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Movement of animals from one farm to another is a potential risk and can lead to the spreading of livestock diseases. Therefore, in order to implement effective control measures, it is important to understand the movement network in a given area. Using the SANITEL data from 2005 to 2009, around 2 million cattle movements in Belgium were traced. Exploratory analysis revealed different spatial structures for the movement of different cattle types: fattening calves are mostly moved to the Antwerp region, adult cattle are moved to different parts in Belgium. Based on these differences, movement of cattle would more likely cause a spread of disease to a larger number of areas in Belgium as compared to the fattening calves. A closer inspection of the spatial and temporal patterns of cattle movement using a weighted negative binomial model, revealed a significant short-distance movement of bovine which could be an important factor contributing to the local spreading of a disease. The model however revealed hot spot areas of movement in Belgium; four areas in the Walloon region (Luxembourg, Hainaut, Namur and Liege) were found as hot spot areas while East and West Flanders are important "receivers" of movement. This implies that an introduction of a disease to these Walloon regions could result in a spread toward the East and West Flanders regions, as what happened in the case of Bluetongue BTV-8 outbreak in 2006. The temporal component in the model also revealed a linear trend and short- and long-term seasonality in the cattle movement with a peak around spring and autumn. The result of this explorative analysis enabled the identification of "hot spots" in time and space which is important in enhancing any existing monitoring and surveillance system. [ABSTRACT FROM AUTHOR]

Abstract: While many studies have concentrated on the effects of the spatial distribution of services on individual accessibility, only little is known about the ways in which equity of individual accessibility is affected by the temporal organisation of service delivery. This paper seeks to deepen our understanding about the relationship between accessibility, equity and the opening hours of public service facilities on the basis of space–time accessibility measures. Three approaches based on different equity principles are presented to schedule the opening hours of public service facilities: a utilitarian, an egalitarian and a distributive approach. A case study of public libraries in Ghent (Belgium) demonstrates the relevance of these approaches for amending the opening hours of public services to control the equity of accessibility levels across individuals. [Copyright &y& Elsevier]

Abstract: In the beginning of the 21st century, it is obvious that health and environmental matters are among the most important political and societal topics. The various kinds of pollution (e.g. air pollution, soil pollution, water contamination) are responsible for significant health and environmental degradation. In order to adequately deal with pollution issues, it is important to better understand the acting processes and to be able to account for specific knowledge about the pollutant. Thanks to this, it will be possible to forecast pollutant concentrations so that efficient actions can be rapidly taken. Based on a Bayesian data fusion (BDF) framework, the present paper proposes a methodology for air pollutant forecasting using the space–time properties of the process and several secondary information sources that contribute to a better understanding of the pollutant behavior (e.g. meteorological variables and anthropogenic activities). Consequently, the present work can contribute to improving the representation and the forecast of pollutant fields. Moreover the developed approach also permits to predict the probability of exceeding a given threshold, as required in official regulations for some pollutants (e.g. the European directives). The BDF framework is applied here to the case of space–time predictions of air concentrations of nitrogen dioxide (NO2) in Belgium. After a detailed description of some specific assumptions, results showed that BDF is able to successfully account for secondary information sources, thus leading to meaningful NO2 predictions. [Copyright &y& Elsevier]