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2024 | OriginalPaper | Buchkapitel

12. A Mixed Method Approach to Estimate Intra-urban Distribution of GDP in Conditions of Data Scarcity

verfasst von : Jessica P. Salazar, Jorge E. Patiño, Jairo A. Gómez, Juan C. Duque

Erschienen in: Urban Inequalities from Space

Verlag: Springer International Publishing

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Abstract

The gross domestic product (GDP) is probably the most important economic measurement worldwide. Although it is officially reported at the country level, the heterogeneities within the countries sparked interest in estimating the GDP at the city level. In this chapter, we introduce an innovative mixed method approach for the spatial disaggregation of the GDP at the intra-urban level. The method uses open data from satellite imagery, street networks, and distributed population counts, combined with local experts’ knowledge. The use of open datasets and local experts’ knowledge makes it possible to use the proposed method in cities with scarce data and small budgets. We use Medellin (Colombia) as a case study. We found that this approach performs better in those areas of the city associated with medium-to-low incomes, whereas the precision decreases in middle to high-income areas.

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Titel: A Mixed Method Approach to Estimate Intra-urban Distribution of GDP in Conditions of Data Scarcity
verfasst von: Jessica P. Salazar
Jorge E. Patiño
Jairo A. Gómez
Juan C. Duque
Verlag: Springer International Publishing
Buch: Urban Inequalities from Space
Print ISBN: 978-3-031-49182-5

Electronic ISBN: 978-3-031-49183-2

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-49183-2_12

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