tree susceptibility to mountain pine beetle outbreak in the Southern Rocky Mountains (USA). We found evidence that both tree growth rate preceding the outbreak and neighborhood structure (within a 10 meter radius of the host tree) mediate the effect of tree size, and that the direction and magnitude of these mediating effects vary with tree size. Tree scale mortality probability increased with pre‐outbreak growth rate for small to medium sized host trees (~10–25 cm diameter), but that same effect was not detected for large trees. Conversely, tree scale mortality probability increased with greater neighborhood density, with the most pronounced effects for medium to large sized host trees (~15–30 cm diameter). Within‐stand topographic variability was not an important predictor of mortality probability; among stands, however, the stand in the driest topographic position experienced the greatest overall mortality. By explicitly considering how within‐stand heterogeneity mediates individual tree scale susceptibility to bark beetle outbreak, our findings bridge an important gap in understanding multi‐scale drivers of disturbance dynamics.

Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

A multivariate Bayesian spatial modeling approach was used to jointly model the counts of two types of crime, i.e., burglary and non-motor vehicle theft, and explore the geographic pattern of crime risks and relevant risk factors. In contrast to the univariate model, which assumes independence across outcomes, the multivariate approach takes into account potential correlations between crimes. Six independent variables are included in the model as potential risk factors. In order to fully present this method, both the multivariate model and its univariate counterpart are examined. We fitted the two models to the data and assessed them using the deviance information criterion. A comparison of the results from the two models indicates that the multivariate model was superior to the univariate model. Our results show that population density and bar density are clearly associated with both burglary and non-motor vehicle theft risks and indicate a close relationship between these two types of crime. The posterior means and 2.5% percentile of type-specific crime risks estimated by the multivariate model were mapped to uncover the geographic patterns. The implications, limitations and future work of the study are discussed in the concluding section.

The effect of road network patterns on pedestrian safety: A zone-based Bayesian spatial modeling approach

Pedestrian safety is increasingly recognized as a major public health concern. Extensive safety studies have been conducted to examine the influence of multiple variables on the occurrence of pedestrian-vehicle crashes. However, the explicit relationship between pedestrian safety and road network characteristics remains unknown. This study particularly focused on the role of different road network patterns on the occurrence of crashes involving pedestrians. A global integration index via space syntax was introduced to quantify the topological structures of road networks. The Bayesian Poisson-lognormal (PLN) models with conditional autoregressive (CAR) prior were then developed via three different proximity structures: contiguity, geometry-centroid distance, and road network connectivity. The models were also compared with the PLN counterpart without spatial correlation effects. The analysis was based on a comprehensive crash dataset from 131 selected traffic analysis zones in Hong Kong.The results indicated that higher global integration was associated with more pedestrian-vehicle crashes; the irregular pattern network was proved to be safest in terms of pedestrian crash occurrences, whereas the grid pattern was the least safe; the CAR model with a neighborhood structure based on road network connectivity was found to outperform in model goodness-of-fit, implying the importance of accurately accounting for spatial correlation when modeling spatially aggregated crash data.

Open area and road density as land use indicators of young offender residential locations at the small-area level: A case study in Ontario, Canada

This research explores associations between land use types and young offender residential location in the Regional Municipality of York, Ontario, Canada, at a small-area level. Employing a Bayesian spatial modelling approach, we found that after controlling for socio-economic risk factors, proportion of open area land use was positively associated, and road density negatively associated, with residential location of young offenders. Map decomposition, which visualises the contribution of each risk factor to total young offender risk, demonstrated that open area land use contributed more risk in rural areas than urban, and that road density contributed less risk in urban areas than rural. We propose explanations for these results focused on social disorganisation theory and accessibility to structured leisure activities and apply findings to inform law enforcement and land use planning. Results provide a criminological perspective not often considered in planning and urban studies research and contrast land use policies generally motivated by public health and the environment.

The epidemiology of gonorrhoea in London: a Bayesian spatial modelling approach

Data obtained from genitourinary medicine clinics through a comprehensive surveillance system were used in a Bayesian mixed-effects Poisson regression model to explore socio-demographic individual and ecological risk factors for gonorrhoea in London, as well as its spatial clustering. The spatial analysis was performed at the Middle-layer Super Output Area level (median population size 7200). A total of 12452 individuals were diagnosed during the 2-year study period (2009-2010). The study confirmed the presence of 'core areas' of high incidence, and identified 'core' high-risk groups, in particular young adults (16-29 years), males, black Caribbeans and more deprived areas. The individual (age, sex, ethnicity) and area-level (deprivation, teenage pregnancies, students) model covariates accounted for 48% of the variance. Most of the remaining variance was explained by the spatial effect, thus capturing other spatially distributed factors associated with gonorrhoea, such as local sexual networks. These findings will be useful in identifying areas for targeted interventions, such as STI testing and health promotion.

Exploring links between juvenile offenders and social disorganization at a large map scale: a Bayesian spatial modeling approach

This paper adopts a Bayesian spatial modeling approach to investigate the distribution of young offender residences in York Region, Southern Ontario, Canada, at the census dissemination area level. Few geographic researches have analyzed offender (as opposed to offense) data at a large map scale (i.e., using a relatively small areal unit of analysis) to minimize aggregation effects. Providing context is the social disorganization theory, which hypothesizes that areas with economic deprivation, high population turnover, and high ethnic heterogeneity exhibit social disorganization and are expected to facilitate higher instances of young offenders. Non-spatial and spatial Poisson models indicate that spatial methods are superior to non-spatial models with respect to model fit and that index of ethnic heterogeneity, residential mobility (1 year moving rate), and percentage of residents receiving government transfer payments are, respectively, the most significant explanatory variables related to young offender location. These findings provide overwhelming support for social disorganization theory as it applies to offender location in York Region, Ontario. Targeting areas where prevalence of young offenders could or could not be explained by social disorganization through decomposing the estimated risk map are helpful for dealing with juvenile offenders in the region. Results prompt discussion into geographically targeted police services and young offender placement pertaining to risk of recidivism. We discuss possible reasons for differences and similarities between the previous findings (that analyzed offense data and/or were conducted at a smaller map scale) and our findings, limitations of our study, and practical outcomes of this research from a law enforcement perspective.