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Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

COMPUTErS & GEOSCIENCES 2011年第3期37卷 382-389页

作者： Leibovici, D. G. Bastin, L. Jackson, M. Univ Nottingham Ctr Geospatial Sci Nottingham NG7 2RD England Aston Univ Sch Engn & Appl Sci Birmingham B4 7ET W Midlands England

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Spatio-temporal data Spatial statistics Co-occurrences Multivariate data Marked point process regression trees Spatial entropy r programming

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Expedient Metrics to Describe Plant Community Change Across Gradients of Anthropogenic Influence

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ENVIrONMENTAL MANAGEMENT 2014年第5期54卷 1121-1130页

作者： Marcelino, Jose A. P. Weber, Everett Silva, Luis Garcia, Patricia V. Soares, Antonio O. Univ Azores Dept Biol CIRN P-9501801 Ponta Delgada Azores Portugal Univ Azores Biodivers Grp Azores GBA P-9700042 Ponta Delgada Azores Portugal Murray State Univ Dept Biol Murray KY 42071 USA Univ Acores Dept Biol Ctr Invest Biodiversidade & Recursos Genet CIBIO InBIO P-9500321 Ponta Delgada Azores Portugal

Human influence associated with land use may cause considerable biodiversity losses, namely in oceanic islands such as the Azores. Our goal was to identify plant indicator species across two gradients of increasing anthropogenic influence and management (arborescent and herbaceous communities) and determine similarity between plant communities of uncategorized vegetation plots to those in reference gradients using metrics derived from r programming. We intend to test and provide an expedient way to determine the conservation value of a given uncategorized vegetation plot based on the number of native, endemic, introduced, and invasive indicator species present. Using the metric IndVal, plant taxa with a significant indicator value for each community type in the two anthropogenic gradients were determined. A new metric, ComVal, was developed to assess the similarity of an uncategorized vegetation plot toward a reference community type, based on (i) the percentage of pre-defined indicator species from reference communities present in the vegetation plots, and (ii) the percentage of indicator species, specific to a given reference community type, present in the vegetation plot. Using a data resampling approach, the communities were randomly used as training or validation sets to classify vegetation plots based on ComVal. The percentage match with reference community types ranged from 77 to 100 % and from 79 to 100 %, for herbaceous and arborescent vegetation plots, respectively. Both IndVal and ComVal are part of a suite of useful tools characterizing plant communities and plant community change along gradients of anthropogenic influence without a priori knowledge of their biology and ecology.

关键词： Plant community ecology Indicator species Change detection r programming Metrics

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COMPUTATIONAL PLATFOrM FluorSimStudio FOr PrOCESSING KINETIC CUrVES OF FLUOrESCENCE DECAY USING SIMULATION MODELING AND DATA MINING ALGOrITHMS

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JOUrNAL OF APPLIED SPECTrOSCOPY 2021年第3期88卷 571-579页

作者： Yatskou, M. M. Apanasovich, V. V. Belarusian State Univ Minsk 220030 BELARUS

Herein, a computational platform FluorSimStudio was developed for processing fluorescence decay curves in molecular systems, which implements the concept of complex analysis of experimental information based on the simulation modeling and data mining methods. Data analysis includes partitioning the fluorescence decay curves into clusters according to the degree of likeness in some measure of similarity, finding the median cluster members (medoids), applying the data reduction method and visualizing the experimental data in a two-dimensional space. Analysis of the decay curves is carried out by the analytical or simulation models of optical processes occurring in molecular systems. The visualization of data clusters in the original and transformed time spaces is done with the aim of user interaction. A functional scheme of the platform is proposed, the choice of software for ensuring high computing performance is substantiated, a web application of the platform is implemented (https://***/ FluorSimStudio), and the results of a comparative analysis of the simulation algorithms are presented. The performance of the computational platform was confirmed by examples of the analysis of data sets representing systems of free fluorophores and in the presence of the Forster electronic excitation energy transfer process. The computational platform is an open system and allows permanent addition of complex analysis models, taking into account the development of new algorithms for modeling the energy transfer processes in molecular systems, studied with the use of time-resolved fluorescence spectroscopy systems.

关键词： fluorescence spectroscopy fluorescence decay simulation modeling data mining computational platform software development r programming

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PbIso: an r package and web app for calculating and plotting Pb isotope data

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CANADIAN JOUrNAL OF EArTH SCIENCES 2024年第1期61卷 1-15页

作者： Armistead, Sheree E. Eglington, Bruce M. Pehrsson, Sally J. Univ Tasmania Ctr Ore Deposit & Earth Sci CODES Hobart Tas Australia Nat Resources Canada Geol Survey Canada Ottawa ON Canada Laurentian Univ Met Earth Harquail Sch Earth Sci Sudbury ON Canada Univ Saskatchewan Dept Geol Sci Saskatoon SK Canada

The package PbIso is a free and open r toolbox for commonly used calculations and plots of Pb-Pb isotope data and for generating Pb evolution models. In this paper, we review Pb isotope systematics and the calculations that are commonly used, such as model age, model source mu (U-238/Pb-204), time-integrated kappa (Th-232/U-238), and initial Pb isotope ratios. These equations are implemented into r functions in the package PbIso. In addition, functions are provided for generating Pb evolution models, paleoisochrons, and isochrons. This allows users to apply calculations to their data in a straightforward way while providing transparency and flexibility of the calculations used. We have implemented some basic features of the PbIso package into an online shiny r application (see https://***/software/pbiso), which makes it easy for users without any r experience to use these calculations with their own data and to generate plots. We have provided a case study from the Superior Province in Canada, showing how different Pb evolution models can be generated in PbIso and compared to Pb isotope data.

关键词： Pb isotopes early earth isotope modelling Superior Province r programming

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Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region: The role of land use, soil texture, topographic indices and the influence of remote sensing data to modelling

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SCIENCE OF THE TOTAL ENVIrONMENT 2017年 601卷 821-832页

作者： Schillaci, Calogero Acutis, Marco Lombardo, Luigi Lipani, Aldo Fantappie, Maria Maerker, Michael Saia, Sergio Univ Milan Dept Agr & Environm Sci Milan Italy Univ Tubingen Dept Geosci Tubingen Germany King Abdullah Univ Sci & Technol PSE Div Thuwal Saudi Arabia King Abdullah Univ Sci & Technol CEMSE Div Thuwal Saudi Arabia TU Wien Inst Software Technol & Interact Syst Vienna Austria Ctr Agrobiol & Pedol CREA ABP Council Agr Res & Econ CREA Florence Italy Univ Pavia Dept Earth & Environm Sci Pavia Italy Cereal Res Ctr CREA CER Council Agr Res & Econ CREA Foggia Italy

SOC is the most important indicator of soil fertility and monitoring its space-time changes is a prerequisite to establish strategies to reduce soil loss and preserve its quality. Here we modelled the topsoil (0-0.3m) SOC concentration of the cultivated area of Sicily in 1993 and 2008. Sicily is an extremely variable region with a high number of ecosystems, soils, and microclimates. We studied the role of time and land use in the modelling of SOC, and assessed the role of remote sensing (rS) covariates in the boosted regression trees modelling. The models obtained showed a high pseudo-r-2 (0.63-0.69) and low uncertainty (s.d. < 0.76 g C kg(-1) with rS, and < 1.25 g C kg(-1) without rS). These outputs allowed depicting a time variation of SOC at 1 arcsec. SOC estimation strongly depended on the soil texture, land use, rainfall and topographic indices related to erosion and deposition. rS indices captured one fifth of the total variance explained, slightly changed the ranking of variance explained by the non-rS predictors, and reduced the variability of the model replicates. During the study period, SOC decreased in the areas with relatively high initial SOC, and increased in the area with high temperature and low rainfall, dominated by arables. This was likely due to the compulsory application of some Good Agricultural and Environmental practices. These results confirm that the importance of texture and land use in short-term SOC variation is comparable to climate. The present results call for agronomic and policy intervention at the district level to maintain fertility and yield potential. In addition, the present results suggest that the application of rS covariates enhanced the modelling performance. (C) 2017 Elsevier B.V. All rights reserved.

关键词： SOC mapping Space-time SOC variation Agro-ecosystems r programming Digital soil mapping Legacy dataset

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Benefits of Doing Generalizability Theory Analyses within Structural Equation Modeling Frameworks: Illustrations Using the rosenberg Self-Esteem Scale

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STrUCTUrAL EQUATION MODELING-A MULTIDISCIPLINArY JOUrNAL 2024年第1期31卷 165-181页

作者： Vispoel, Walter P. Hong, Hyeri Lee, Hyeryung Univ Iowa Iowa City IA 52242 USA Calif State Univ Fresno Fresno CA USA

Although generalizability theory (GT) designs typically are analyzed using analysis of variance (ANOVA) procedures, they also can be integrated into structural equation models (SEMs). In this tutorial, we review basic concepts for conducting univariate and multivariate GT analyses and demonstrate advantages of doing such analyses within SEM frameworks using multi-occasion data from the rosenberg Self-Esteem Scale. We show how GT-SEMs can reproduce variance components for both relative and absolute error obtained from ANOVA models, estimate effects of changes made to measurement procedures and universes of generalization, incorporate estimation methods to correct for scale coarseness, represent essential tau-equivalent or congeneric relationships, include additional method factors for negatively and positively worded items, incorporate bifactor designs, allow for formal tests of model fit when warranted, and derive Monte Carlo confidence intervals for key parameters of interest. We provide code for conducting the demonstrated analyses using several statistical packages in extended online.

关键词： Generalizability theory rosenberg self-esteem scale r programming structural equation modeling

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Integration of Multivariate Adaptive regression Splines and Weighted Arithmetic Water Quality Index Methods for Drinking Water Quality Analysis

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WATEr CONSErVATION SCIENCE AND ENGINEErING 2024年第1期9卷 6页

作者： Jumber, Marshet B. Damtie, Menwagaw T. Tegegne, Desalegn Debre Tabor Univ Hydraul & Water Resources Engn Debre Tabor 272 Ethiopia Int Water Management Inst IWMI Addis Ababa Ethiopia

The water quality index (WQI) is a widely used tool for assessing water quality of various water bodies, but it has drawn criticism for being transferable globally and taking a more physical approach. The present study aimed to assess the water quality indices using weighted arithmetic method and investigate alternative approach to improve the prediction accuracy of WQI by applying machine learning algorithms. These include artificial neural networks, decision trees, random forest, gradient boosting machine, multivariate adaptive regression splines (MArS), Gaussian process with radial basic function, support vector machine with radial basic function, a hybrid of Bayesian and ridge regression, and K-nearest neighbor;the spatiotemporal assessment of the WQI revealed a considerable fluctuation that requires further research to determine the potential causes. The water quality dataset was split into training (70%) and testing (30%) datasets, and the tenfold cross-validation technique was utilized to compare models and optimize hyperparameters on various subsets of the dataset. The study result revealed that almost all of the deployed machine learning models performed well on the training dataset. The multivariate adaptive regression spline (MArS) model outperformed others during both the training and testing phases (rMSE = 0.044, r2 = 0.89, and MAE = 0.025;rMSE = 0.090, r2 = 0.87, and MAE = 0.061 respectively), with the normalized dataset. The worst prediction performance in the test dataset was attained by kernel-based models such as the Gaussian process and support vector machine, which was possibly the effect of overfitting during the model-building process. A MArS model equation, employing three strongly impacting water quality parameters, including E. coli, free residual chlorine, and turbidity, was finally suggested to predict the water quality index for drinking purposes.

关键词： Water quality index Machine learning r programming Weighted arithmetic WQI

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Omics Untargeted Key Script: r-Based Software Toolbox for Untargeted Metabolomics with Bladder Cancer Biomarkers Discovery Case Study

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JOUrNAL OF PrOTEOME rESEArCH 2022年第3期21卷 833-847页

作者： Plyushchenko, Ivan, V Fedorova, Elizaveta S. Potoldykova, Natalia, V Polyakovskiy, Konstantin A. Glukhov, Alexander, I rodin, Igor A. Lomonosov Moscow State Univ Chem Dept Moscow 119991 Russia Russian Acad Sci Frumkin Inst Phys Chem & Electrochem Moscow 119071 Russia Sechenov First Moscow State Med Univ Inst Urol & Reprod Hlth Moscow 119992 Russia Lomonosov Moscow State Univ Biol Dept Moscow 119991 Russia Sechenov First Moscow State Med Univ Dept Biochem Moscow 119991 Russia Sechenov First Moscow State Med Univ Dept Epidemiol & Evidence Based Med Moscow 119435 Russia

Large-scale untargeted LC-MS-based metabolomic profiling is a valuable source for systems biology and biomarker discovery. Data analysis and processing are major tasks due to the high complexity of generated signals and the presence of unwanted variations. In the present study, we introduce an r-based open-source collection of scripts called OUKS (Omics Untargeted Key Script), which provides comprehensive data processing. OUKS is developed by integrating various r packages and metabolomics software tools and can be easily set up and prepared to create a custom pipeline. Novel computational features are related to quality control samples-based signal processing and are implemented by gradient boosting, tree-based, and other nonlinear regression algorithms. Bladder cancer biomarkers discovery study which is based on untargeted LC-MS profiling of urine samples is performed to demonstrate exhaustive functionality of the developed software tool. Unique examination among dozens of metabolomics-specific data curation methods was carried out at each processing step. As a result, potential biomarkers were identified, statistically validated, and described by metabolism disorders. Our study demonstrates that OUKS helps to make untargeted LC-MS metabolomic profiling with the latest computational features readily accessible in a ready-to-use unified manner to a research community.

关键词： metabolomics untargeted profiling r programming data analysis bladder cancer

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From time series to networks in r with the ts2net package

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APPLIED NETWOrK SCIENCE 2024年第1期9卷 32页

作者： Ferreira, Leonardo N. Max Planck Inst Human Dev Ctr Humans & Machines Lentzeallee 94 D-14195 Berlin Germany Univ Oxford Big Data Inst Old Rd Campus Oxford OX3 7LF England

Network science established itself as a prominent tool for modeling time series and complex systems. This modeling process consists of transforming a set or a single time series into a network. Nodes may represent complete time series, segments, or single values, while links define associations or similarities between the represented parts. r is one of the main programming languages used in data science, statistics, and machine learning, with many packages available. However, no single package provides the necessary methods to transform time series into networks. This paper presents a detailed revision of the main transformation methods in the literature and their implementation in the ts2net package in r. The package provides time series distance functions that can be easily computed in parallel and in supercomputers to process larger data sets and methods to transform distance matrices into networks. ts2net also provides methods to transform a single time series into a network, such as recurrence networks, visibility graphs, and transition networks. Together with other packages, ts2net permits the use of network science and graph mining tools to extract information from time series.

关键词： Time series Networks Complex systems Data science r programming

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Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants

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GISCIENCE & rEMOTE SENSING 2020年第2期57卷 207-226页

作者： Althuwaynee, Omar F. Balogun, Abdul-Lateef Al Madhoun, Wesam Sejong Univ Dept Energy & Mineral Resources Engn Seoul South Korea UTP Dept Civil & Environm Engn GAM Res Grp Seri Iskandar Perak Malaysia UTP Dept Civil & Environm Engn Seri Iskandar Perak Malaysia

The automated classification of ambient air pollutants is an important task in air pollution hazard assessment and life quality research. In the current study, machine learning (ML) algorithms are used to identify the inter-correlation between dominant air pollution index (API) for PM10 percentile values and other major air pollutants in order to detect the vital pollutants' clusters in ambient monitoring data around the study area. Two air quality stations, CA0016 and CA0054, were selected for this research due to their strategic locations. Non-linear rPart and Tree model of Decision Tree (DT) algorithm within the r programming environment were adopted for classification analysis. The pollutants' respective significance to PM10 occurrence was evaluated using random forest (rF) of DT algorithms and K means polar cluster function identified and grouped similar features, and also detected vital clusters in ambient monitoring data around the industrial areas. results show increase in the number of clusters did not significantly alter results. PM10 generally shows a reduction in trend, especially in SW direction and an overall minimal reduction in the pollutants' concentration in all directions is observed (less than 1). Fluctuations were observed in the behaviors of CO and NOx during the day while NOx displayed relative stability. results also show that a direct and positive linear relationship exists between the PM10 (target pollutant) and CO, SO2, which suggests that these pollutants originate from the same sources. A semi-linear relationship is observed between the PM10 and others (O-3 and NOx) while humidity shows a negative linearity with PM10. We conclude that most of the major pollutants show a positive trend toward the industrial areas in both stations while trac emissions dominate this site (CA0016) for CO and NOx. Potential applications of nuggets of information derived from these results in reducing air pollution and ensuring sustainability within the city a

关键词： Air quality machine learning PM10 r programming sustainability

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