Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Market-Based Instruments for ecosystem services: institutional innovation or renovation?

Recent years have seen widespread experimentation with market-based instruments (MBIs) for the provision of environmental goods and ecosystem services. However, little attention has been paid to their design or to the effects of the underlying pro-market narrative on environmental policy instruments. The purpose of this article is to analyze the emergence and dissemination of the term "market-based instruments" applied to the provision of environmental services and to assess to what extent the instruments associated are genuinely innovative. The recommendation to develop markets can lead in practice to a variety of institutional forms, as we show it based on the example of payments for environmental services (PES) and biodiversity offsets, two very different mechanisms that are both presented in the literature as MBIs. Our purpose is to highlight the gap between discourse and practice in connection with MBIs.

Clustering and hot spot detection in socio-economic spatio-temporal data

Distribution of socio-economic features in urban space is an important source of information for land and transportation planning. The metropolization phenomenon has changed the distribution of types of professions in space and has given birth to different spatial patterns that the urban planner must know in order to plan a sustainable city. Such distributions can be discovered by statistical and learning algorithms through different methods. In this paper, an unsupervised classification method and a cluster detection method are discussed and applied to analyze the socio-economic structure of Switzerland. The unsupervised classification method, based on Ward's classification and self-organized maps, is used to classify the municipalities of the country and allows to reduce a highly-dimensional input information to interpret the socio-economic landscape. The cluster detection method, the spatial scan statistics, is used in a more specific manner in order to detect hot spots of certain types of service activities. The method is applied to the distribution services in the agglomeration of Lausanne. Results show the emergence of new centralities and can be analyzed in both transportation and social terms.

State-Society Relationships, Social Trust and the Development of Labour Market Policies in Italy and Sweden

The first decade of the twenty-first century may be remembered for the rebirth of consensus on labour market policy. After three decades of bitter political and ideological controversy between a neo-liberal and a traditional social democratic approach, a new model, often labelled flexicurity, has emerged. This model is promoted by numerous political organisations since it promises to put an end to the old trade-off between equality and efficiency. Several countries are embracing the flexicurity model as a blueprint for labour market reform, but others, mostly belonging to the 'Mediterranean Rim', are clearly lagging behind. Why is it so difficult for these countries to implement the flexicurity model? This paper argues that the application of a flexicurity strategy in these countries is complicated by the lack of social trust between social partners and the state as well as political economy traditions that highlight the role of labour market regulation as a source of social protection.