High‐Resolution Downscaling of Disposable Income in Europe Using Open‐Source Data
Abstract Income maps have been extensively used for identifying populations vulnerable to global changes. The frequency and intensity of extreme events are likely to increase in coming years as a result of climate change. In this context, several studies have hypothesized that the economic and socia...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Earth's Future |
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| Online Access: | https://doi.org/10.1029/2024EF004576 |
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| author | Mehdi Mikou Améline Vallet Céline Guivarch David Makowski |
| author_facet | Mehdi Mikou Améline Vallet Céline Guivarch David Makowski |
| author_sort | Mehdi Mikou |
| collection | DOAJ |
| description | Abstract Income maps have been extensively used for identifying populations vulnerable to global changes. The frequency and intensity of extreme events are likely to increase in coming years as a result of climate change. In this context, several studies have hypothesized that the economic and social impact of extreme events depend on income. However, to rigorously test this hypothesis, fine‐scale spatial income data is needed, compatible with the analysis of extreme climatic events. To produce reliable high‐resolution income data, we have developed an innovative machine learning framework, that we applied to produce a European 1 km‐gridded data set of per capita disposable income for 2015. This data set was generated by downscaling income data available for more than 120,000 administrative units. Our learning framework showed high accuracy levels, and performed better or equally than other existing approaches used in the literature for downscaling income. It also yielded better results for the estimation of spatial inequality within administrative units. Using SHAP values, we explored the contribution of the model predictors to income predictions and found that, in addition to geographic predictors, distance to public transport or nighttime light intensity were key drivers of income predictions. More broadly, this data set offers an opportunity to explore the relationships between economic inequality and environmental degradation in health, adaptation or urban planning sectors. It can also facilitate the development of future income maps that align with the Shared Socioeconomic Pathways, and ultimately enable the assessment of future climate risks. |
| format | Article |
| id | doaj-art-24cb1186b3d548b0aa17edd415b926fd |
| institution | Kabale University |
| issn | 2328-4277 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Earth's Future |
| spelling | doaj-art-24cb1186b3d548b0aa17edd415b926fd2025-01-28T15:40:37ZengWileyEarth's Future2328-42772025-01-01131n/an/a10.1029/2024EF004576High‐Resolution Downscaling of Disposable Income in Europe Using Open‐Source DataMehdi Mikou0Améline Vallet1Céline Guivarch2David Makowski3Université Paris‐Saclay AgroParisTech, CNRS, Ecole des Ponts ParisTech, Cirad, EHESS, UMR CIRED Nogent‐sur‐Marne FranceUniversité Paris‐Saclay AgroParisTech, CNRS, Ecole des Ponts ParisTech, Cirad, EHESS, UMR CIRED Nogent‐sur‐Marne FranceUniversité Paris‐Saclay AgroParisTech, CNRS, Ecole des Ponts ParisTech, Cirad, EHESS, UMR CIRED Nogent‐sur‐Marne FranceUniversité Paris‐Saclay AgroParisTech, INRAE, UMR MIA Paris‐Saclay Palaiseau FranceAbstract Income maps have been extensively used for identifying populations vulnerable to global changes. The frequency and intensity of extreme events are likely to increase in coming years as a result of climate change. In this context, several studies have hypothesized that the economic and social impact of extreme events depend on income. However, to rigorously test this hypothesis, fine‐scale spatial income data is needed, compatible with the analysis of extreme climatic events. To produce reliable high‐resolution income data, we have developed an innovative machine learning framework, that we applied to produce a European 1 km‐gridded data set of per capita disposable income for 2015. This data set was generated by downscaling income data available for more than 120,000 administrative units. Our learning framework showed high accuracy levels, and performed better or equally than other existing approaches used in the literature for downscaling income. It also yielded better results for the estimation of spatial inequality within administrative units. Using SHAP values, we explored the contribution of the model predictors to income predictions and found that, in addition to geographic predictors, distance to public transport or nighttime light intensity were key drivers of income predictions. More broadly, this data set offers an opportunity to explore the relationships between economic inequality and environmental degradation in health, adaptation or urban planning sectors. It can also facilitate the development of future income maps that align with the Shared Socioeconomic Pathways, and ultimately enable the assessment of future climate risks.https://doi.org/10.1029/2024EF004576machine learningrandom forestincomeEuropespatial modelingeconomic vulnerability |
| spellingShingle | Mehdi Mikou Améline Vallet Céline Guivarch David Makowski High‐Resolution Downscaling of Disposable Income in Europe Using Open‐Source Data Earth's Future machine learning random forest income Europe spatial modeling economic vulnerability |
| title | High‐Resolution Downscaling of Disposable Income in Europe Using Open‐Source Data |
| title_full | High‐Resolution Downscaling of Disposable Income in Europe Using Open‐Source Data |
| title_fullStr | High‐Resolution Downscaling of Disposable Income in Europe Using Open‐Source Data |
| title_full_unstemmed | High‐Resolution Downscaling of Disposable Income in Europe Using Open‐Source Data |
| title_short | High‐Resolution Downscaling of Disposable Income in Europe Using Open‐Source Data |
| title_sort | high resolution downscaling of disposable income in europe using open source data |
| topic | machine learning random forest income Europe spatial modeling economic vulnerability |
| url | https://doi.org/10.1029/2024EF004576 |
| work_keys_str_mv | AT mehdimikou highresolutiondownscalingofdisposableincomeineuropeusingopensourcedata AT amelinevallet highresolutiondownscalingofdisposableincomeineuropeusingopensourcedata AT celineguivarch highresolutiondownscalingofdisposableincomeineuropeusingopensourcedata AT davidmakowski highresolutiondownscalingofdisposableincomeineuropeusingopensourcedata |