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Academic literature on the topic 'Multidimensional indicators'

Author: Grafiati
Published: 14 March 2023
Last updated: 13 June 2025

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Journal articles on the topic "Multidimensional indicators"

1

Sel�n, Jan. "Multidimensional descriptions of social indicators." Social Indicators Research 17, no. 4 (1985): 435–45. http://dx.doi.org/10.1007/bf00290324.

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2

Zhao, Rongying, and Xu Wang. "Research on impact evaluation of academic journals from multidimensional perspective." Library Hi Tech 38, no. 2 (2019): 458–78. http://dx.doi.org/10.1108/lht-03-2019-0067.

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Purpose The purpose of this paper is to introduce altmetric indicators and combine with traditional citation indicators to comprehensively evaluate the impact of academic journals from the perspective of multidimensional and multi-indicator fusion. Design/methodology/approach The authors take international multidisciplinary journals as an example, combining 14 traditional citation indicators of academic journals and introducing 14 altmetric indicators to build a comprehensive evaluation model of the impact of academic journals (academic impact and societal impact). At the same time, the authors systematically construct a journal evaluation indicator system from three dimensions. Then, the indicators data of three dimensions are evaluated by normalized processing, correlation analysis, reliability and validity analysis, PCA and factor analysis. Findings Two-dimensional and three-dimensional analyses can exactly provide some useful information for academic journals’ location in the respective coordinate systems. There are strong positive correlations among the measured indicators in the three dimensions, and each indicator has a significant consistency between whole and internal. The correlation coefficient between FD1 and FD2 is 0.888 with a strong positive correlation. It shows that the traditional citation indicators provided by WoS and Scopus database are highly consistent, and they are comparable and alternative in evaluating the academic impact of journals. The correlation coefficients of FD1, FD2 with FD3 are 0.831 and 0.798. There are strong positive correlations among them, which indicate that the evaluation of journals’ societal impact based on altmetrics indicator can be considered as a potential supplement to academic impact evaluation based on citation and to reflect the multidimensional nature of journals impact in an immediate way. Originality/value Multidimensional and multi-indicator perspective evaluation can provide references for the selection of impact evaluation indicators and model optimization of academic journals, and also provide new ideas for improving the status of the impact evaluation of academic journals.
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3

Ameur, H. Ben, G. Chavent, F. Clément, and P. Weis. "Image segmentation with multidimensional refinement indicators." Inverse Problems in Science and Engineering 19, no. 5 (2011): 577–97. http://dx.doi.org/10.1080/17415977.2011.579609.

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4

Valdés, Javier. "Arbitrariness in Multidimensional Energy Security Indicators." Ecological Economics 145 (March 2018): 263–73. http://dx.doi.org/10.1016/j.ecolecon.2017.09.002.

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5

Ambel, Alemayehu Azeze, Harriet Kasidi Mugera, and Robert E. S. Bain. "Accounting for drinking water quality in measuring multidimensional poverty in Ethiopia." PLOS ONE 15, no. 12 (2020): e0243921. http://dx.doi.org/10.1371/journal.pone.0243921.

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The Multidimensional Poverty Index is used increasingly to measure poverty in developing countries. The index is constructed using selected indicators that cover health, education, and living standards dimensions. The accuracy of this tool, however, depends on how each indicator is measured. This study explores the effect of accounting for water quality in multidimensional poverty measurement. Access to drinking water is traditionally measured by water source types. The study uses a more comprehensive measure, access to safely managed drinking water services, which are free from E. coli contamination, available when needed and accessible on premises in line with Sustainable Development Goal target 6.1. The study finds that the new measure increases national multidimensional headcount poverty by 5–13 percentage points, which would mean that 5–13 million more people are multidimensionally poor. It also increases the poverty level in urban areas to a greater extent than in rural areas. The finding is robust to changes in water contamination risk levels and Multidimensional Poverty Index aggregation approaches and weighting structures.
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6

Akimkina, E. E. "Structuring and visualization of indicators in multidimensional data cubes." Informacionno-technologicheskij vestnik, no. 4 (December 30, 2018): 79–87. http://dx.doi.org/10.21499/2409-1650-2018-4-79-87.

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The problems of structuring of indicators in multidimensional data cubes with their subsequent processing with the help of end-user tools providing multidimensional visualization and data management are analyzed; the possibilities of multidimensional data processing technologies for managing and supporting decision making at a design and technological enterprise are shown; practical recommendations on the use of domestic computer environments for the structuring and visualization of multidimensional data cubes are given.
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7

Cerdeiro, Diego, and Rachel Nam. "A Multidimensional Approach to Trade Policy Indicators." IMF Working Papers 18, no. 32 (2018): 1. http://dx.doi.org/10.5089/9781484342350.001.

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8

Nur, Widi Astuti, Firmansyah Firmansyah, and Widodo Wahyu. "A Multidimensional Approach of Child Poverty in Indonesia." E3S Web of Conferences 73 (2018): 10015. http://dx.doi.org/10.1051/e3sconf/20187310015.

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This research discuss about child poverty, which is commonly called deprivation. The purpose of this research is to count multidimensional child poverty index as child poverty measuring istrument in 34 provinces of Indonesia, by using composite idex count calculation. Dimensions and indicators used to compose Multidimensional Child Poverty Index in this research are adapting from dimensions and indicators that used in research by Mahadewi and Kodoatie1. Besides that, the determination of dimensions and indicator also adjust the availability of data from SUSENAS 2015.The calculation result of Multidimensional Child Poverty shows that 7 provinces pertain to number I priority (high), 9 provinces belong to number II priority (medium-high), 10 provinces include to number III priorities (medium-low), and 8 provinces are number IV priority (low).
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9

Leplat, L., R. S. Torres, D. Aspen, and A. Amundsen. "VIZPLAN: A VISUAL ANALYTICS PLATFORM FOR THE ASSESSMENT OF MULTIDIMENSIONAL INDICATORS OVER TIME." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W3-2022 (October 14, 2022): 127–34. http://dx.doi.org/10.5194/isprs-annals-x-4-w3-2022-127-2022.

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Abstract. In this paper, we introduce VizPlan, a new platform to support the assessment of multidimensional indicators over time. VizPlan includes a visualisation scheme based on a radial visual structure that allows the direct comparison of indicator values over time, a search tool to support the identification of entities whose indicators are similar to each other, and a clustering tool to group entities according to their indicator scores. VizPlan was designed and implemented to be flexible; it can be easily tailored to the visualization and analysis of any multidimensional temporal data. In this paper, the use of VizPlan is illustrated in the context of three case studies concerning the analysis of sustainability indicators to support urban planning: key performance indicators related to the sustainable development goals, walkability analysis, and bus service availability assessment. All case studies refer to real data related to Norwegian cities, especially Ålesund. VizPlan is available as an open source software at https://github.com/Rylern/VizPlan – As of May 2022.
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10

Shinkarenko, Volodymyr, Maksym Matskul, and Dean Linok. "Investment attractiveness modeling using multidimensional statistical analysis methods." SHS Web of Conferences 65 (2019): 04007. http://dx.doi.org/10.1051/shsconf/20196504007.

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The article examines the investment attractiveness of the main branches of the food industry of Ukraine as a latent variable. For the first time in this area, a combination of various methods of multivariate statistical analysis is used for research (cluster analysis and factor analysis – the principal component method). These methods made it possible to use a large number of various indicators of the activities of industries to characterize investment attractiveness. As a result, the set of the branches was divided into three groupsclusters: “leaders” are the most attractive sectors for investment, “middle peasants” are attractive branches for investment, and “outsiders” are the least attractive branches for investment. The generalizing factors (principal components), which influence the resulting factor - investment attractiveness, were found. The interrelation of the generalizing factors and initial indicators is established. As a result of the research, it was possible to make an objective assessment of the investment attractiveness (as a latent indicator) of the main branches of the food industry in Ukraine, using instead of a multitude of indicators only three latent factors.
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