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distance-based methods for estimating density of nonrandomly distributed populations

ECOLOGY 2020年第10期101卷 e03143-e03143页

作者： Shen, Guochun Wang, Xihua He, Fangliang East China Normal Univ Sch Ecol & Environm Sci Zhejiang Tiantong Forest Ecosyst Natl Observat & ECNU Alberta Joint Lab Biodivers Study Shanghai 200241 Peoples R China Shanghai Inst Pollut Control & Ecol Secur 1515 North Zhongshan Rd 2 Shanghai 200092 Peoples R China Univ Alberta Dept Renewable Resources Edmonton AB T6G 2H1 Canada

Population density is the most basic ecological parameter for understanding population dynamics and biological conservation. distance-based methods (or plotless methods) are considered as a more efficient but less robust approach than quadrat-based counting methods in estimating plant population density. The low robustness of distance-based methods mainly arises from the oversimplistic assumption of completely spatially random (CSR) distribution of a population in the conventional distance-based methods for estimating density of non-CSR populations in natural communities. In this study we derived two methods to improve on density estimation for plant populations of non-CSR distribution. The first method modified an existing composite estimator to correct for the long-recognized bias associated with that estimator. The second method was derived from the negative binomial distribution (NBD) that directly deals with aggregation in the distribution of a species. The performance of these estimators was tested and compared against various distance-based estimators by both simulation and empirical data of three large-scale stem-mapped forests. Results showed that the NBD point-to-tree distance estimator has the best and most consistent performance across populations with vastly different spatial distributions. This estimator offers a simple, efficient and robust method for estimating density for empirical populations of plant species.

关键词： distance-based methods nearest-neighbor distance negative binomial distribution plotless method population density estimator spatial distribution of species

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distance-based weighting methods for composite indicators, with applications related to energy sustainability

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2025年第1期32卷 478-501页

作者： Karagiannis, Roxani Karagiannis, Giannis Ctr Planning & Econ Res 11 Amerikis Str Athens 10672 Greece Univ Macedonia Dept Econ 156 Egnatia str Thessaloniki 54006 Greece

In this paper, we propose a novel use of four distance-based methods for constructing composite indicators: namely, the maximizing deviations method, the weighted least-square (WLD) deviation from the mean method, the WLD deviation from the ideal method, and the WLD dissimilarity method. The main advantages of these methods are that they have analytical solutions. Thus, there is no need to solve the underlying programming problem for each application. They also result in common (but not necessarily equal) weights that allow complete comparison and ranking. We explore the potential of the suggested weighting method by using it to re-estimate the Sustainable Energy Index and the Sustainable Development Goal 7 composite indicator.

关键词： composite indicators weighting distance-based methods SEI SDG7

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ARDOD: adaptive radius density-based outlier detection

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EVOLUTIONARY INTELLIGENCE 2024年第5-6期17卷 3707-3722页

作者： Rahmati, Farshad Gharaei, Reza Heydari Nezamabadi-pour, Hossein Shahid Bahonar Univ Kerman Dept Elect Engn Intelligent Data Proc Lab IDPL Kerman Iran

Outlier detection has garnered considerable attention in recent years due to its wide-ranging applications across various research domains. This surge in interest has led to the development of numerous detection techniques, predominantly based on distance or density metrics. A notable limitation of these existing methods is their reliance on parameter adjustments, significantly affecting the outcome. Additionally, these methods exhibit intrinsic flaws: distance-based approaches struggle with clusters with varying local densities, while density-based methods fail to identify patterns within low-density areas. Moreover, most prior techniques are adept at identifying only one kind of outlier-local, global, or group of outliers. Addressing these challenges, we introduce the Adaptive Radius Density-based Outlier Detection (ARDOD) method, which departs from the traditional parameter-dependent approach. ARDOD is a novel parameter-free algorithm that dynamically determines the necessary parameters based on the data distribution within the feature space. This innovative method demonstrates robust performance in detecting all three categories of outliers. The efficacy and superior performance of ARDOD are validated through an extensive experimental analysis involving various synthetic and real-world datasets. This analysis showcases ARDOD's advantages over seven established methods: Local Outlier Factor (LOF), Angle-based Outlier Detection (ABOD), Robust distance-based Outlier Score (RDOS), Directed density ratio Changing Rate-based outlier detection (DCROD), Empirical-Cumulative-distribution-based Outlier Detection(ECOD), mean-shift outlier detector(MOD +),and Local-Global Outlier Detection (LGOD), underscoring its potential as a versatile tool in outlier detection research.

关键词： Outlier detection Anomaly detection Density-based methods distance-based methods Adaptive radius Mass sharing method

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DNA barcodes provide insights into the diversity and biogeography of the non-biting midge Polypedilum (Diptera, Chironomidae) in South America

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ECOLOGY AND EVOLUTION 2023年第10期13卷 e10602页

作者： da Silva, Fabio Laurindo Pinho, Luiz Carlos Stur, Elisabeth Nihei, Silvio Shigueo Ekrem, Torbjorn Norwegian Univ Sci & Technol NTNU Univ Museum Dept Nat Hist Trondheim Norway Univ Fed Santa Catarina Dept Ecol & Zool Lab Systemat Diptera Florianopolis Brazil Univ Sao Paulo Inst Biosci Dept Zool Lab Systemat & Biogeog Insecta Sao Paulo Brazil Univ Sao Paulo Inst Biosci Dept Zool Lab Aquat Insect Biodivers & Ecol Sao Paulo Brazil

South America, particularly within its tropical belt, is renowned for its unparalleled high levels of species richness, surpassing other major biomes. Certain neotropical areas harbor fragmented knowledge of insect diversity and face imminent threats from biodiversity loss and climate change. Hence, there is an urgent need for rapid estimation methods to complement slower traditional taxonomic approaches. A variety of algorithms for delimiting species through single-locus DNA barcodes have been developed and applied for rapid species diversity estimates across diverse taxa. However, tree-based and distance-based methods may yield different group assignments, leading to potential overestimation or underestimation of putative species. Here, we investigate the performance of different DNA-based species delimitation approaches to rapidly estimate the diversity of Polypedilum (Chironomidae, Diptera) in South America. Additionally, we test the hypothesis that significant differences exist in the community structure of Polypedilum fauna between South America and its neighboring regions, particularly the Nearctic. Our analysis encompasses a dataset of 1492 specimens from 598 locations worldwide, with a specific focus on South America. Within this region, we analyzed a subset of 247 specimens reported from 37 locations. Using various methods including the Barcode Index Number (BIN), Bayesian Poisson tree processes (bPTP), multi-rate Poisson tree processes (mPTP), single-rate Poisson tree processes (sPTP), and generalized mixed Yule coalescent (sGMYC), we identify molecular operational taxonomic units (MOTUs) ranging from 267 to 520. Our results indicate that the sGMYC method is the most suitable for estimating putative species in our dataset, resulting in the identification of 75 species in the Neotropical region, particularly in South America. Notably, this region exhibited higher species richness in comparison to the Palearctic and Oriental realms. Additionally, our findin

关键词： biogeography Chironomidae distance-based methods DNA barcode tree-based methods

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The MultiFurcating Neighbor-Joining Algorithm for Reconstructing Polytomic Phylogenetic Trees

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JOURNAL OF MOLECULAR EVOLUTION 2023年第6期91卷 773-779页

作者： Fernandez, Alberto Segura-Alabart, Natalia Serratosa, Francesc Univ Rovira i Virgili Dept Engn Quim Tarragona Spain Univ Rovira i Virgili Dept Engn Informat & Matemat Tarragona Spain

Results from phylogenetic analyses that study the evolution of species according to their biological characteristics are frequently structured as phylogenetic trees. One of the most widely used methods for reconstructing them is the distance-based method known as the neighbor-joining (NJ) algorithm. It is known that the NJ algorithm can produce different phylogenetic trees depending on the order of the taxa in the input matrix of evolutionary distances, because the method only yields bifurcating branches or dichotomies. According to this, results and conclusions published in articles that only calculate one of the possible dichotomic phylogenetic trees are somehow biased. We have generalized the formulas used in the NJ algorithm to cope with Multifurcating branches or polytomies, and we have called this new variant of the method the multifurcating neighbor-joining (MFNJ) algorithm. Instead of the dichotomic phylogenetic trees reconstructed by the NJ algorithm, the MFNJ algorithm produces polytomic phylogenetic trees. The main advantage of using the MFNJ algorithm is that only one phylogenetic tree can be obtained, which makes the experimental section of any study completely reproducible and unbiased to external issues such as the input order of taxa.

关键词： distance-based methods Neighbor-joining Polytomy Ties in proximity

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Agglomeration and coagglomeration of co-working spaces and creative industries in the city

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EUROPEAN PLANNING STUDIES 2023年第3期31卷 445-466页

作者： Coll-Martinez, Eva Mendez-Ortega, Carles Sci Po Toulouse LEREPS Toulouse Toulouse France Univ Rovira & Virgili Reus Dept Econ QURE ECO SOS Reus Spain

The present paper investigates the location patterns and the effects co-working spaces (CWS) generate on the urban context. The focus is on Barcelona, one of the most important creative hubs in Europe in terms of knowledge-based, creative, digital, and sharing economy, and the city hosting the largest number of co-working spaces in Spain. The paper addresses three main questions: (1) Which are the location patterns of co-working spaces in Barcelona? (2) Do CWS agglomerate in the same areas? And, (3) Do CWS coagglomerate with the creative industries (CIs)? To do that, this paper uses open data on Barcelona neighbourhoods' socioeconomic composition provided by the Statistics Department of the Council of Barcelona and micro-geographic data of private CWS and creative labs in Barcelona. By using Geographical Information Systems (GIS) and Kd functions of agglomeration and coagglomeration, results show that CWS are highly concentrated in central areas of Barcelona where there are greater chances to meet customers and suppliers, the proximity to urban amenities and the fact of being associated to a specific place-image. Moreover, they coagglomerate with CIs firms, especially with symbolic and synthetic knowledge-based CIs. These results are relevant when assessing the actual goal of urban policies in Barcelona.

关键词： Co-working spaces creative industries distance-based methods agglomeration coagglomeration Barcelona

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Fast and accurate distance-based phylogenetic placement using divide and conquer

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MOLECULAR ECOLOGY RESOURCES 2022年第3期22卷 1213-1227页

作者： Balaban, Metin Jiang, Yueyu Roush, Daniel Zhu, Qiyun Mirarab, Siavash Univ Calif San Diego Bioinformat & Syst Biol Grad Program La Jolla CA 92093 USA Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA Arizona State Univ Ctr Fundamental & Appl Microbi Tempe AZ USA

Phylogenetic placement of query samples on an existing phylogeny is increasingly used in molecular ecology, including sample identification and microbiome environmental sampling. As the size of available reference trees used in these analyses continues to grow, there is a growing need for methods that place sequences on ultra-large trees with high accuracy. distance-based placement methods have recently emerged as a path to provide such scalability while allowing flexibility to analyse both assembled and unassembled environmental samples. In this study, we introduce a distance-based phylogenetic placement method, APPLES-2, that is more accurate and scalable than existing distance-based methods and even some of the leading maximum-likelihood methods. This scalability is owed to a divide-and-conquer technique that limits distance calculation and phylogenetic placement to parts of the tree most relevant to each query. The increased scalability and accuracy enables us to study the effectiveness of APPLES-2 for placing microbial genomes on a data set of 10,575 microbial species using subsets of 381 marker genes. APPLES-2 has very high accuracy in this setting, placing 97% of query genomes within three branches of the optimal position in the species tree using 50 marker genes. Our proof-of-concept results show that APPLES-2 can quickly place metagenomic scaffolds on ultra-large backbone trees with high accuracy as long as a scaffold includes tens of marker genes. These results pave the path for a more scalable and widespread use of distance-based placement in various areas of molecular ecology.

关键词： distance-based methods metagenomics microbiome phylogenetic placement

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Tourism competitiveness measurement. A perspective from Central America and Caribbean destinations

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TOURISM REVIEW 2022年第6期77卷 1401-1417页

作者： Perez Leon, Victor Ernesto Ma Guerrero, Flor Caballero, Rafael Univ Seville Dept Econ Aplicada 2 Fac Ciencias Econ & Empresariales Seville Spain Pablo de Olavide Univ Dept Econ Quantitat Methods & Econ Hist Seville Spain Univ Malaga Dept Appl Econ Math Malaga Spain

Purpose This study aims to present diverse proposals for the measurement of tourism destination competitiveness that serve as alternatives to the travel and tourism competitiveness index (TTCI). Design/methodology/approach The proposal includes principal component analysis, the DP2-distance method, goal programming, data envelopment analysis and the Borda count. The study evaluates 17 destinations from Central America and the Caribbean. Findings These include the feasibility that the methodologies provide reliable competitiveness rankings and the possibility of using less information due to the strength of the statistical methodologies. International tourist arrivals, income from international tourism and travel and tourism contribution to the gross domestic product could be used as approximations of tourism destination competitiveness. Research limitations/implications The main limitation is the absence of major destinations from the region that constitutes fierce competitors. Practical implications New aggregation methods can build composite indicators for competitiveness measurement and their presentation in a more comprehensible way. Social implications The results serve as an alternative for countries that have yet to be considered in international tourism competitiveness comparisons. Originality/value A better explanatory power of the proposed index is given, thanks to their decomposition capacity and the reduction of the limitations of the original TTCI. Moreover, the proposals facilitate the inclusion of external information or the execution of a completely objective methodology.

关键词： Competitiveness Central America and the Caribbean Composite indicators distance-based methods Multicriteria Data envelopment analysis ??? ?????? ???? ??????? ??? ?????? Competitividad Centro america y el caribe Indicadores sinteticos Metodos basados en distancia Multicriterio analisis envolvente de datos

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Identification of Key Players in a Social Media based on the Kendall-Wei Ranking 11th

Identification of Key Players in a Social Media Based on the...

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11th International Conference on High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production (HPCST)

作者： Yefremov, Alexander A. Luneva, Elena E. Tomsk Polytech Univ Lenin Ave 30 Tomsk 634050 Russia Tomsk State Univ Control Syst & Radioelect Lenin Ave 40 Tomsk 634050 Russia

ISBN: (纸本)9783030941413;9783030941406

The paper concerns studying the effectiveness of Kendall-Wei ranking procedure applied to the problem of identification of opinion leaders in social media. In order to achieve this, authors conducted both model data and real data experiments, using the original technique of constructing social graphs, and compared the sets of key players, obtained by the popular methods as well as by the procedure under consideration. The comparison results demonstrate that in some instances Kendall-Wei ranking procedure outperforms other methods of ranking nodes in a social graph, which plays a significant part in solving the problem of detecting major actors in social networks.

关键词： Social graph Key players distance-based methods Ranking Rank correlation

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Differentially Private distance Learning in Categorical Data

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DATA MINING AND KNOWLEDGE DISCOVERY 2021年第5期35卷 2050-2088页

作者： Battaglia, Elena Celano, Simone Pensa, Ruggero G. Univ Turin Dept Comp Sci Turin Italy

Most privacy-preserving machine learning methods are designed around continuous or numeric data, but categorical attributes are common in many application scenarios, including clinical and health records, census and survey data. distance-based methods, in particular, have limited applicability to categorical data, since they do not capture the complexity of the relationships among different values of a categorical attribute. Although distance learning algorithms exist for categorical data, they may disclose private information about individual records if applied to a secret dataset. To address this problem, we introduce a differentially private family of algorithms for learning distances between any pair of values of a categorical attribute according to the way they are co-distributed with the values of other categorical attributes forming the so-called context. We define different variants of our algorithm and we show empirically that our approach consumes little privacy budget while providing accurate distances, making it suitable in distance-based applications, such as clustering and classification.

关键词： Differential privacy Metric learning Categorical attributes distance-based methods

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