A Practical Approach to choosing Formulae for Economic Index Numbers

Traditional axiomatic, economic and stochastic approaches to choosing a formula for economic index numbers are based on theoretical considerations, with little regard to whether they reflect the actual circumstances affecting the markets to which they relate. This paper presents an approach to index number formulation based on how markets operate and presents a general, parameter-based formula for price and quantity indices. Two variants of this general formula cater for the “substitution effect” in different ways and one of these variants provides a practical expression for an economic-theoretic index based on purchasers' revealed preferences. Another variant applies this approach to short-term inflation indices. A final variant provides a straightforward means of estimating purchasing power parities for spatial indices. The analysis also emphasises the importance in National Accounts of using coherent price and quantity indices, whose products generate the corresponding value indices.

Predicting symptoms of downy mildew, powdery mildew, and gray mold diseases of grapevine through machine learning

Downy mildew, powdery mildew, and gray mold are major diseases of grapevine with a strong negative impact on fruit yield and fruit quality. These diseases are controlled by the application of chemicals, which may cause undesirable effects on the environment and on human health. Thus, monitoring and forecasting crop disease is essential to support integrated pest management (IPM) measures. In this study, two tree-based machine learning (ML) algorithms, random forest and C5.0, were compared to test their capability to predict the appearance of symptoms of grapevine diseases, considering meteorological conditions, spatial indices, the number of crop protection treatments and the frequency of monitoring days in which symptoms were recorded in the previous year. Data collected in Tuscany region (Italy), on the presence of symptoms on grapevine, from 2006 to 2017 were divided with an 80/20 proportion in training and test set, data collected in 2018 and 2019 were tested as independent years for downy mildew and powdery mildew. The frequency of symptoms in the previous year and the cumulative precipitation from April to seven days before the monitoring day were the most important variables among those considered in the analysis for predicting the occurrence of disease symptoms. The best performance in predicting the presence of symptoms of the three diseases was obtained with the algorithm C5.0 by applying (i) a technique to deal with imbalanced dataset (i.e., symptoms were detected in the minority of observations) and (ii) an optimized cut-off for predictions. The balanced accuracy achieved in the test set was 0.8 for downy mildew, 0.7 for powdery mildew and 0.9 for gray mold. The application of the models for downy mildew and powdery mildew in the two independent years (2018 and 2019) achieved a lower balanced accuracy, around 0.7 for both the diseases. Machine learning models were able to select the best predictors and to unravel the complex relationships among geographic indices, bioclimatic indices, protection treatments and the frequency of symptoms in the previous year.

Assessing Ecosystem and Urban Services for Landscape Suitability Mapping

Ecosystem services (ES) and urban services (US) can comparably improve human well-being. Models for integrating ES and US with unexpressed and objective needs of defined groups of stakeholders may prove helpful for supporting decisions in landscape planning and management. In fact, they could be applied for highlighting landscape areas with different characteristics in terms of services provided. From this base, a suitability spatial assessment model (SUSAM) was developed and applied in a study area considering different verisimilar scenarios that policy makers could analyse. Each scenario is based on the prioritization of a set of services considering a defined group of stakeholders. Consistent and comparable ES and US indices of spatial benefiting areas (SBA) of services were calculated using GIS spatialization techniques. These indices were aggregated hierarchically with the relevance of services according to a spatial multicriteria decision analysis (S-MCDA). Results include maps for each scenario showing detailed spatial indices of suitability that integrate the local availability of SBA of ES and US, along with their relevance. The results were compared with known landscape classes identified in previous studies, which made it possible to interpret the spatial variation of suitability in the light of known landscape features. A complete sensitivity analysis was performed to test the sensitiveness of the model’s outputs to variations of judgements and their resistance to the indicators’ variation. The application of the model demonstrated its effectiveness in a landscape suitability assessment. At the same time, the sensitivity analysis and helping to understand the model behaviour in the different landscape classes also suggested possible solutions for simplifying the whole methodology.

Role OF Structural Indices and Other Spatial Measures in the Improvement Of Crown Variables Prediction For Lannea fructicosa

Theoretically, the stem of the tree must be strong enough to withstand the forces that act on it. These forces include the weight of the crown and the drag exerted on it by the wind. This mean that for a well-established root system, there should be some kind of balance between crown and stem sizes, otherwise the stem be break. The sizes, shapes and relative locations of crowns both determine and respond to the shading and constriction effects that characterize aboveground interactions between trees. Due to this kind of balance, tree crown parameters have been used as predictor variables in diameter and height growth equations. Although the correlation between tree variables and crown dimensions has well documented in the literature, other stand composition and conditions such as competition, elevation and aspect are believed to be among the unexplained forces that exert strong influences on the accuracy of the allometric models used for that relationship. This study attempted to quantify the effect of structural indices and other spatial measures to improve the prediction of crown radius and crown length for trees in natural woodlands. Field data were recorded for Lannea fruticosa tree species that naturally grown in Elgarri forest reserve in Blue Nile State, Sudan. The data was used to test the performance of estimating crown dimensions on the basis of allometric relationships with tree diameter and height. A total of thirteen spatial and non-spatial indices were incorporated into modified crown dimension models. Coefficient of multiple determination (R2) and relative bias were used to test the performance of these indices in improving the accuracy of estimates. According to the results all predictions of crown length and radius were found to be better after the incorporation of the spatial and non-spatial, with positive R2 gain and acceptable negative bias values for crown radius and positive ones for crown length. For all cases, the spatial indices were found to be better than the non-spatial ones.

Efficient synthetic generation of ecological data with preset spatial association of individuals

Many experiments are not feasible to be conducted as factorials. Simulations using synthetically generated data are viable alternatives to such factorial experiments. The main objective of the present research is to develop a methodology and a platform to synthetically generate spatially explicit forest ecosystems represented by points with a predefined spatial pattern. Using algorithms with polynomial complexity and parameters that control the number of clusters, the degree of clusterization, and the proportion of nonrandom trees, we show that spatially explicit forest ecosystems can be generated time efficiently, which enable large factorial simulations. The proposed method was tested on 1200 synthetically generated forest stands, each of 25 ha, using 10 spatial indices: Clark-Evans aggregation index, Ripley’s K, Besag’s L, Morisita’s dispersion index, Greig-Smith index, the size dominance index of Hui, index of nonrandomness of Pielou, directional index and mean directional index of Corral-Rivas, and size differentiation index of Von Gadow. The size of individual trees was randomly generated aiming at variograms like real forests. We obtained forest stands with the expected spatial arrangement and distribution of sizes in less than one hour. To ensure replicability of the study we have provided a free fully functional software that executes the stated tasks.

Quantification of spatial tumor heterogeneity in immunohistochemistry staining images

Motivation Quantitative Immunofluorescence (QIF) is often used for immunohistochemistry (IHC) quantification of proteins that serve as cancer biomarkers. Advanced image analysis systems for pathology allow capturing expression levels in each individual cell or subcellular compartment. However, only the Mean Signal Intensity (MSI) within the cancer tissue region of interest is usually considered as biomarker completely ignoring the issue of tumor heterogeneity. Results We propose using IHC image-derived information on the spatial distribution of cellular signal intensity (CSI) of protein expression within the cancer cell population to quantify both mean expression level and tumor heterogeneity of CSI levels. We view CSI levels as marks in a marked point process of cancer cells in the tissue and define spatial indices based on conditional mean and conditional variance of the marked point process. The proposed methodology provides objective metrics of cell-to-cell heterogeneity in protein expressions that allow discriminating between different patterns of heterogeneity. The prognostic utility of new spatial indices is investigated and compared to the standard MSI biomarkers using the protein expressions in tissue microarrays (TMAs) incorporating tumor tissues from1000+ breast cancer patients. Supplementary information Supplementary data are available at Bioinformatics online.