Predicting poverty with vegetation index
Accurate and timely predictions of the poverty status of communities in developing countries are critical to policymakers. Previous work has applied convolutional neural networks (CNNs) to high‐resolution satellite imagery to perform community‐level poverty prediction. Although promising, such image...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Wiley
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/141232 |
| _version_ | 1855526915519021056 |
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| author | Tang, Binh Liu, Yanyan Matteson, David S. |
| author_browse | Liu, Yanyan Matteson, David S. Tang, Binh |
| author_facet | Tang, Binh Liu, Yanyan Matteson, David S. |
| author_sort | Tang, Binh |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Accurate and timely predictions of the poverty status of communities in developing countries are critical to policymakers. Previous work has applied convolutional neural networks (CNNs) to high‐resolution satellite imagery to perform community‐level poverty prediction. Although promising, such imagery has limitations in predicting poverty among poor communities. We provide the first evidence that a publicly available, moderate‐resolution vegetation index (the normalized difference vegetation index [NDVI]), can be used with CNNs to produce accurate poverty predictions contemporaneously among poor communities heavily dependent on agriculture. We also show that the NDVI can effectively detect consumption variation over time. To our knowledge, this is the first attempt to use remote sensing data to predict future‐period consumption expenditure at the community level. |
| format | Journal Article |
| id | CGSpace141232 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace1412322025-12-08T10:11:39Z Predicting poverty with vegetation index Tang, Binh Liu, Yanyan Matteson, David S. news machine learning poverty Accurate and timely predictions of the poverty status of communities in developing countries are critical to policymakers. Previous work has applied convolutional neural networks (CNNs) to high‐resolution satellite imagery to perform community‐level poverty prediction. Although promising, such imagery has limitations in predicting poverty among poor communities. We provide the first evidence that a publicly available, moderate‐resolution vegetation index (the normalized difference vegetation index [NDVI]), can be used with CNNs to produce accurate poverty predictions contemporaneously among poor communities heavily dependent on agriculture. We also show that the NDVI can effectively detect consumption variation over time. To our knowledge, this is the first attempt to use remote sensing data to predict future‐period consumption expenditure at the community level. 2022-06 2024-04-12T13:37:30Z 2024-04-12T13:37:30Z Journal Article https://hdl.handle.net/10568/141232 en https://doi.org/10.1002/aepp.13175 https://doi.org/10.1371/journal.pone.0255519 Limited Access Wiley Tang, Binh; Liu, Yanyan; and Matteson, David S. 2022. Predicting poverty with vegetation index. Applied Economic Perspectives and Policy 44(2): 930-945. https://doi.org/10.1002/aepp.13221 |
| spellingShingle | news machine learning poverty Tang, Binh Liu, Yanyan Matteson, David S. Predicting poverty with vegetation index |
| title | Predicting poverty with vegetation index |
| title_full | Predicting poverty with vegetation index |
| title_fullStr | Predicting poverty with vegetation index |
| title_full_unstemmed | Predicting poverty with vegetation index |
| title_short | Predicting poverty with vegetation index |
| title_sort | predicting poverty with vegetation index |
| topic | news machine learning poverty |
| url | https://hdl.handle.net/10568/141232 |
| work_keys_str_mv | AT tangbinh predictingpovertywithvegetationindex AT liuyanyan predictingpovertywithvegetationindex AT mattesondavids predictingpovertywithvegetationindex |