Examinando por Materia "cluster analysis"
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Ítem A computationally efficient method for delineating irregularly shaped spatial clusters(Springer Berlin Heidelberg, 2011-12-01) Duque, Juan C.; Aldstadt, Jared; Velasquez, Ermilson; Franco, Jose L.; Betancourt, Alejandro; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)In this paper, we present an efficiency improvement for the algorithm called AMOEBA, A Multidirectional Optimum Ecotope-Based Algorithm, devised by Aldstadt and Getis (Geogr Anal 38(4):327-343, 2006). AMOEBA embeds a local spatial autocorrelation statistic in an iterative procedure in order to identify spatial clusters (ecotopes) of related spatial units. We provide an analysis of the computational complexity of the original AMOEBA and develop an alternative formulation that reduces computational time without losing optimality. Empirical evidence is provided using georeferenced socio-demographic data in Accra, Ghana. © 2010 Springer-Verlag.Ítem HouSI: Heuristic for delimitation of housing submarkets and price homogeneous areas(ELSEVIER SCI LTD, 2013-01-01) Royuela, V.; Duque, Juan C.; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)This paper seeks to address the problem of the empirical identification of housing market segmentation, once we assume that submarkets exist. The typical difficulty in identifying housing submarkets when dealing with many locations is the vast number of potential solutions and, in such cases, the use of the Chow test for hedonic functions is not a practical solution. Here, we solve this problem by undertaking an identification process with a heuristic for spatially constrained clustering, the "Housing Submarket Identifier" (HouSI). The solution is applied to the housing market in the city of Barcelona (Spain), where we estimate a hedonic model for fifty thousand dwellings aggregated into ten groups. In order to determine the utility of the procedure we seek to verify whether the final solution provided by the heuristic is comparable with the division of the city into ten administrative districts. © 2012 Elsevier Ltd.Ítem Implementation of multilateral environmental agreements: Rationale and design of the environmental conventions index(MDPI AG, 2020-01-01) Escobar-Pemberthy N.; Ivanova M.Global environmental conventions are created to address and resolve global environmental problems. Assessments of the achievement of specific environmental goals, however, indicate that there is room for progress and that improved collective action is required. Given the few existing studies that measure the implementation of international environmental law, it is important to expand existing analytical frameworks about international environmental agreements and their translation into national policies. This article explains the rationale and design of the Environmental Conventions Index, an implementation measurement tool we have developed at the Center for Governance and Sustainability at the University of Massachusetts Boston. The Index assesses the implementation of global environmental conventions in two clusters-conservation and pollution-showcasing the main trends for both countries and conventions. Using a mixed-method approach, we explain the development of this measurement tool as an instrument to inform policy changes at the national and global levels and present aggregate results of the analysis. As global environmental challenges in the conservation of natural resources and the persistence of pollutants become critical, assessment of countries' performance on international environmental goals is essential. We offer an innovative method that provides academic analysis and policy input to improve implementation, and thus the effectiveness of these governance instruments in addressing global environmental challenges. © 2020 by the authors.Ítem The max-p-regions problem(WILEY-BLACKWELL PUBLISHING INC, 2012-01-01) Duque, J.C.; Anselin, L.; Rey, S.J.; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)In this paper, we introduce a new spatially constrained clustering problem called the max-p-regions problem. It involves the clustering of a set of geographic areas into the maximum number of homogeneous regions such that the value of a spatially extensive regional attribute is above a predefined threshold value. We formulate the max-p-regions problem as a mixed integer programming (MIP) problem, and propose a heuristic solution. © 2011, Wiley Periodicals, Inc..Ítem The p-Regions Problem(WILEY-BLACKWELL, 2011-01-01) Duque, Juan C.; Church, Richard L.; Middleton, Richard S.; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)The p-regions problem involves the aggregation or clustering of n small areas into p spatially contiguous regions while optimizing some criteria. The main objective of this article is to explore possible avenues for formulating this problem as a mixed integer-programming (MIP) problem. The critical issue in formulating this problem is to ensure that each region is a spatially contiguous cluster of small areas. We introduce three MIP models for solving the p regions problem. Each model minimizes the sum of dissimilarities between all pairs of areas within each region while guaranteeing contiguity. Three strategies designed to ensure contiguity are presented: (1) an adaptation of the Miller, Tucker, and Zemlin tour-breaking constraints developed for the traveling salesman problem; (2) the use of ordered-area assignment variables based upon an extension of an approach by Cova and Church for the geographical site design problem; and (3) the use of flow constraints based upon an extension of work by Shirabe. We test the efficacy of each formulation as well as specify a strategy to reduce overall problem size. © 2011 The Ohio State University.Ítem Unsupervised fuzzy binning of metagenomic sequence fragments on three-dimensional Barnes-Hut t-Stochastic Neighbor Embeddings(Institute of Electrical and Electronics Engineers Inc., 2018-01-01) Ariza-Jimenez L.; Quintero O.L.; Pinel N.; Universidad EAFIT. Departamento de Ciencias; Ciencias Biológicas y Bioprocesos (CIBIOP)Shotgun metagenomic studies attempt to reconstruct population genome sequences from complex microbial communities. In some traditional genome demarcation approaches, high-dimensional sequence data are embedded into two-dimensional spaces and subsequently binned into candidate genomic populations. One such approach uses a combination of the Barnes-Hut approximation and the t -Stochastic Neighbor Embedding (BH-SNE) algorithm for dimensionality reduction of DNA sequence data pentamer profiles; and demarcation of groups based on Gaussian mixture models within humanimposed boundaries. We found that genome demarcation from three-dimensional BH-SNE embeddings consistently results in more accurate binnings than 2-D embeddings. We further addressed the lack of a priori population number information by developing an unsupervised binning approach based on the Subtractive and Fuzzy c-means (FCM) clustering algorithms combined with internal clustering validity indices. Lastly, we addressed the subject of shared membership of individual data objects in a mixed community by assigning a degree of membership to individual objects using the FCM algorithm, and discriminated between confidently binned and uncertain sequence data objects from the community for subsequent biological interpretation. The binning of metagenome sequence fragments according to thresholds in the degree of membership opens the door for the identification of horizontally transferred elements and other genomic regions of uncertain assignment in which biologically meaningful information resides. The reported approach improves the unsupervised genome demarcation of populations within complex communities, increases the confidence in the coherence of the binned elements, and enables the identification of evolutionary processes ignored in hard-binning approaches in shotgun metagenomic studies. © 2018 IEEE.Ítem Unsupervised fuzzy binning of metagenomic sequence fragments on three-dimensional Barnes-Hut t-Stochastic Neighbor Embeddings(Institute of Electrical and Electronics Engineers Inc., 2018-01-01) Ariza-Jimenez L.; Quintero O.L.; Pinel N.; Universidad EAFIT. Escuela de Ciencias; Modelado MatemáticoShotgun metagenomic studies attempt to reconstruct population genome sequences from complex microbial communities. In some traditional genome demarcation approaches, high-dimensional sequence data are embedded into two-dimensional spaces and subsequently binned into candidate genomic populations. One such approach uses a combination of the Barnes-Hut approximation and the t -Stochastic Neighbor Embedding (BH-SNE) algorithm for dimensionality reduction of DNA sequence data pentamer profiles; and demarcation of groups based on Gaussian mixture models within humanimposed boundaries. We found that genome demarcation from three-dimensional BH-SNE embeddings consistently results in more accurate binnings than 2-D embeddings. We further addressed the lack of a priori population number information by developing an unsupervised binning approach based on the Subtractive and Fuzzy c-means (FCM) clustering algorithms combined with internal clustering validity indices. Lastly, we addressed the subject of shared membership of individual data objects in a mixed community by assigning a degree of membership to individual objects using the FCM algorithm, and discriminated between confidently binned and uncertain sequence data objects from the community for subsequent biological interpretation. The binning of metagenome sequence fragments according to thresholds in the degree of membership opens the door for the identification of horizontally transferred elements and other genomic regions of uncertain assignment in which biologically meaningful information resides. The reported approach improves the unsupervised genome demarcation of populations within complex communities, increases the confidence in the coherence of the binned elements, and enables the identification of evolutionary processes ignored in hard-binning approaches in shotgun metagenomic studies. © 2018 IEEE.