From mobile crowdsourcing to crowd-trusted food price in Nigeria: statistical pre-processing and post-sampling
Timely and reliable monitoring of food market prices at high spatial and temporal resolution is essential to understanding market and food security developments and supporting timely policy and decision-making. Mostly, decisions rely on price expectations, which are updated with new information rele...
| Autores principales: | , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/159967 |
| _version_ | 1855518479389556736 |
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| author | Arbia, G. Solano-Hermosilla, G. Nardelli, V. Micale, F. Genovese, G. Amerise, I.L. Adewopo, J. |
| author_browse | Adewopo, J. Amerise, I.L. Arbia, G. Genovese, G. Micale, F. Nardelli, V. Solano-Hermosilla, G. |
| author_facet | Arbia, G. Solano-Hermosilla, G. Nardelli, V. Micale, F. Genovese, G. Amerise, I.L. Adewopo, J. |
| author_sort | Arbia, G. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Timely and reliable monitoring of food market prices at high spatial and temporal resolution is essential to understanding market and food security developments and supporting timely policy and decision-making. Mostly, decisions rely on price expectations, which are updated with new information releases. Therefore, increasing the availability and timeliness of price information has become a national and international priority. We present two new datasets in which mobile app-based crowdsourced daily price observations, voluntarily submitted by self-selected participants, are validated in real-time within spatio-temporal markets (pre-processed data). Then, they are reweighted weekly using their geo-location to resemble a formal sample design and allow for more reliable statistical inference (post-sampled data). Using real-time data collected in Nigeria, we assess the accuracy and propose that our reweighted estimates are more accurate with respect to the unweighted version. Results have important implications for governments, food chain actors, researchers and other organisations. |
| format | Journal Article |
| id | CGSpace159967 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| record_format | dspace |
| spelling | CGSpace1599672025-12-08T10:11:39Z From mobile crowdsourcing to crowd-trusted food price in Nigeria: statistical pre-processing and post-sampling Arbia, G. Solano-Hermosilla, G. Nardelli, V. Micale, F. Genovese, G. Amerise, I.L. Adewopo, J. markets food prices data processing value chains Timely and reliable monitoring of food market prices at high spatial and temporal resolution is essential to understanding market and food security developments and supporting timely policy and decision-making. Mostly, decisions rely on price expectations, which are updated with new information releases. Therefore, increasing the availability and timeliness of price information has become a national and international priority. We present two new datasets in which mobile app-based crowdsourced daily price observations, voluntarily submitted by self-selected participants, are validated in real-time within spatio-temporal markets (pre-processed data). Then, they are reweighted weekly using their geo-location to resemble a formal sample design and allow for more reliable statistical inference (post-sampled data). Using real-time data collected in Nigeria, we assess the accuracy and propose that our reweighted estimates are more accurate with respect to the unweighted version. Results have important implications for governments, food chain actors, researchers and other organisations. 2023 2024-11-20T11:21:24Z 2024-11-20T11:21:24Z Journal Article https://hdl.handle.net/10568/159967 en Open Access application/pdf Arbia, G., Solano-Hermosilla, G., Nardelli, V., Micale, F., Genovese, G., Amerise, I.L. & Adewopo, J. (2023). From mobile crowdsourcing to crowd-trusted food price in Nigeria: statistical pre-processing and post-sampling. Scientific Data, 10(1): 446, 1-12. |
| spellingShingle | markets food prices data processing value chains Arbia, G. Solano-Hermosilla, G. Nardelli, V. Micale, F. Genovese, G. Amerise, I.L. Adewopo, J. From mobile crowdsourcing to crowd-trusted food price in Nigeria: statistical pre-processing and post-sampling |
| title | From mobile crowdsourcing to crowd-trusted food price in Nigeria: statistical pre-processing and post-sampling |
| title_full | From mobile crowdsourcing to crowd-trusted food price in Nigeria: statistical pre-processing and post-sampling |
| title_fullStr | From mobile crowdsourcing to crowd-trusted food price in Nigeria: statistical pre-processing and post-sampling |
| title_full_unstemmed | From mobile crowdsourcing to crowd-trusted food price in Nigeria: statistical pre-processing and post-sampling |
| title_short | From mobile crowdsourcing to crowd-trusted food price in Nigeria: statistical pre-processing and post-sampling |
| title_sort | from mobile crowdsourcing to crowd trusted food price in nigeria statistical pre processing and post sampling |
| topic | markets food prices data processing value chains |
| url | https://hdl.handle.net/10568/159967 |
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