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Journal articles on the topic 'Socio-economic data'

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Author: Grafiati

Published: 10 March 2023

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1

Lypchuk, V. V., and О. M. Krupa. "Data Reduction in Socio-Economic Studies." Statistics of Ukraine, no. 1(76) (March 20, 2017): 15–20. http://dx.doi.org/10.31767/su.1(76).2017.01.02.

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The article is devoted to the problem of data reduction as an important step on the way of providing reliability and efficiency of socio-economic studies. Through the reduction the large amounts of raw data, generated from different sources, become more useful, convenient and clear for use. Meanwhile, the data reduction is not treated as a separate phase of studies in the national statistic practice. The aim of the article is to substantiate the importance of data reduction in economic studies and attempt to systematize and generalize the essence and components of the phase of data reduction as well as ways of their implementation. The study is based on methods of theoretical generalization, abstract and logic, analogy and others. The essence of data reduction is defined as the process of converting raw data into the pure form and reducing the number of units’ attributes (features), which are not significant to further analysis. In fact, this is part of the analysis involving selection of the data that are most important from the viewpoint of the study’s goals. The significance of data reduction in economic studies is outlined. It is found that it assures the validity of their results, reduces their time and costs, simplifies the representational complexity of the problem being addressed, eliminates the errors and redundant data from the investigated set, looses the requirements to calculation tools. The data resulting from reduction are much more informative. Many dependencies and relationships become more readable (visual). It is emphasized that reduction applies to the current data (on-line), as well as to historical data (off-line), contained in the already created databases. The phases of data reduction are described. They are: control of data collection, data editing, classification, data construction and grouping, coding and transmission (data transmission to the processing tools - computers). Data reduction techniques and methods most common in the global practice are shown. Future studies of data reduction problems are expected to focus on potential ways to implement its advanced methods in the domestic practice of statistical science. It will allow for enhancing significantly the speed and efficiency of economic analysis and the reliability of its results.

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2

Girul, Agata. "MAJOR SOCIO-ECONOMIC DATA ON VOIVODSHIPS." Śląski Przegląd Statystyczny, no. 14 (2016): 255–62. http://dx.doi.org/10.15611/sps.2016.14.17.

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Girul, agata. "MAJOR SOCIO-ECONOMIC DATA ON VOIVODESHIPS." Śląski Przegląd Statystyczny, no. 15 (2016): 297–303. http://dx.doi.org/10.15611/sps.2017.15.17.

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Lee, Ji-Wan, Eun-Mi Hong, Won-Jin Jang, and Seong-Joon Kim. "Assessment of socio-economic drought information using drought-related Internet news data (Part A: Socio-economic drought data construct and evaluation socio-economic drought information)." International Journal of Disaster Risk Reduction 75 (June 2022): 102961. http://dx.doi.org/10.1016/j.ijdrr.2022.102961.

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Aiubov, Magomed salekh Alievich. "Chechen Republic’s socio-economic development data analysis." Interactive science, no. 5 (39) (May 27, 2019): 48–49. http://dx.doi.org/10.21661/r-496606.

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The analysis of socio-economic state of Chechen Republic is given in this article. The data given in this analysis can be used in order to find a way to optimize region’s level of development. The author gives detailed data on both socio-economic state of the country and region’s GRP growth (development) rate. The data presented in the article are the most recent.

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Slingsby, Aidan, Jason Dykes, and Jo Wood. "Rectangular Hierarchical Cartograms for Socio-Economic Data." Journal of Maps 6, no. 1 (January 2010): 330–45. http://dx.doi.org/10.4113/jom.2010.1090.

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7

Yun-Shan Sun, Yun-Shan Sun, Hong-Yan Xu Yun-Shan Sun, and Yan-Qin Li Hong-Yan Xu. "Missing Data Interpolation with Variational Bayesian Inference for Socio-economic Statistics Applications." 電腦學刊 33, no. 2 (April 2022): 169–76. http://dx.doi.org/10.53106/199115992022043302015.

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<p>The information integrity is needed to solving socio-economic statistical problems. However, the information integrity is destroyed by missing data which is caused by various subjective and objective reasons. So the missing data interpolation is used to supplement missing data. In this paper, missing data interpolation with variational Bayesian inference is proposed. This method is combined with Gaussian model to approximate the posterior distribution to obtain complete data. The experiments include two datasets (artificial dataset and actual dataset) based on three missing ratios separately. The missing data interpolation performance of variational Bayesian method is compared with that which is obtained by mean interpolation and K-nearest neighbor interpolation methods separately in MSE (Mean Square Error) and MAPE (Mean Absolute Percentage Error). The experimental results show that the proposed variational Bayesian method is better in MSE and MAPE.</p> <p> </p>

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Yun-Shan Sun, Yun-Shan Sun, Hong-Yan Xu Yun-Shan Sun, and Yan-Qin Li Hong-Yan Xu. "Missing Data Interpolation with Variational Bayesian Inference for Socio-economic Statistics Applications." 電腦學刊 33, no. 2 (April 2022): 169–76. http://dx.doi.org/10.53106/199115992022043302015.

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<p>The information integrity is needed to solving socio-economic statistical problems. However, the information integrity is destroyed by missing data which is caused by various subjective and objective reasons. So the missing data interpolation is used to supplement missing data. In this paper, missing data interpolation with variational Bayesian inference is proposed. This method is combined with Gaussian model to approximate the posterior distribution to obtain complete data. The experiments include two datasets (artificial dataset and actual dataset) based on three missing ratios separately. The missing data interpolation performance of variational Bayesian method is compared with that which is obtained by mean interpolation and K-nearest neighbor interpolation methods separately in MSE (Mean Square Error) and MAPE (Mean Absolute Percentage Error). The experimental results show that the proposed variational Bayesian method is better in MSE and MAPE.</p> <p> </p>

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9

Otto, Ilona M., Anne Biewald, Dim Coumou, Georg Feulner, Claudia Köhler, Thomas Nocke, Anders Blok, et al. "Socio-economic data for global environmental change research." Nature Climate Change 5, no. 6 (May 21, 2015): 503–6. http://dx.doi.org/10.1038/nclimate2593.

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10

Müller, G. P., P. Alpar, R. J. Grausam, and F. Worpitz. "Knowledge-Based Analysis of Socio-Economic Panel Data." IFAC Proceedings Volumes 20, no. 10 (September 1987): 87–89. http://dx.doi.org/10.1016/s1474-6670(17)59038-2.

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11

van Leth, F., R. S. Guilatco, S. Hossain, A. H. van't Hoog, N. B. Hoa, M. J. van der Werf, and K. Lönnroth. "Measuring socio-economic data in tuberculosis prevalence surveys." International Journal of Tuberculosis and Lung Disease 15, no. 6 (June 1, 2011): 58–63. http://dx.doi.org/10.5588/ijtld.10.0417.

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12

IWAKURA, Nozomi, Keita OCHI, Syunpei HAGINOYA, and Midori SUGIYAMA. "Crime analysis in Tokyo using Socio-Economic data." Proceedings of the Annual Convention of the Japanese Psychological Association 74 (September 20, 2010): 3PM143. http://dx.doi.org/10.4992/pacjpa.74.0_3pm143.

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13

D C, Daman Dhoj. "Socio-Economic Status of Kingariya." Academia Research Journal 2, no. 1 (February 13, 2023): 60–72. http://dx.doi.org/10.3126/academia.v2i1.52337.

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Analyzing the socioeconomic situation and current circumstances of the Kingariya community of Dalit Muslims in Puraini, Nepalgunj, was the goal of this study. They are a group of traditional dancers and cultural performers from the upper caste of Muslims. Muslims from the Dalit community work as wage laborers in agriculture and construction. Despite spending a lot of time in the Terai region of Nepal, they are poor and landless. The primary goal of this study is to examine the socioeconomic situation of the Kingariya community in Kingariyanpurwa, Puraini, in the Banke district. In this study, a descriptive and exploratory research design was used. The socioeconomic situation of the Kingariya people is generally described in chronological sequence in this study design. Both primary and secondary data are used in this investigation. Interviews were used to gather primary data from the field survey, while secondary data came from the Nepalgunj Sub-Metropolitan Office, private agencies, government offices, numerous research publications, and other pertinent literature and websites on the internet. The 26 Dalit Muslim households in this study area make up the study's target population. To determine the socioeconomic status of the dalit Muslim community, a purposeful sample technique was chosen. The small population of Dalit Muslims lives in Puraini, Nepalgunj. In the 56 total households for the household survey, there were 26 Kingariya households, or 50% of all households. The study's conclusion is that Muslims live in a socioeconomically deplorable situation. They are hardworking, straightforward, and illiterate, yet they are also poor. High cast has complete control over them in all aspect of life. The socioeconomic status of the Muslim population has had both positive and negative effects over time.

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Winkler, Othmar W. "Interpreting the cumulative frequency distribution of Socio-Economic data." Biometrics & Biostatistics International Journal 8, no. 1 (January 23, 2019): 13–16. http://dx.doi.org/10.15406/bbij.2019.08.00263.

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15

Dunkel, Jürgen, David Dominguez, Óscar G. Borzdynski, and Ángel Sánchez. "Solid Waste Analysis Using Open-Access Socio-Economic Data." Sustainability 14, no. 3 (January 21, 2022): 1233. http://dx.doi.org/10.3390/su14031233.

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Nowadays, problems related with solid waste management become a challenge for most countries due to the rising generation of waste, related environmental issues, and associated costs of produced wastes. Effective waste management systems at different geographic levels require accurate forecasting of future waste generation. In this work, we investigate how open-access data, such as provided from the Organisation for Economic Co-operation and Development (OECD), can be used for the analysis of waste data. The main idea of this study is finding the links between socio-economic and demographic variables that determine the amounts of types of solid wastes produced by countries. This would make it possible to accurately predict at the country level the waste production and determine the requirements for the development of effective waste management strategies. In particular, we use several machine learning data regression (Support Vector, Gradient Boosting, and Random Forest) and clustering models (k-means) to respectively predict waste production for OECD countries along years and also to perform clustering among these countries according to similar characteristics. The main contributions of our work are: (1) waste analysis at the OECD country-level to compare and cluster countries according to similar waste features predicted; (2) the detection of most relevant features for prediction models; and (3) the comparison between several regression models with respect to accuracy in predictions. Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), respectively, are used as indices of the efficiency of the developed models. Our experiments have shown that some data pre-processings on the OECD data are an essential stage required in the analysis; that Random Forest Regressor (RFR) produced the best prediction results over the dataset; and that these results are highly influenced by the quality of available socio-economic data. In particular, the RFR model exhibited the highest accuracy in predictions for most waste types. For example, for “municipal” waste, it produced, respectively, R2 = 1 and MAPE=4.31 global error values for the test set; and for “household” waste, it, respectively, produced R2 = 1 and MAPE=3.03. Our results indicate that the considered models (and specially RFR) all are effective in predicting the amount of produced wastes derived from input data for the considered countries.

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16

Konovalov, V. A. "CATEGORY BIG DATA MODEL OF THE SOCIO-ECONOMIC SYSTEM." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 210 (December 2021): 36–42. http://dx.doi.org/10.14489/vkit.2021.12.pp.036-042.

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The socioeconomic systems in which the tasks of countering the legalization (laundering) of proceeds from crime and the financing of terrorism are solved are considered. An intermediate conclusion is presented and made that it is necessary to synthesize a problem-oriented control subsystem for such systems and tasks. The management tasks that distinguish such a data model in modern conditions are highlighted. It is proposed to use a categorical-theoretic approach for synthesizing a data model, which is free from the mathematical paradoxes of mathematical sets, and is also not limited by the size of the data sample from very large samples, such as big data. The purpose of modeling is to synthesize a mathematical model of big data from a variety of data sources of socioeconomic systems, which provides the classification of objects in interrelation (interaction) with other objects to identify such objects and relationships (interactions) that relate to legalization (laundering) proceeds from crime and financing of terrorism. The input samples are mathematically defined. A mathematical model of big data is synthesized in terms of category theory. The module provides the representation of input data samples in the form (format), which makes it possible to classify objects and categories of socioeconomic systems.

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17

Agbozo, Ebenezer, and Benjamin Kwesi Asamoah. "Data-driven E-Government: Exploring the Socio-Economic Ramifications." JeDEM - eJournal of eDemocracy and Open Government 11, no. 1 (December 16, 2019): 81–90. http://dx.doi.org/10.29379/jedem.v11i1.510.

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The evident benefits of big data, artificial intelligence and machine learning in society have begun to influence the transition towards a data-driven public sector. Decision-making in the public sector is in an infancy phase of a revolution owing to the inclusion of these new technological innovations. Research has revealed that data-driven e-government policies improve socio-economic development in some nations. Despite the immense opportunities data-driven e-government models have for governments, similar to every system, there are ramifications. This study explores the concept of data-driven e-government as well as investigates the socio-economic implications such an e-government model can have on society. Findings of this exploratory study add insight into a field which is in its early days and still unfocused, as well as making recommendations for policymakers.

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18

Longley, Paul A. "Geographical Information Systems: developments in socio-economic data infrastructures." Progress in Human Geography 27, no. 1 (February 2003): 114–21. http://dx.doi.org/10.1191/0309132503ph415pr.

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19

Breidenbach, Philipp, and Lea Eilers. "RWI-GEO-GRID: Socio-economic data on grid level." Jahrbücher für Nationalökonomie und Statistik 238, no. 6 (October 25, 2018): 609–16. http://dx.doi.org/10.1515/jbnst-2017-0171.

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20

Ruíz, Ricardo, Christopher R. Stephens, and Santiago Roel Rodríguez. "Crime Data Mining: Combining Socio-economic and Spatial Analysis." Research in Computing Science 100, no. 1 (December 31, 2015): 75–88. http://dx.doi.org/10.13053/rcs-100-1-7.

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21

Carroll, L. "An information jungle: socio-economic data for water management." Australasian Journal of Environmental Management 17, no. 2 (June 2010): 88–99. http://dx.doi.org/10.1080/14486563.2010.9725254.

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22

Ahn, Namkee, and Virginia Sánchez-Marcos. "Analysis of fertility using cohort-specific socio-economic data." Review of Economics of the Household 18, no. 3 (May 29, 2019): 711–33. http://dx.doi.org/10.1007/s11150-019-09455-0.

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23

ABESADZE, NINO, NATALIA ROBITASHVILI, and OTAR ABESADZE. "SOCIO-ECONOMIC-GENDER DISBALANCES IN GEORGIA." Globalization and Business 4, no. 7 (June 25, 2019): 143–47. http://dx.doi.org/10.35945/gb.2019.07.017.

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Introduction and aim According to the policy towards gender equality, Georgia has become one of the most reformed countries in the Caucasus. Despite the fact that many steps have been taken in this regard, the problems are still in the society. The main objective of the study is to identify and analyze gender imbalances in the country on the background of gender stereotypes in the context of civil cognitive levels. Research methodology: In development of the article, generally accepted qualitative and quantitative data analysis methods of the economic science were employed, among them, statistical data processing, data grouping, inductive deductive data analysis methods. The scientific study employs surveying, observation, as well as comparative, analytical, and graphical methods, which are used by the author to compare and analyze facts and assess solutions to specific issues. Results and implications: The paper considers the gender aspects of Discussion of inequality problems in Georgia. Besides, the trends of unemployment and their reasons are identified and the international instruments of protection of human rights regulating the equality between the men and the women in Georgia are considered. In addition, based on the statistical data, is given the analysis of employment and unemployment in a gender respect; a particular accent is made on the need of International practice of gender protection. Conclusion: Stereotypical views on gender roles still exist in Georgia: the function of women is the care and upbringing of children, doing homework, and man-taking care of family finances. The fact is that more men employed in Georgia than women. The level of inactivity in women is almost twice as high as in men with a similar indicator of 42.0% and 21.8% respectively. In 2017, unemployment rate for women Increased by 1.8 percentage points to 12.7 percent, and for men decreased by 1.6 percentage points and by 15 percent. Changes for to the same period are also indicators of activity level. The level of activity increased realised by 1.5 percentage points for women and decreased by 2.8 percentage points for men and 58.2 percent and 74.6 percent respectively. Interesting trends are shown by employment indicators according to age marks. The number of men employed by the 45-54 age group is significantly higher than the number of women employed for the whole nine years, indicating the employment rate of women, especially in the 15-24 age group where the number of men employed is twice as high as the number of women employed. It is also true that the average level of compensation for women is lower than men. Their rates of growth are also different.

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McLaughlin, S., J. McKenna, and J. A. G. Cooper. "Socio-economic data in coastal vulnerability indices: constraints and opportunities." Journal of Coastal Research 36 (March 2002): 487–97. http://dx.doi.org/10.2112/1551-5036-36.sp1.487.

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25

Siedler, Thomas, Jürgen Schupp, C. Katharina Spiess, and Gert G. Wagner. "The German Socio-Economic Panel (SOEP) as Reference Data Set." Schmollers Jahrbuch 129, no. 2 (April 2009): 367–74. http://dx.doi.org/10.3790/schm.129.2.367.

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26

Tikhomirov, B. I. "Creating and Using Big Data for Socio-Economic Development Management." Economics, taxes & law 13, no. 2 (2020): 103–12. http://dx.doi.org/10.26794/1999-849x-2020-13-2-103-112.

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27

Goswami, P. R. "Access to Socio-Economic Data with Particular Reference to India." DESIDOC Bulletin of Information Technology 18, no. 4 (July 1, 1998): 29–38. http://dx.doi.org/10.14429/dbit.18.4.3405.

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28

Kjærgaard, S⊘ren, Yunus Emre Ergemen, Marie‐Pier Bergeron‐Boucher, Jim Oeppen, and Malene Kallestrup‐Lamb. "Longevity forecasting by socio‐economic groups using compositional data analysis." Journal of the Royal Statistical Society: Series A (Statistics in Society) 183, no. 3 (March 2, 2020): 1167–87. http://dx.doi.org/10.1111/rssa.12555.

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Alfó, Marco, and Francesco Bartolucci. "Latent variable models for the analysis of socio-economic data." METRON 73, no. 2 (August 2015): 151–54. http://dx.doi.org/10.1007/s40300-015-0074-3.

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Konovalov, V. A. "СLASSIFIER OF BIG DATA OBJECTS OF THE SOCIO-ECONOMIC SYSTEM." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 212 (February 2022): 32–39. http://dx.doi.org/10.14489/vkit.2022.02.pp.032-039.

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The well-known models that provide the synthesis of object classifiers are analyzed: the Grothendieck topos model, Kripke’s intuitionistic model and the model of the normal Markov algorithm, in which similar approaches to the representation of objects are highlighted. The differences in the classifiers used in them are highlighted. A categorical-theoretical model of topos has been developed, in which sets are represented by n-categories. A generalized classifier has been developed that includes the properties of the Grothendieck, Kripke and Markov classifiers. An N-scheme is synthesized to replace the g-scheme of the normal Markov algorithm. A model of n-category topos has been developed and theoretically substantiated, in which the compositions of morphisms of an object are specified by an N-scheme. The alphabet of the n-category associator is synthesized, which provides the representation of non-associative compositions of morphisms, interacting objects, taking into account the assumptions laid down in the Kripke model. A database has been developed that implements the synthesized topos model. It is noted that such a database can be implemented as a cascade of unordered containers with the computation of hash functions to quickly find and retrieve keys and values. For some keys, such a base, it is allowed to duplicate a key in containers. The database provides a synthesis of self-learning on the input sampling of artificial intelligence data and supported by control lists of objects of selection, exclusion and deletion. The normal Markov algorithm is adapted to the theoretical model of the Grothendieck topos, taking into account the assumptions of the Kripke model, by replacing the g-scheme with the synthesized N-scheme.

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31

Petrushenko, Yuriy, Fedir Zhuravka, Vladyslav Shapoval, Lyudmila Khomutenko, and Olena Zhuravka. "Sustainable socio-economic development and Rainbow Europe Index." Problems and Perspectives in Management 19, no. 4 (December 21, 2021): 408–21. http://dx.doi.org/10.21511/ppm.19(4).2021.33.

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The issues of recognizing the rights of the LGBTQ+ community around the world and developing appropriate anti-discrimination policies and laws are one of the main topics for discussion in the global agenda. This is due to the commitment of the world community to protect human rights and meet the needs of society. The paper aims to assess the relationship between socio-economic development indicators of some European countries and the Rainbow Europe Index. To find out how discrimination against the LGBTQ+ community affects various social and economic development indicators of some European countries, a data matrix was developed and the Spearman rank correlation coefficient was calculated. The obtained results confirmed a positive relationship between the Rainbow Europe Index and GDP per capita, the Human Development Index, the Corruption Index, and the Index of Happiness. Calculations have shown that the Rainbow Europe Index had a significant impact on these indicators. The study proved the dependence of indicators and demonstrated the need to provide freedoms and rights for LGBTQ+ affiliated members in Ukraine and other European countries. AcknowledgmentThis paper is published as a part of research projects “Convergence of economic and educational transformations in the digital society: modeling the impact on regional and national security” (No. 0121U109553) and “Reforming the lifelong learning system in Ukraine for the prevention of the labor emigration: a coopetition model of institutional partnership” (No. 0120U102001).

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Nurul Qomariyah, Siti, Harti, and Hariyati. "Social Interaction, Socio-Economic Status, and Basic Economic Knowledge of Students' Economic Behavior." Jurnal Pendidikan Ekonomi Dan Bisnis (JPEB) 7, no. 2 (September 30, 2019): 101–11. http://dx.doi.org/10.21009/jpeb.007.2.1.

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This study aims to determine the effect of social interaction, socioeconomic status, and basic economic knowledge on the economic behavior of MAN students in the Pati Regency. This research uses a quantitative research approach. This study uses primary data obtained from distributing questionnaires to students. The population in this study amounted to 266 students. Participants in this study are 160 students with a proportional random sampling technique. The data analysis technique used is multiple linear regression analysis. Based on the results of data analysis, there is an influence of social interaction, socioeconomic status, and basic economic knowledge on the economic behavior of MAN students in Pati Regency.

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Schröder, Carsten, Johannes König, Alexandra Fedorets, Jan Goebel, Markus M. Grabka, Holger Lüthen, Maria Metzing, Felicitas Schikora, and Stefan Liebig. "The economic research potentials of the German Socio-Economic Panel study." German Economic Review 21, no. 3 (September 26, 2020): 335–71. http://dx.doi.org/10.1515/ger-2020-0033.

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AbstractWe provide a concise introduction to a household-panel data infrastructure that provides the international research community with longitudinal data of private households in Germany since 1984: the German Socio-Economic Panel (SOEP). We demonstrate the comparative strength of the SOEP data in answering economically-relevant questions by highlighting its diverse and impactful applications throughout the field.

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34

Turrell, Gavin, Belinda Hewitt, Carla Patterson, and Brian Oldenburg. "Measuring socio-economic position in dietary research: is choice of socio-economic indicator important?" Public Health Nutrition 6, no. 2 (April 2003): 191–200. http://dx.doi.org/10.1079/phn2002416.

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AbstractObjectives:To examine the association between socio-economic position (SEP) and diet, by assessing the unadjusted and simultaneously adjusted (independent) contributions of education, occupation and household income to food purchasing behaviourDesign:The sample was randomly selected using a stratified two-stage cluster design, and the response rate was 66.4%. Data were collected by face-to-face interview. Food purchasing was examined on the basis of three composite indices that reflected a household's choice of grocery items (including meat and chicken), fruit and vegetablesSetting:Brisbane City, Australia, 2000Participants:Non-institutionalised residents of private dwellings (n = 1003), located in 50 small areas (Census Collectors Districts)Results:When shopping, respondents in lower socio-economic groups were less likely to purchase grocery foods that were high in fibre and low in fat, salt and sugar. Disadvantaged groups purchased fewer types of fresh fruits and vegetables, and less often, than their counterparts from more advantaged backgrounds. When the relationship between SEP and food purchasing was examined using each indicator separately, education and household income made an unadjusted contribution to purchasing behaviour for all three food indices; however, occupation was significantly related only with the purchase of grocery foods. When education and occupation were simultaneously adjusted for each other, the socio-economic patterning with food purchase remained largely unchanged, although the strength of the associations was attenuated. When household income was introduced into the analysis, the association between education, occupation and food purchasing behaviour was diminished or became non-significant; income, however, showed a strong, graded association with food choiceConclusions:The food purchasing behaviours of socio-economically disadvantaged groups were least in accord with dietary guideline recommendations, and hence are more consistent with greater risk for the development of diet-related disease. The use of separate indicators for education, occupation and household income each adds something unique to our understanding of how socio-economic position is related to diet: each indicator reflects a different underlying social process and hence they are not interchangeable, and do not serve as adequate proxies for one another

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35

Zhuravleva, Lyudmila, Elena Zarubina, and Aleksey Ruchkin. "Nutrition as a socio-economic institution." Agrarian Bulletin of the, no. 13 (January 29, 2021): 10–18. http://dx.doi.org/10.32417/1997-4868-2021-13-10-18.

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Abstract. Purpose. The article describes the economic component of routine eating behavior. Based on the ideas of the structural-functional paradigm and classical economic institutionalism, as well as the data of a sociological study conducted by the authors using the original methodology, the analysis of nutrition as a socio-economic institution that directly affects the distribution of resources and household costs is presented. Methods. To test the hypothesis of the formation of institutionally regulated food practices in modern society, the authors conducted a sociological study in September 2020. An integrative research strategy was applied using qualitative and quantitative methods of collecting social information, ensuring the quality and reliability of the data obtained. Implementing qualitative methods, we used narrative interviews, included observation, focus groups and written surveys (provoked by researchers essays on nutrition problems, presented by respondents in free form). The quantitative method was presented by a pilot smart survey (N = 75). Results. Conclusions: on the formation of a model of healthy nutrition in the majority of respondents and that nutrition increasingly acquires the features of a social institution and depends on the functioning of other social institutions in the economic, political, social and cultural spheres. First of all, the role of the state, the institution of property, education, culture, science is increasing, and their role in the formation of ecological healthy consumption of various population groups is increasing. Scientific novelty. Programs of social and economic support of vulnerable segments of the population, creation of conditions for an exit from “coronavirus crisis” are necessary. The problem of food security requires innovative programs for the development of the agro-industrial complex, modernization of logistics supply chains of food “from field to counter”, the creation of large wholesale distribution centers and investments in agricultural science.

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Präg, Patrick. "Subjective socio-economic status predicts self-rated health irrespective of objective family socio-economic background." Scandinavian Journal of Public Health 48, no. 7 (June 7, 2020): 707–14. http://dx.doi.org/10.1177/1403494820926053.

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Aim: Subjective appraisals of socio-economic status (SES) are robustly associated with health outcomes, even when controlling for objective SES. Is this because objective SES is not accounted for in a sufficiently exhaustive way? Methods: I pool eight waves of nationally representative survey data from Germany (German General Social Survey, 2004–18, N=13,557) to assess the association between two separate subjective appraisals of SES (a 10-point scale and subjectively chosen social class membership) and poor self-rated health using logit and linear probability models. I account for an exhaustive range of objective SES variables, including respondents’ household incomes and social status, as well as occupational status, social class and education of respondents and of their partners, fathers and mothers. Results: The association between subjective SES and poor self-rated health remains stable, even when accounting for a wide range of objective SES markers. This is true for both subjective SES measured on a 10-point scale and as a subjective class identification. Conclusions: Even when controlling for a large number of objective SES markers, subjective SES and self-rated health are linked, suggesting that subjective assessments of SES are meaningful measures of SES which form a distinct pathway to health.

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Kalim, Inayat, Syeda Zohra Jabeen Naqvi, and Muhammad Mubeen. "Socio-economic Disparities in Balochistan: Assessing StructuralEtiology." Global Economics Review III, no. I (June 30, 2018): 134–54. http://dx.doi.org/10.31703/ger.2018(iii-i).14.

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This study attempts to explain the insurgency in Balochistan in the context of existing socio-economic disparities. Structural etiology is a systemic and institutionalized deprivation of the people to their needs and rights. The empirical evidence suggests that violence in Balochistan is a complex phenomenon triggered by numerous instigators. The data on socio-economic indicators testifies that Balochistan lags behind the rest of the provinces on all important indicators, which points towards structural negligence. From the analysis of the data collected through interviews, socio-economic discrimination has been an influential factor that promoted Baloch insurgency.

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Din, Shamas-ud, and Naimatullah Hashmi. "Socio-economic investigation of osteoporosis patients." Masyarakat, Kebudayaan dan Politik 33, no. 2 (June 24, 2020): 153. http://dx.doi.org/10.20473/mkp.v33i22020.153-161.

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Osteoporosis has become a public health problem risk factor worldwide and a deliberate disease that affects both women and men equally. The main objective of this study is to explore the socio-economic status of osteoporosis patients related to the occurrence of osteoporosis— the current study conducted at federal government hospitals in Islamabad. A quantitative research method used to collect data through a well-designed questionnaire. A stratified random sampling technique used to select a sample of 400 osteoporosis patients within 35-80 years. Mann-Whitney U Test applied for a dependent, independent variable, and frequency distribution tables used for univariate analysis. Results showed that osteoporosis patients were spending more money on their household expenditures than health. The socio-economic status of osteoporosis patients is essential for the prevention of osteoporosis and their treatment. Osteoporosis is one of the major global health issues associated with socio-economic status. The present study recommended that the improvement of socio-economic status and increasing the level of education could play a vital role in reducing the risks of osteoporosis.

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Ogay, М. Yu. "Problems of Quality Assurance for Socio-Economic Data at Local Level." Statistics of Ukraine 89, no. 2-3 (November 24, 2020): 22–31. http://dx.doi.org/10.31767/su.2-3(89-90)2020.02-03.03.

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The process of decentralization involves the enhanced interest of local power bodies in carrying out an effective benchmarking at local level, along with the increasing demand on relevant and reliable information on the development of territorial communities. Today, the local self-government faces financial and technical limitations in creating effective local systems for information support. It follows that the only way of producing a unified statistical support as a tool for performance analysis at local level is developing a centralized information system. The article contains an analysis of the quality of the existing centralized information system, which viability is supported by the Association of Ukrainian Cities. The computerized system of municipal statistics, developed in Ukraine, is an online system for collection, storage and processing of information on the core areas of life activities of territorial communities and self-government bodies. The system is based on the advanced principles of creation and operation of information products, whereas its organization, structure and technological tooling on the whole is compliant with analogous systems used in other spheres and in some other countries. However, most part of the indicators for which the data are collected and processed by the system does not comply with the core dimensions of quality, namely: relevance (topicality) / compliance with user needs; accuracy/reliability of estimated figures; timeliness and punctuality of production and publishing of processed data; accessibility and comprehensibility/clearness of metadata; comparability; consistency/coherence of indicators in space and time. The author’s study laid the basis for the elaborated recommendations on quality enhancement for the data collected at municipal level. A reliable information system must meet the following requirements: compliance of the system’s purpose and functions with user needs; coverage of the maximal number of administrative-territorial units; free access for users by hierarchical approach; availability of computerized data conversion; optimization of the set of primary, secondary and summary indicators and metadata that are developed by standard forms; use of advanced methods for data analysis and presentation given well-developed monitoring capabilities; viability and provision of appropriate professional support. If implemented, these recommendations will enable to form the solid information basis for comprehensive monitoring of life activities of cities and local communities in Ukraine.

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Ogay, М. Yu. "Problems of Quality Assurance for Socio-Economic Data at Local Level." Statistics of Ukraine 89, no. 2-3 (November 24, 2020): 22–31. http://dx.doi.org/10.31767/su.2-3(89-90)2020.02-03.03.

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The process of decentralization involves the enhanced interest of local power bodies in carrying out an effective benchmarking at local level, along with the increasing demand on relevant and reliable information on the development of territorial communities. Today, the local self-government faces financial and technical limitations in creating effective local systems for information support. It follows that the only way of producing a unified statistical support as a tool for performance analysis at local level is developing a centralized information system. The article contains an analysis of the quality of the existing centralized information system, which viability is supported by the Association of Ukrainian Cities. The computerized system of municipal statistics, developed in Ukraine, is an online system for collection, storage and processing of information on the core areas of life activities of territorial communities and self-government bodies. The system is based on the advanced principles of creation and operation of information products, whereas its organization, structure and technological tooling on the whole is compliant with analogous systems used in other spheres and in some other countries. However, most part of the indicators for which the data are collected and processed by the system does not comply with the core dimensions of quality, namely: relevance (topicality) / compliance with user needs; accuracy/reliability of estimated figures; timeliness and punctuality of production and publishing of processed data; accessibility and comprehensibility/clearness of metadata; comparability; consistency/coherence of indicators in space and time. The author’s study laid the basis for the elaborated recommendations on quality enhancement for the data collected at municipal level. A reliable information system must meet the following requirements: compliance of the system’s purpose and functions with user needs; coverage of the maximal number of administrative-territorial units; free access for users by hierarchical approach; availability of computerized data conversion; optimization of the set of primary, secondary and summary indicators and metadata that are developed by standard forms; use of advanced methods for data analysis and presentation given well-developed monitoring capabilities; viability and provision of appropriate professional support. If implemented, these recommendations will enable to form the solid information basis for comprehensive monitoring of life activities of cities and local communities in Ukraine.

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Shevchuk, E. I., P. L. Kirillov, and A. N. Petrosian. "Data generalization for spatial socio-economic disparities on different research scales." Regional'nye issledovaniya, no. 3 (2019): 4–15. http://dx.doi.org/10.5922/10.5922/1994-5280-2019-3-1.

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This paper reviews aspects of utilizing various research scales (polyscale method) in spatial heterogeneity studies. Analysis is based on socio-economic data for different subdivision levels in Russia – municipalities, regions and economic regions (‘rayon’). Operating on lower levels of spatial organization, in accordance with fundamentals of statistics theory, is determined as the source of an increase in the ratio of observed heterogeneity. Yet, the loss of information on the rate of heterogeneity is observed when applying polyscale method. Such inaccuracies occur owing to the continuity of space and artificial nature of geographical borders. Generalization ratio which defines the difference of heterogeneity indices on spatial scales is proposed as a measure of polyscale heterogeneity. The ratio allows to distinguish the scale levels with the highest variety of spatial units, along with the degree of actual geographical contribution to the heterogeneity indicators. Calculations example on data for microregions, as proposed by E. Leizerovich, shows considerable differences between actual and assessed heterogeneity. Application of the method in research studies is proposed, e.g. typology assessment or multi-level forecasting.

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Feldmeyer, Daniel, Claude Meisch, Holger Sauter, and Joern Birkmann. "Using OpenStreetMap Data and Machine Learning to Generate Socio-Economic Indicators." ISPRS International Journal of Geo-Information 9, no. 9 (August 21, 2020): 498. http://dx.doi.org/10.3390/ijgi9090498.

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Socio-economic indicators are key to understanding societal challenges. They disassemble complex phenomena to gain insights and deepen understanding. Specific subsets of indicators have been developed to describe sustainability, human development, vulnerability, risk, resilience and climate change adaptation. Nonetheless, insufficient quality and availability of data often limit their explanatory power. Spatial and temporal resolution are often not at a scale appropriate for monitoring. Socio-economic indicators are mostly provided by governmental institutions and are therefore limited to administrative boundaries. Furthermore, different methodological computation approaches for the same indicator impair comparability between countries and regions. OpenStreetMap (OSM) provides an unparalleled standardized global database with a high spatiotemporal resolution. Surprisingly, the potential of OSM seems largely unexplored in this context. In this study, we used machine learning to predict four exemplary socio-economic indicators for municipalities based on OSM. By comparing the predictive power of neural networks to statistical regression models, we evaluated the unhinged resources of OSM for indicator development. OSM provides prospects for monitoring across administrative boundaries, interdisciplinary topics, and semi-quantitative factors like social cohesion. Further research is still required to, for example, determine the impact of regional and international differences in user contributions on the outputs. Nonetheless, this database can provide meaningful insight into otherwise unknown spatial differences in social, environmental or economic inequalities.

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MARTIN, DAVID, and GARY HIGGS. "The visualization of socio-economic GIS data using virtual reality tools." Transactions in GIS 1, no. 4 (October 1996): 255–66. http://dx.doi.org/10.1111/j.1467-9671.1996.tb00050.x.

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Cherepanova, E. S., E. S. Kiseleva, S. I. Perminov, and A. V. Tarasov. "MATHEMATICAL-CARTOGRAPHIC MODELING IN SOCIO- ECONOMIC MAPPING: PECULIARITIES OF DATA VISUALIZATION." Географический вестник = Geographical bulletin, no. 2 (2017): 137–47. http://dx.doi.org/10.17072/2079-7877-2017-2-137-147.

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Babongo, Flora, Tapio Niemi, Valérie Chavez Demoulin, Ari Pekka Hameri, and Patrik Appelqvist. "Forecasting (un-)seasonal demand using geostatistics, socio-economic and weather data." International Journal of Business Forecasting and Marketing Intelligence 5, no. 1 (2019): 103. http://dx.doi.org/10.1504/ijbfmi.2019.099069.

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Hameri, Ari Pekka, Tapio Niemi, Patrik Appelqvist, Flora Babongo, and Valérie Chavez Demoulin. "Forecasting (un-)seasonal demand using geostatistics, socio-economic and weather data." International Journal of Business Forecasting and Marketing Intelligence 1, no. 1 (2019): 1. http://dx.doi.org/10.1504/ijbfmi.2019.10019287.

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Šćepanović, Sanja, Igor Mishkovski, Pan Hui, Jukka K. Nurminen, and Antti Ylä-Jääski. "Mobile Phone Call Data as a Regional Socio-Economic Proxy Indicator." PLOS ONE 10, no. 4 (April 21, 2015): e0124160. http://dx.doi.org/10.1371/journal.pone.0124160.

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Tavares, Aida Isabel. "Infant mortality in Europe, socio-economic determinants based on aggregate data." Applied Economics Letters 24, no. 21 (June 29, 2017): 1588–96. http://dx.doi.org/10.1080/13504851.2017.1340565.

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Curtis, Allan, Ian Byron, and Jacinta MacKay. "INTEGRATING SOCIO-ECONOMIC AND BIOPHYSICAL DATA TO UNDERPIN COLLABORATIVE WATERSHED MANAGEMENT." Journal of the American Water Resources Association 41, no. 3 (June 2005): 549–63. http://dx.doi.org/10.1111/j.1752-1688.2005.tb03754.x.

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Šlachtová, Hana, Hana Tomášková, Anna Šplíchalová, Pavla Polaufová, and Petra Fejtková. "Czech socio-economic deprivation index and its correlation with mortality data." International Journal of Public Health 54, no. 4 (February 11, 2009): 267–73. http://dx.doi.org/10.1007/s00038-008-7092-3.

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