# **Methods**

This paper leveraged previously published food price datasets, available in different public repositories (Andrée, 2021a; Solano-Hermosilla, et al., 2021; Arbia, et al., 2023; Andrée & Pape, 2023), which were coupled with unpublished metadata (mainly ID of data submitter – volunteer crowd or trained enumerator) and wrangled to establish linkages across data sources and generate new evidence regarding the validity of the high frequency data outputs from the newer methods (i.e. crowdsourcing and AI-based imputation).

Briefly, the FPCA crowdsourced price data were submitted by ~1,200 volunteer citizens (so called “crowd”) who were onboarded and encouraged to submit prices of six (6) major commodities whenever they leisurely or purposefully visit nearest market within or beyond their locality (Adewopo, et al., 2022). The crowdsourced data were spatially rich because volunteers submitted georeferenced datapoints from many (>100) market locations and across nine (9) market types which all fall within three major market segment classification – Farmgate (nearest to production locations), Retail, and wholesale (Arbia, et al., 2023; SolanoHermosilla, et al., 2022). We accessed the monthly AI-estimated prices with the version date of 09/26/2023 from the World Bank microdata library. The AI-estimated data were generated by imputation of sparse, incomplete, or infrequent price data gathered from multiple sources and provided by Humanitarian Data Exchange (HDX) in partnership with the World Food Programme (WFP), through hybrid machine learning models that integrate local relationships within specific regions of the feature space, similar to the concept of local receptive fields in CNN, to support investigation of local price dynamics in markets where prices are sensitive to localized shocks and traditional data are not available (Andrée, 2021a).

We computed basic statistical metrics and implemented dual-layered correlative analyses. The first layer focused on the relationship between daily crowdsourced prices and ground truth prices, and this cascaded to second layer assessment of relationship between monthly averages of crowdsourced prices and monthly AI-estimates. Although the AI-estimates include open, high, low, and close (OHLC) prices, we opted to utilize closing prices because they often represent the prevailing market sentiment within each period.

The raw intraday crowdsourced dataset (level 0 data) was downloaded and queried for the coinciding commodities between actual data submissions and AI-estimates (rice and maize). Both commodities are major staples that are commonly traded in the region, and nationally. The ground truth data collection was implemented in parallel to the submission of data by crowd volunteers during the final 8-months of FPCA initiative (EC-JRC, 2022). Twelve (12) enumerators who are resident at different locations, dispersed across the focal region (Figure 1), were duly trained with a data collection protocol. By visiting the nearest market daily, the enumerators submitted observed or transactional data through a preconfigured data collection survey instrument (on smartphone). Multiple enumerators were enlisted to improve coverage of multiple market locations, and minimize potential human bias and artefacts in the data. It should be noted that the different unit packages for the prices that were submitted by volunteer crowd and enumerators reflect the market reality that commodities are usually transacted in diverse unit packages, which are standardized across various market segments and geographies. The price per unit (₦/kg) for each submitted datapoints were eventually computed with reference to standard conversion units (Arbia, et al., 2023).

Prior to implementing the two-step analysis, the raw intraday crowdsourced and enumerator-submitted prices were checked for obvious outliers, based on expert 14