Flood events cause substantial damage to urban and rural areas. Monitoring water extent during large-scale flooding is crucial in order to identify the area affected and to evaluate damage. During such events, spatial assessments of floodwater may be derived from satellite or airborne sensing platforms. Meanwhile, an increasing availability of smartphones is leading to documentation of flood events directly by individuals, with information shared in real-time using social media. Topographic data, which can be used to determine where floodwater can accumulate, are now often available from national mapping or governmental repositories. In this work, we present and evaluate a method for rapidly estimating flood inundation extent based on a model that fuses remote sensing, social media and topographic data sources. Using geotagged photographs sourced from social media, optical remote sensing and high-resolution terrain mapping, we develop a Bayesian statistical model to estimate the probability of flood inundation through weights-of-evidence analysis. Our experiments were conducted using data collected during the 2014 UK flood event and focus on the Oxford city and surrounding areas. Using the proposed technique, predictions of inundation were evaluated against ground-truth flood extent. The results report on the quantitative accuracy of the multisource mapping process, which obtained area under receiver operating curve values of 0.95 and 0.93 for model fitting and testing, respectively.

Objective: Higher intakes of red and processed meat are associated with poorer health outcomes and negative environmental impacts. Drawing upon a population survey the present paper investigates meat consumption behaviours, exploring perceived impacts for human health, animal welfare and the environment. Design: Structured self-completion postal survey relating to red and processed meat, capturing data on attitudes, sustainable meat purchasing behaviour, red and processed meat intake, plus sociodemographic characteristics of respondents. Setting: Urban and rural districts of Nottinghamshire, East Midlands, UK, drawn from the electoral register. Subjects: UK adults (n 842) aged 18-91 years, 497 females and 345 males, representing a 35·6 % response rate from 2500 randomly selected residents. Results: Women were significantly more likely (P < 0·01) to consume ≤ 1 portion of meat/d compared with men. Females and older respondents (>60 years) were more likely to hold positive attitudes towards animal welfare (P < 0·01). Less than a fifth (18·4 %) of the sample agreed that the impact of climate change could be reduced by consuming less meat, dairy products and eggs. Positive attitudes towards animal welfare were associated with consuming less meat and a greater frequency of 'higher welfare' meat purchases. Conclusions: Human health and animal welfare are more common motivations to avoid red and processed meat than environmental sustainability. Policy makers, nutritionists and health professionals need to increase the public's awareness of the environmental impact of eating red and processed meat. A first step could be to ensure that dietary guidelines integrate the nutritional, animal welfare and environmental components of sustainable diets.

There is no evidence to suggest that wine and tobacco consumption may protect against Alzheimer's disease.

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The purpose of this paper is to describe the R package PTAk and how the spatiotemporal context can be taken into account in the analyses. Essentially PTAk() is a multiway multidimensional method to decompose a multi-entries data-array, seen mathematically as a tensor of any order. This PTAk-modes method proposes a way of generalizing SVD (singular value decomposition), as well as some other well known methods included in the R package, such as PARAFAC or CANDECOMP and the PCAn-modes or Tucker-n model. The example datasets cover different domains with various spatio-temporal characteristics and issues: (i) medical imaging in neuropsychology with a functional MRI (magnetic resonance imaging) study, (ii) pharmaceutical research with a pharmacodynamic study with EEG (electro-encephaloegraphic) data for a central nervous system (CNS) drug, and (iii) geographical information system (GIS) with a climatic dataset that characterizes arid and semi-arid variations. All the methods implemented in the R package PTAk also support non-identity metrics, as well as penalizations during the optimization process. As a result of these flexibilities, together with pre-processing facilities, PTAk constitutes a framework for devising extensions of multidimensional methods such as correspondence analysis, discriminant analysis, and multidimensional scaling, also enabling spatio-temporal constraints.

When it comes to characterize the distribution of 'things' observed spatially and identified by their geometries and attributes, the Shannon entropy has been widely used in different domains such as ecology, regional sciences, epidemiology and image analysis. In particular, recent research has taken into account the spatial patterns derived from topological and metric properties in order to propose extensions to the measure of entropy. Based on two different approaches using either distance-ratios or co-occurrences of observed classes, the research developed in this paper introduces several new indices and explores their extensions to the spatio-temporal domains which are derived whilst investigating further their application as global and local indices. Using a multiplicative space-time integration approach either at a macro or micro-level, the approach leads to a series of spatio-temporal entropy indices including from combining co-occurrence and distances-ratios approaches. The framework developed is complementary to the spatio-temporal clustering problem, introducing a more spatial and spatio-temporal structuring perspective using several indices characterizing the distribution of several class instances in space and time. The whole approach is first illustrated on simulated data evolutions of three classes over seven time stamps. Preliminary results are discussed for a study of conflicting maritime activities in the Bay of Brest where the objective is to explore the spatio-temporal patterns exhibited by a categorical variable with six classes, each representing a conflict between two maritime activities.