A robust hierarchical clustering for georeferenced data

The detection of spatially contiguous clusters is a relevant task in geostatistics since near located observations might have similar features than distant ones. Spatially compact groups can also improve clustering results interpretation according to the different detected subregions. In this paper, we propose a robust metric approach to neutralize the effect of possible outliers, i.e. an exponential transformation of a dissimilarity measure between each pair of locations based on non-parametric kernel estimator of the direct and cross variograms (Fouedjio, 2016) and on a different bandwidth identification, suitable for agglomerative hierarchical clustering techniques applied to data indexed by geographical coordinates. Simulation results are very promising showing very good performances of our proposed metric with respect to the baseline ones. Finally, the new clustering approach is applied to two real-word data sets, both giving locations and top soil heavy metal concentrations

Gambling Disorders Among Young Women Regular Gamblers: The Unique and Common Contribution of Executive Thinking Style and Mindfulness

The aim of this study is to investigate the role played by mindfulness in the relationship between cognitive styles and gambling disorders in a sample of female young adults. Participants in this study (125 women; Mage = 18.64 years; SD = 1.7) were recruited in betting or bingo halls. They completed the South Oaks Gambling Screen, the Child and Adolescent Mindfulness Measure, and Sternberg's questionnaire on thinking styles. The results from the mediation analyses revealed that the executive thinking style increases gambling and that the deficit in mindfulness ability mediates this relationship. Theoretical and clinical implications are discussed

Fuzzy clustering of spatial interval-valued data

In this paper, two fuzzy clustering methods for spatial interval-valued data are proposed, i.e. the fuzzy C-Medoids clustering of spatial interval-valued data with and without entropy regularization. Both methods are based on the Partitioning Around Medoids (PAM) algorithm, inheriting the great advantage of obtaining non-fictitious representative units for each cluster. In both methods, the units are endowed with a relation of contiguity, represented by a symmetric binary matrix. This can be intended both as contiguity in a physical space and as a more abstract notion of contiguity. The performances of the methods are proved by simulation, testing the methods with different contiguity matrices associated to natural clusters of units. In order to show the effectiveness of the methods in empirical studies, three applications are presented: the clustering of municipalities based on interval-valued pollutants levels, the clustering of European fact-checkers based on interval-valued data on the average number of impressions received by their tweets and the clustering of the residential zones of the city of Rome based on the interval of price values

Assessing influence factors on daily ammonia and greenhouse gas concentrations from an open-sided cubicle barn in hot mediterranean climate

Measurement of gas concentrations constitutes basic knowledge for the computation of emissions from livestock buildings. Although it is well known that hot climate conditions increase gas emissions, in the literature the relation between gas concentrations from open barns and animalrelated parameters has not been investigated yet. This study aimed at filling this gap by evaluating daily gas concentrations within an open-sided barn in hot Mediterranean climate. The influence of microclimatic parameters (MC) and cow behavior and barn management (CBBM) were evaluated for ammonia (NH3 ), methane (CH4 ), and carbon dioxide (CO2 ) concentrations. Results showed that both MC and CBBM affected concentrations of NH3 (p < 0.02), CH4 (p < 0.001), and CO2 (p < 0.001). Higher values of NH3 concentration were detected during the cleaning of the floor by a tractor with scraper, whereas the lowest NH3 concentrations were recorded during animal lying behavior. Measured values of CO2 and CH4 were highly correlated (C = 0.87–0.89) due to the same sources of production (i.e., digestion and respiration). The different management of the cooling systems during the two observation periods reduced significantly CH4 concentrations in the barn when the cooling system in the feeding area was switched off. Based on methodological choices due to the specific barn typology, parameters related to animals can provide information on the variation of gas concentrations in the barn environment in hot climate conditions

Cross Sectional and Longitudinal Fuzzy Clustering of the NUTS and Positioning of the Italian Regions with Respect to the Regional Competitiveness Index (RCI) Indicators with Contiguity Constraints

In socio-economical clustering often the empirical information is represented by time-varying data generated by indicators observed over time on a set of subnational (regional) units. Usually among these units may exist contiguity relations, spatial but not only.In this paper we propose a fuzzy clustering model of multivariate time-varying data, the longitudinal fuzzy C-Medoids clustering with contiguity constraints. The temporal aspect is dealt with by using appropriate measures of dissimilarity between time trajectories. The contiguity among units is dealt with adding a contiguity matrix as a penalization term in the clustering model.The cross sectional fuzzy C-Medoids clustering with contiguity constraints is obtained considering one instant of time. The model is applied to the classification of the European NUTS on the basis of the observed dynamics of the Basic, Efficiency and Innovation subindexes of the Regional Competitiveness Index (RCI) 2013 and 2016. The positioning of the Italian regions is analyzed through the values of the medoids of the clusters and shows the peculiarities of the regions with respect to the subindexes either in single times or in the dynamic. Two contiguity constraints, one based on the European Western, Southern, Central and Northern geographic areas and one on the level of GDP—taken into account in the computation of the RCI—are also introduced in the models

The Central Laser Facility at the Pierre Auger Observatory

The Central Laser Facility is located near the middle of the Pierre Auger Observatory in Argentina. It features a UV laser and optics that direct a beam of calibrated pulsed light into the sky. Light scattered from this beam produces tracks in the Auger optical detectors which normally record nitrogen fluorescence tracks from cosmic ray air showers. The Central Laser Facility provides a "test beam" to investigate properties of the atmosphere and the fluorescence detectors. The laser can send light via optical fiber simultaneously to the nearest surface detector tank for hybrid timing analyses. We describe the facility and show some examples of its many uses.Comment: 4 pages, 5 figures, submitted to 29th ICRC Pune Indi

Fuzzy clustering with entropy regularization for interval-valued data with an application to scientific journal citations

In recent years, the research of statistical methods to analyze complex structures of data has increased. In particular, a lot of attention has been focused on the interval-valued data. In a classical cluster analysis framework, an interesting line of research has focused on the clustering of interval-valued data based on fuzzy approaches. Following the partitioning around medoids fuzzy approach research line, a new fuzzy clustering model for interval-valued data is suggested. In particular, we propose a new model based on the use of the entropy as a regularization function in the fuzzy clustering criterion. The model uses a robust weighted dissimilarity measure to smooth noisy data and weigh the center and radius components of the interval-valued data, respectively. To show the good performances of the proposed clustering model, we provide a simulation study and an application to the clustering of scientific journals in research evaluation

Solitary pulmonary nodules: Morphological and metabolic characterisation by FDG-PET-MDCT [Nodulo polmonare solitario: Caratterizzazione morfologico-metabolica mediante imaging integrato TCms/FDG-PET]

Purpose. This study was done to analyse the additional morphological and functional information provided by the integration of [18F]-2-fluoro- 2-deoxy-D-glucose positron emission tomography ([18F]-FDG-PET) with contrast-enhanced multidetector computed tomography (MDCT) in the characterisation of indeterminate solitary pulmonary nodules (SPNs). Materials and methods. Fifty-six SPNs, previously classified as indeterminate, were evaluated using a Discovery ST16 PET/CT system (GE Medical Systems) with nonionic iodinated contrast material and [18F]-FDG as a positron emitter. Images were evaluated on a dedicated workstation. Semiquantitative parameters of [18F]-FDG uptake and morphological, volumetric and densitometric parameters before and after contrast administration were analysed. Results were correlated with the histological and follow-up findings. Results. Twenty-six SPNs were malignant and 30 were benign. Malignant lesions at both PET/CT and histology had a mean diameter of 1.8±1.2 cm, a volume doubling time (DT) of 222 days, a mean standardized uptake value (SUV) of 4.7 versus 1.08 in benign lesions and a mean postcontrast enhancement of 44.8 HU as opposed to 4.8 HU in benign nodules. Malignant lesions had a significantly shorter doubling time and significantly greater postcontrast enhancement compared with benign nodules. Based on the SUV and using a cut-off value of >2.5, PET/CT had a sensitivity of 76.9%, specificity of 100%, diagnostic accuracy of 89.2%, positive predictive value (PPV) of 100% and negative predictive value (NPV) of 83.3%. Based on doubling time (cut off <400 days), it had a sensitivity of 76.9%, specificity of 93.3%, accuracy of 85.7%, PPV of 90.9% and NPV of 82.3%. Based on postcontrast enhancement (cut off >15 HU), it had a sensitivity of 92.3%, specificity of 100%, accuracy of 96.4%, PPV of 100% and NPV of 93.7%. Conclusion. PET/CT allows accurate analysis of anatomical/morphological and metabolic/functional correlations of SPN, providing useful data for identifying and locating the disease, for differentiating between malignant and benign nodules and for establishing the aggressiveness and degree of vascularity of pulmonary lesions. Therefore, partly in view of the considerable reduction in time and cost of the single examinations, we believe that PET/CT will gain an increasingly dominant role in the diagnostic and therapeutic approach to lung cancer, especially in the preclinical phase. © 2007 Springer-Verlag

Analysing cluster evolution using repeated cross-sectional ordinal data

This study contributes to the existing literature on tourism market segmentation by providing a new matching-clustering procedure that allows patterns of behaviours to be identified using repeated cross-sectional surveys. By extracting equivalent samples over time, the matching method allows inter-temporal cluster analyses to be performed so that a deeper insight into a phenomenon can be obtained beyond the traditional aggregate level of understanding. The paper provides a step-by-step description of the matching-clustering procedure that can be easily replicated, both within and outside the tourism field, when repeated cross-sectional data are available. From a practical and managerial perspective, the proposed procedure helps destination managers and municipal- ities to describe and verify the efficacy of policy and strategies adopted over years without the necessity to rely on longitudinal surveys, which are often difficult to conduct