1,720,963 research outputs found
Sampling design optimization for geostatistical modelling and prediction
No full textSpace-time monitoring and prediction of environmental variables requires measurements of the environment. But environmental variables cannot be measured everywhere and all the time. Scientists can only collect a fragment, a sample of the property of interest in space and time, with the objective of using this sample to infer the property at unvisited locations and times. Sampling might be a costly and time consuming affair. Consequently, we need efficient strategies to select an optimal design for mapping. Most studies on sampling design optimization consider the case of predictive mapping using geostatistics. In recent years geostatistical models and associated mapping techniques have advanced, which calls for adaptation of associated sampling designs. The main objective of this thesis is to address the optimal design of four recent advances in mapping. Chapter 3 explores sampling design optimization for the non-stationary variance geostatistical model defined in Chapter 2. Accounting for non-stationarity in the variance of environmental properties in complex landscapes leads to better quantification of the mapping uncertainty. This is applied in a case study mapping daily rainfall in the north of England, and optimizing the rain-gauges for mapping. It is shown that rainfall prediction benefits from a model that includes non-stationarity in the mean and variance, as shown by the likelihood and Akaike Information Criterion statistics. The optimization of the rain gauge network is achieved by spatial simulated annealing. The optimized rain gauge network improves slightly the rainfall mapping accuracy. The accuracy gain is limited because I used a static design for all time steps, while the areas with larger prediction uncertainty vary day-by-day. The optimized design also shows a specific spatial pattern, with a fairly uniform spatial distribution but an increased density in areas where the residual variance is large. I further test an optimized design using a reduction of 10% of the total number of rain-gauges. The optimized design shows a significant improvement over the original design using all rain-gauges. I conclude that 10% of the rain-gauges may be removed (e.g. to save costs) without loss of mapping accuracy, provided that the rain-gauges are placed optimally. Chapter 4 investigates the use of simple sampling strategies to account for a criterion that encompasses both prediction error variance and variogram parameter uncertainty in geostatistical mapping of soil properties. I test two sampling designs: spatial coverage and spatial coverage supplemented by a subset of close-pairs units, and compare these to a design optimized for this criterion. I show that a spatial coverage design performs poorly for mapping using ordinary kriging because of the lack of information at short distances to estimate the variogram parameters. This is valid for series of estimated variogram parameters of a Matèrn function. An optimized design performs always slightly better, but has several disadvantages. For example, it requires the variogram parameters to be known. It also involves defining an objective function characterizing the total error, and minimizing this error using optimization algorithms. In contrast, a spatial coverage design supplemented by a subset of close-pair units offers accurate results for most variograms tested. I therefore recommend to use the latter design for designing a geostatistical survey, unless prior knowledge of the variogram is available (e.g. an average variogram). If an average variogram is available for the property of interest, it can be used to optimize the design. I further test the minimum number of units required to estimate the variogram of a geostatistical survey, and show that it strongly depends on the degree of spatial correlation of the target variable. For large values of the variogram effective range and small nugget to sill ratios, it is shown that only 15 units are enough to make geostatistical analysis worthwhile, i.e. more accurate than a design-based estimate. Mapping is not always performed using geostatistical methods. There is growing interest towards mapping using data-driven, non-linear machine learning techniques. The objective of Chapter 5 is to extend our knowledge on sampling optimization for mapping using random forest, and to compare it to conventional sampling designs. I tested the methodology in a potential application scenarios, mapping topsoil organic carbon at European scale using measurements of the LUCAS dataset as population of interest. I demonstrate that an optimized design is always more accurate than other common designs, but possible to obtain only when subsampling an existing dataset with known values of the soil property at all locations. By comparing the mean square error (MSE) of the maps obtained by an optimized design with the those obtained by common designs, it is shown that optimizing a design in terms of MSE is not always worthwhile. When the sample size increases, the maps produced by the different designs converge to similar accuracy values. In a case study on large scale soil organic carbon mapping, a sampling density greater than 1 sampling unit per 4000 km2 decreases markedly the difference in term of average MSE between designs. A design optimized for the mean squared shortest standardized distance in the feature space has the closest match with the optimized design in terms of MSE. By analysing the distribution of the sampling locations in both geographic and feature space, I further show that the optimized design is not spread in the geographic space, but seems to be spread somewhat uniformly in the feature space, and especially in the most important covariates of the machine learning model. It is however difficult to draw further conclusions because of the complex spread of the units in feature space. Further research is needed in this direction. Sampling design optimization becomes more complex when the ultimate goal is to provide a map used as input for a model whose output is the main interest. This is done by integrating geostatistics for mapping rainfall and Bayesian calibration of a hydrological model for predicting discharges in Chapter 6. The Bayesian calibration enables to capture model input, initial state, parameter and structural uncertainty, while also taking uncertainties in the output measurements into account. In a case study predicting river discharge using a rainfall-runoff model and maps of rainfall as input, a single rain gauge is sufficient to obtain accurate model parameter calibration and discharge predictions. Adding up to five rain gauges improves the model prediction. Adding even more only produces a marginal improvement of the prediction accuracy. Calibrating the rainfall time series as additional parameters leads to more accurate model performance compared to the case where rainfall uncertainty is not updated using discharge measurements. Furthermore, it is demonstrated for the case study that model parameter uncertainty is the main contributor to the posterior discharge uncertainty and that input uncertainty has a relatively small contribution. However, the study also shows that Bayesian calibration of rainfall has serious computational disadvantages. In particular, calibrating a large number of rainfall input parameters remains a serious challenge. The thesis synthesis is given in Chapter 7. It discusses the findings of this thesis, compares these with existing literature, gives directions for future research and provides a personal reflection on sampling design optimization practices. On the basis of this thesis, I conclude that there is no single best optimal design. It is very much case dependent. It depends, among others, on: (i) the assumed model of spatial variation, (ii) the assumption whether we need or need not estimate the parameters from the data, and (iii) the criterion that is used to optimize the sampling configuration. This thesis shows that the choice of the criterion has a serious impact on the optimized design. In practice we may not know the three elements listed above. This is typically the case at the start of a project when no previous data or expertise are available but where we need to design a survey. In this case, it is sensible to use some rules of thumb to design a survey for mapping. Chapter 7 provides some basis for this. This thesis makes a step towards derivation of optimal designs for novel mapping techniques, with case studies on mapping soil and hydrological variables. But it also shows that we are just at the beginning of this specific field of science. In recent years, there has been a large increase in complexity of techniques and models used for mapping. We make more use of spatially explicit covariate information, such as remote sensing imagery, and measurements are increasingly inferred rather than measured. Mapping techniques have become more data-driven and non-linear, increasing de facto the complexity of the sampling designs that should accompany such developments. Because sampling is the basis of mapping and has a large impact on cost and accuracy, this research field will remain as important as ever in geostatistics and spatial modelling
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Sampling design optimization for soil mapping with random forest
No full textMachine learning techniques are widely employed to generate digital soil maps. The map accuracy is partly determined by the number and spatial locations of the measurements used to calibrate the machine learning model. However, determining the optimal sampling design for mapping with machine learning techniques has not yet been considered in detail in digital soil mapping studies. In this paper, we investigate sampling design optimization for soil mapping with random forest. A design is optimized using spatial simulated annealing by minimizing the mean squared prediction error (MSE). We applied this approach to mapping soil organic carbon for a part of Europe using subsamples of the LUCAS dataset. The optimized subsamples are used as input for the random forest machine learning model, using a large set of readily available environmental data as covariates. We also predicted the same soil property using subsamples selected by simple random sampling, conditioned Latin Hypercube sampling (cLHS), spatial coverage sampling and feature space coverage sampling. Distributions of the estimated population MSEs are obtained through repeated random splitting of the LUCAS dataset, serving as the population of interest, into subsets used for validation, testing and selection of calibration samples, and repeated selection of calibration samples with the various sampling designs. The differences between the medians of the MSE distributions were tested for significance using the non-parametric Mann-Whitney test. The process was repeated for different sample sizes. We also analyzed the spread of the optimized designs in both geographic and feature space to reveal their characteristics. Results show that optimization of the sampling design by minimizing the MSE is worthwhile for small sample sizes. However, an important disadvantage of sampling design optimization using MSE is that it requires known values of the soil property at all locations and as a consequence is only feasible for subsampling an existing dataset. For larger sample sizes, the effect of using an MSE optimized design diminishes. In this case, we recommend to use a sample spread uniformly in the feature (i.e. covariate) space of the most important random forest covariates. The results also show that for our case study, cLHS sampling performs worse than the other sampling designs for mapping with random forest. We stress that comparison of sampling designs for calibration by splitting the data just once is very sensitive to the data split that one happens to use if the validation set is small
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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