SAGE PublicationsGielen, E.; Riutort-Mayol, G.; Palencia Jiménez, JS.; Cantarino-Martí, I. (2017) Analysis to obtain a single (unidimensional) measuring index of sprawl, which also allows us to obtain the uncertainty of the inferred index, in contrast to traditional approaches. All these techniques have been applied to study the phenomenon of urban sprawl at the municipality level in Valencia, Spain using a wide set of variables related to the characteristics and patterns of urban land use.

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Gaussian processes are powerful non-parametric probabilistic models for stochastic functions. However, the direct implementation entails a complexity that is computationally intractable when the number of observations is large, especially when estimated with fully Bayesian methods such as Markov chain Monte Carlo. In this paper, we focus on a low-rank approximate Bayesian Gaussian processes, based on a basis function approximation via Laplace eigenfunctions for stationary covariance functions. The main contribution of this paper is a detailed analysis of the performance, and practical recommendations for how to select the number of basis functions and the boundary factor. Intuitive visualizations and recommendations, make it easier for users to improve approximation accuracy and computational performance. We also propose diagnostics for checking that the number of basis functions and the boundary factor are adequate given the data. The approach is simple and exhibits an attractive computational complexity due to its linear structure, and it is easy to implement in probabilistic programming frameworks. Several illustrative examples of the performance and applicability of the method in the probabilistic programming language Stan are presented together with the underlying Stan model code.

In Europe, especially in the Valencian Community, Spain, the growth of cities in the last few decades has brought with it a major paradigm change, shifting from a compact to a sprawling urban model. Although it is known about its important environmental, social, and economic effects, there is no clear and unequivocal measurement of the impact of urban sprawl on municipal spending. The impact of the sprawling city on public finances and on the cost of local public services is clearly one of the conditioning factors that should be assessed when making urban development decisions. Based on a measurement of the sprawling city, our aim is to calculate the effect of urban sprawl on the local administration’s expenditure and particularly on the cost of basic public municipal services. These are obtained through a statistical model with cost functions that can assess the increase in spending prompted by urban sprawl for municipal current expenditure. The proposed model is novel in the field of urban planning and is based on a Bayesian hierarchical model with the ability to include modeling constraints among the expenditures variables and handle missing values accurately. With this paper, we show that urban sprawl has a significant and positive effect on the unit cost of local public services, which results in an inefficient urban growth model from the economic point of view. The effect is not transferred homogeneously to the budget. There are spending items that are more sensitive to urban sprawl like expenditure on security and public transportation and community wellbeing, which primarily covers waste collection, elimination, and treatment; sanitation, and water supply and distribution; road cleaning; and public lighting.

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The documentation of archeological sites requires the adoption of non‐destructive techniques to safeguard the unique legacy coming from prehistoric periods. This article tackles the assessment of lightfastness properties on a rock art site to determine the behavior of motif's color deterioration over time in Remígia Cave, Castellón (Spain), which is considered part of a UNESCO World Heritage Site. The measurements were performed using a microfade testing device to analyze the spectral characteristics and the aging properties of the colorant system and various substrates on site. Two scenarios have been identified depending on whether the lightness (L*) parameter of the rocky substrate changes or not in relation with the painted motifs. If the substrate remains stable without any change, red motifs containing iron oxide pigments will become more visible. If the substrate becomes lighter, the pigments will experience similar changes. Therefore, the contrast between paintings and support will be considerably enhanced.

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In this paper, we propose a novel approach to undertake the colorimetric camera characterization procedure based on a Gaussian process (GP). GPs are powerful and flexible nonparametric models for multivariate nonlinear functions. To validate the GP model, we compare the results achieved with a second-order polynomial model, which is the most widely used regression model for characterization purposes. We applied the methodology on a set of raw images of rock art scenes collected with two different Single Lens Reflex (SLR) cameras. A leave-one-out cross-validation (LOOCV) procedure was used to assess the predictive performance of the models in terms of CIE XYZ residuals and sans-serifΔEab* color differences. Values of less than 3 CIELAB units were achieved for sans-serifΔEab*. The output sRGB characterized images show that both regression models are suitable for practical applications in cultural heritage documentation. However, the results show that colorimetric characterization based on the Gaussian process provides significantly better results, with lower values for residuals and sans-serifΔEab*. We also analyzed the induced noise into the output image after applying the camera characterization. As the noise depends on the specific camera, proper camera selection is essential for the photogrammetric work.