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Predicting well-being based on features visible from space – the case of Warsaw

Author

Listed:
  • Krystian Andruszek

    (Data Science Lab WNE UW)

  • Piotr Wójcik

    (Faculty of Economic Sciences, Data Science Lab WNE UW, University of Warsaw)

Abstract

In recent years, availability of satellite imagery has grown rapidly. In addition, deep neural networks gained popularity and become widely used in various applications. This article focuses on using innovative deep learning and machine learning methods with combination of data that is describing objects visible from space. High resolution daytime satellite images are used to extract features for particular areas with the use of transfer learning and convolutional neural networks. Then extracted features are used in machine learning models (LASSO and random forest) as predictors of various socio-economic indicators. The analysis is performed on a local level of Warsaw districts. The findings from such approach can be a great help to get almost continuous measurement of the economic well-being, independently of statistical offices.

Suggested Citation

  • Krystian Andruszek & Piotr Wójcik, 2020. "Predicting well-being based on features visible from space – the case of Warsaw," Working Papers 2020-37, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2020-37
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    File URL: https://www.wne.uw.edu.pl/index.php/download_file/5904/
    File Function: First version, 2020
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    References listed on IDEAS

    as
    1. Frank Bickenbach & Eckhardt Bode & Peter Nunnenkamp & Mareike Söder, 2016. "Night lights and regional GDP," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 152(2), pages 425-447, May.
    2. Charlotta Mellander & José Lobo & Kevin Stolarick & Zara Matheson, 2015. "Night-Time Light Data: A Good Proxy Measure for Economic Activity?," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-18, October.
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    Cited by:

    1. Federico Ninivaggi & Eleonora Cutrini, 2024. "Exploring local well-being and vulnerability through OpenStreetMap: the case of Italy," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(4), pages 3435-3473, August.

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    More about this item

    Keywords

    well-being; economic indicators; Open Street Map; satellite images; Warsaw;
    All these keywords.

    JEL classification:

    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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