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 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. Map showing the focal states of the FPCA project and the spatial distribution of the volunteers within the focal states during the project’s second phase (FPCA-II) from April to November 2021.. Flyer distributed to invite prospective volunteers to participate in the second wave of food price crowdsourcing in Africa (FPCA-II) project in Nigeria. Diagrammatic representation of the three steps of outlier detection.