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

Krystian Andruszek and Piotr Wójcik
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Krystian Andruszek: Data Science Lab WNE UW

No 2020-37, Working Papers from Faculty of Economic Sciences, 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.

Keywords: well-being; economic indicators; Open Street Map; satellite images; Warsaw (search for similar items in EconPapers)
JEL-codes: C14 I31 O18 R12 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2020
New Economics Papers: this item is included in nep-big and nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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https://www.wne.uw.edu.pl/index.php/download_file/5904/ First version, 2020 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2020-37

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