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Point Locations (point + locations)
Selected AbstractsThe role of floristic survey data and quantitative analysis in identification and description of ecological communities under threatened species legislation: A case study from north-eastern New South WalesECOLOGICAL MANAGEMENT & RESTORATION, Issue 2009Penny Kendall Summary The concept of ecological communities play an important role in conservation planning and natural resource management. However, inherent uncertainties in the definition and identification of individual communities make it difficult to assess whether particular communities are present on particular sites and how they may be affected by proposed developments or management actions. If communities are poorly defined or misidentified, they may not perform their intended role as effective representations of biodiversity. We use a case study of forest communities dominated by Brown Barrel (Eucalyptus fastigata) in north-eastern New South Wales to demonstrate the value of quantitative floristic survey data for resolving robust and effective classifications of communities. Numerical analyses of an extensive set of floristic data suggested a re-configuration of a prior classification based largely on subjective interpretation. Although the general existence of assemblages dominated by Brown Barrel was confirmed, the new classification replaced three prior units with two assemblages that were more robust and better reflected the overall patterns in species composition. As only one of the two assemblages potentially warranted threatened status, the new classification allows scarce conservation resources to be targeted where they are most needed. The quantitative survey data also enabled a more detailed floristic description of the assemblages and provided a basis for maps of point locations and modelled habitat. These maps identified previously undocumented occurrences of the communities and helped to assess their extent of decline since European settlement. Improving the coverage of quadrat-based floristic sampling is therefore a valuable and cost-effective investment to inform better management of native vegetation and biodiversity. [source] Quantifying the Effects of Mask Metadata Disclosure and Multiple Releases on the Confidentiality of Geographically Masked Health DataGEOGRAPHICAL ANALYSIS, Issue 1 2008Dale L. Zimmerman The availability of individual-level health data presents opportunities for monitoring the distribution and spread of emergent, acute, and chronic conditions, as well as challenges with respect to maintaining the anonymity of persons with health conditions. Particularly when such data are mapped as point locations, concerns arise regarding the ease with which individual identities may be determined by linking geographic coordinates to digital street networks, then determining residential addresses and, finally, names of occupants at specific addresses. The utility of such data sets must therefore be balanced against the requirements of protecting the confidentiality of individuals whose identities might be revealed through the availability of precise and accurate locational data. Recent literature has pointed toward geographic masking as a means for striking an appropriate balance between data utility and confidentiality. However, questions remain as to whether certain characteristics of the mask (mask metadata) should be disclosed to data users and whether two or more distinct masked versions of the data can be released without breaching confidentiality. In this article, we address these questions by quantifying the extent to which the disclosure of mask metadata and the release of multiple masked versions may affect confidentiality, with a view toward providing guidance to custodians of health data sets. The masks considered include perturbation, areal aggregation, and their combination. Confidentiality is measured by the areas of confidence regions for individuals' locations, which are derived under the probability models governing the masks, conditioned on the disclosed mask metadata. [source] An algorithm for fast optimal Latin hypercube design of experimentsINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 2 2010Felipe A. C. Viana Abstract This paper presents the translational propagation algorithm, a new method for obtaining optimal or near optimal Latin hypercube designs (LHDs) without using formal optimization. The procedure requires minimal computational effort with results virtually provided in real time. The algorithm exploits patterns of point locations for optimal LHDs based on the ,p criterion (a variation of the maximum distance criterion). Small building blocks, consisting of one or more points each, are used to recreate these patterns by simple translation in the hyperspace. Monte Carlo simulations were used to evaluate the performance of the new algorithm for different design configurations where both the dimensionality and the point density were studied. The proposed algorithm was also compared against three formal optimization approaches (namely random search, genetic algorithm, and enhanced stochastic evolutionary algorithm). It was found that (i) the distribution of the ,p values tends to lower values as the dimensionality is increased and (ii) the proposed translational propagation algorithm represents a computationally attractive strategy to obtain near optimum LHDs up to medium dimensions. Copyright © 2009 John Wiley & Sons, Ltd. [source] Interpreting variability in global SST data using independent component analysis and principal component analysisINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 3 2010Seth Westra Abstract Component extraction techniques are used widely in the analysis and interpretation of high-dimensional climate datasets such as global sea surface temperatures (SSTs). Principal component analysis (PCA), a frequently used component extraction technique, provides an orthogonal representation of the multivariate dataset and maximizes the variance explained by successive components. A disadvantage of PCA, however, is that the interpretability of the second and higher components may be limited. For this reason, a Varimax rotation is often applied to the PCA solution to enhance the interpretability of the components by maximizing a simple structure. An alternative rotational approach is known as independent component analysis (ICA), which finds a set of underlying ,source signals' which drive the multivariate ,mixed' dataset. Here we compare the capacity of PCA, the Varimax rotation and ICA in explaining climate variability present in globally distributed SST anomaly (SSTA) data. We find that phenomena which are global in extent, such as the global warming trend and the El Niño-Southern Oscillation (ENSO), are well represented using PCA. In contrast, the Varimax rotation provides distinct advantages in interpreting more localized phenomena such as variability in the tropical Atlantic. Finally, our analysis suggests that the interpretability of independent components (ICs) appears to be low. This does not diminish the statistical advantages of deriving components that are mutually independent, with potential applications ranging from synthetically generating multivariate datasets, developing statistical forecasts, and reconstructing spatial datasets from patchy observations at multiple point locations. Copyright © 2009 Royal Meteorological Society [source] Wavelet analysis for detecting anisotropy in point patternsJOURNAL OF VEGETATION SCIENCE, Issue 2 2004Michael S. Rosenberg Although many methods have been proposed for analysing point locations for spatial pattern, previous methods have concentrated on clumping and spacing. The study of anisotropy (changes in spatial pattern with direction) in point patterns has been limited by lack of methods explicitly designed for these data and this purpose; researchers have been constrained to choosing arbitrary test directions or converting their data into quadrat counts and using methods designed for continuously distributed data. Wavelet analysis, a booming approach to studying spatial pattern, widely used in mathematics and physics for signal analysis, has started to make its way into the ecological literature. A simple adaptation of wavelet analysis is proposed for the detection of anisotropy in point patterns. The method is illustrated with both simulated and field data. This approach can easily be used for both global and local spatial analysis. [source] | ||||||||||