Paper | Nature - Scientific Data (2023)

From mobile crowdsourcing to crowd-trusted food price in Nigeria

Giuseppe Arbia, Gloria Solano-Hermosilla, Vincenzo Nardelli, Fabio Micale, Giampiero Genovese, Ilaria Lucrezia Amerise, Julius Adewopo

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Abstract

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 …

Data collection Crowdsoucing Spatial