Data mining of sustainable development process with using nightlight indicators

Authors

DOI:

https://doi.org/10.15587/2312-8372.2019.172157

Keywords:

data mining, sustainable development, nightlight luminocity, spatial regression, geospatial analysis

Abstract

The object of research is the process of sustainable development of territorial units on the example of the regions of Ukraine. The concept of sustainable development has become a leading development strategy for most countries in the world. One of the biggest challenges is the availability of complete and verified data for sustainable development assessment models. The method used to calculate the index of sustainable development, developed in the World Data Center for Geoinformatics and Sustainable Development of the National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute». This methodology is based on calculating the metric of the index of sustainable development based on measurements of the quality of life of the population and the components of the safety of living for individual countries and regions. For the application of the methodology at the regional level, it was suggested to use information on the night lighting of the territory received by means of remote sensing of the Earth from satellites. The character and cohesiveness of the connection between the brightness of night lighting and indicators of sustainable development are studied. It is established that the most significant link exists between indicators of the economic development index, the index of influence on climate change and night lighting of the regions of Ukraine. Based on geographic information analysis of ArcGIS software, ESRI has applied statistical zoning tools that provide opportunities for statistical processing of satellite images within the regionally or regionally allocated areas.

On the basis of the mathematical apparatus of the intellectual data analysis, a global and local regression analysis of the relationships between the revealed indicators was carried out. Consideration of the cramped nature of this connection in the territorial section revealed areas with varying degrees of close ties, which is explained by the peculiarities of the socio-economic development of the territories. Due to this, it is possible to obtain the calculation of similar indicators at more detailed territorial levels, corresponding to separate regions or cities of regional significance.

Author Biographies

Viktor Putrenko, National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute», 37, Peremohy ave., Kyiv, Ukraine, 03056

PhD, Senior Researcher

Education and Scientific Complex «World Data Center for Geoinformatics and Sustainable Development»

Nataliia Pashynska, Taras Shevchenko National University of Kyiv, 60, Volodymyrska str., Kyiv, Ukraine, 01033

PhD, Senior Researcher

Department of Intellectual and Information Systems

References

  1. Revised list of global Sustainable Development Goal indicators. Available at: https://unstats.un.org/sdgs/indicators/Official%20Revised%20List%20of%20global%20SDG%20indicators.pdf Last accessed: 26.06.2019
  2. The Sustainable Development Goals Report 2016 (2016). UN. Available at: http://ggim.un.org/documents/The%20Sustainable%20Development%20Goals%20Report%202016.pdf
  3. Prototype Global Sustainable Development Report – Executive Summary (2013). UN-DESA. Available at: https://sustainabledevelopment.un.org/content/documents/975GSDR%20Executive%20Summary.pdf
  4. Global Sustainable Development Report – 2016 (2016). UN. Available at: https://sustainabledevelopment.un.org/content/documents/2328Global%20Sustainable%20development%20report%202016%20(final).pdf
  5. The Applied Remote Sensing Training (ARSET) program (2017). NASA. Available at: https://arset.gsfc.nasa.gov/
  6. Satellite Derived Annual PM2.5 Data Sets in Support of United Nations Sustainable Development Goals (2017). NASA. Available at: https://arset.gsfc.nasa.gov/airquality/webinars/AQ-SDG-17
  7. Putrenko, V. V., Pashynska, N. M. (2017). The use of remote sensing data for modeling air quality in the cities. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-5/W1, 57–62. doi: http://doi.org/10.5194/isprs-annals-iv-5-w1-57-2017
  8. Putrenko, V. (2017). Data mining of relationship in crowdsourcing projects and social activities of citizens. Proceedings IEEE 1st Ukraine Conference on Electrical and Computer Engineering (UKRCON), 1060–1065. doi: http://doi.org/10.1109/ukrcon.2017.8100413
  9. Remote Sensing of Land Indicators for Sustainable Development Goal 15. NASA. Available at: https://arset.gsfc.nasa.gov/land/webinars/sdg15
  10. Lieskovsky, A. J. (2014). Electricity Use as an Indicator of U.S. Economic Activity. Washington: Energy Information Administration, 87.
  11. Henderson, J. V., Storeygard, A., Weil, D. N. (2012). Measuring Economic Growth from Outer Space. American Economic Review, 102 (2), 994–1028. doi: http://doi.org/10.1257/aer.102.2.994
  12. Coupe, T., Myck, M., Najsztub, M. (2016). And the Lights Went Out — Measuring the Economic Situation in Eastern Ukraine. VoxUkraine. Available at: https://voxukraine.org/en/and-the-lights-went-out-measuring-the-economic-situation-in-eastern-ukraine-en/
  13. Lyalko, V. I., Sakhatsky, O. I., Elistratova, L. O., Аpostolov, A. A. (2017). Use of space images of NPP / VIIRS at night to assess the economic crisis in the East of Ukraine (Donetsk and Luhansk regions). Bulletin of the National Academy of Sciences of Ukraine, 2, 48–53. doi: http://doi.org/10.15407/visn2017.02.048
  14. Zghurovskyi, M. Z., Akimova, O. A., Boldak, A. O., Vavulin, P. L., Dzhyhyrei, I. M., Yefremov, K. V., Putrenko, V. V. (2014). Sustainable Development Analysis: Global and Regional Contexts. Part 2. Ukraine in the Indicators of Sustainable Development (2013). Kyiv: NTUU «KPI», 172.
  15. Zgurovsky, M. Z., Boldak, A. A., Yefremov, K. V. (2013). Intelligent analysis and the systemic adjustment of scientific data in interdisciplinary research. Cybernetics and Systems Analysis, 49 (4), 541–552. doi: http://doi.org/10.1007/s10559-013-9539-7

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Published

2019-06-30

How to Cite

Putrenko, V., & Pashynska, N. (2019). Data mining of sustainable development process with using nightlight indicators. Technology Audit and Production Reserves, 3(2(47), 4–8. https://doi.org/10.15587/2312-8372.2019.172157

Issue

Vol. 3 No. 2(47) (2019): Information and control systems

Section

Information Technologies: Original Research

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Copyright (c) 2019 Viktor Putrenko, Nataliia Pashynska

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