1,721,134 research outputs found
Spatial Sampling for Non-compact Patterns
Full text linkThe objective of spatial sampling is to collect subsets of individuals from a population in the two-dimensional space, in order to estimate some population characteristics. Traditional sampling techniques are accordingly enriched to keep space into account. We consider sequential techniques that use weights for introducing space in the update of population units' inclusion probabilities and propose a new weighting system that includes the spatial entropy of the study variable. Techniques only based on distances between locations perform well in the case of a compact structure. Any non-compact spatial scheme takes advantage of the involvement of spatial entropy in the sequential modification of first-order inclusion probabilities
Sample Redesign of the Italian Consumer Expenditure Survey
No full textThe paper presented the design of the current Italian consumer expenditure survey and a sample redesign for the same survey with a possible rotation scheme of the primary sampling units (PSU
Le prime frequentazioni umane della Versilia e delle Alpi Apuane alla luce delle ultime scoperte
No full textMagnetoresistance in triphenyl-diamine derivative blue organic light emitting devices
No full textCopyright 2008 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. This article appeared in Journal of Applied Physics 103, 043706 (2008) and may be found at
Advances in spatial entropy measures
Full text linkA very recent proposal of a set of entropy measures for spatial data, based on building pairs of realizations, allows to split the data heterogeneity that is usually assessed via Shannon's entropy into two components: spatial mutual information, identifying the role of space, and spatial residual entropy, measuring heterogeneity due to other sources. A further decomposition into partial terms deeply investigates the role of space at specific distance ranges. The present work proposes improvements to the method and adds relevant results proving that the new set of spatial entropies satisfies a list of desirable properties. We extend the methodology to sets of realizations greater than pairs. We also show that the approach is more general, better performing and more interpretable than the most popular proposals in the literature, thanks to the property of additivity and a new way of computing entropy that explicitly discards the order within sets. A novel procedure for building the necessary quantities for computations is also provided. A comparative study illustrates the superior performance of the new set of measures over representative spatial configurations. Practical questions are answered by means of a case study on land use data
Effects of insulin and ghrelin on AMP-activated protein kinase (AMPK) activity in osteoblast-like cells
No full textUna grotticella sepolcrale eneolitica con vaso campaniforme scoperta presso Vecchiano (Pisa)
No full textA new approach to spatial entropy measures
Full text linkEntropy is widely employed in many applied sciences to measure the heterogeneity of observations. Recently, many attempts have been made to build entropy measures for spatial data, in order to capture the influence of space over the variable outcomes. The main limit of these developments is that all indices are computed conditional on a single distance and do not cover the whole spatial configuration of the phenomenon under study. Moreover, most of them do not satisfy the desirable additivity property between local and global spatial measures. This work reviews some recent developments, based on univariate distributions, and compares them to a new approach which considers the properties of entropy measures linked to bivariate distributions. This perspective introduces substantial innovations. Firstly, Shannon’s entropy may be decomposed into two terms: spatial mutual information, accounting for the role of space in determining the variable outcome, and spatial global residual entropy, summarizing the remaining heterogeneity carried by the variable itself. Secondly, these terms both satisfy the additivity property, being sums of partial entropies measuring what happens at different distance classes. The proposed indices are used for measuring the spatial entropy of a marked point pattern on rainforest tree species. The new entropy measures are shown to be more informative and to answer a wider set of questions than the current proposals of the literature
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