Factors influencing privatization of urban solid waste collection: some evidence from Spain

In this paper we analyse some of the organisational aspects of the urban solid waste collection and, in particular, the privatization modality of contracting out. We start by discussing some of the theoretical aspects of contracting out. We then specify and estimate an explanatory model on a sample of municipalities that we surveyed. Our purpose is twofold: on the one hand, we identify the economic factors at work when deciding to contract out the service and, on the other hand, we analyse the role of ideological factors in choosing between the public production of the service or contracting it out. The results show a significant effect of the demand for waste collection on contracting out. There also appears to be a neighbouring effect as the municipalities close to other cities that contract out are also more prone to do so. Finally, the decisions to contract seem to have been motivated by pragmatic rather than ideological reasons.

Classification of urban multi-angular image sequences by aligning their manifolds

When dealing with multi-angular image sequences, problems of reflectance changes due either to illumination and acquisition geometry, or to interactions with the atmosphere, naturally arise. These phenomena interplay with the scene and lead to a modification of the measured radiance: for example, according to the angle of acquisition, tall objects may be seen from top or from the side and different light scatterings may affect the surfaces. This results in shifts in the acquired radiance, that make the problem of multi-angular classification harder and might lead to catastrophic results, since surfaces with the same reflectance return significantly different signals. In this paper, rather than performing atmospheric or bi-directional reflection distribution function (BRDF) correction, a non-linear manifold learning approach is used to align data structures. This method maximizes the similarity between the different acquisitions by deforming their manifold, thus enhancing the transferability of classification models among the images of the sequence.