Despite being the main source of fresh, convenient, and affordable food for 80% of Hanoi���s population, food flows within traditional markets remain largely invisible due to a lack of tracing systems and environmental conditions which make traditional tracking approaches challenging. By providing free internet to a series of wholesalers and markets in the Cau Giay and Dong Anh districts of Hanoi, Vietnam, this project will put in place the first pieces of tracking system that will characterize and monitor food flows between traders, retailers, and consumers. Research has found that 10-40% of traditional market food is contaminated with microbes or parasites which cause foodborne illnesses. As shoppers become increasingly concerned about food safety and large-scale retailers that can offer food safety certification expand rapidly, this project aims to equip traditional market actors with data that could prevent their marginalization through urban policy decisions that may favor organized retailers, as well as improve the safety of traditional market goods. The collected food flow data will allow for improved linkages among key traditional market actors and help identify better policy and planning options for improving distribution channels in ways that benefits under-resourced communities. To implement the project, the Alliance of Bioversity International and CIAT and the General Statistics Office (GSO) of Vietnam survey actors and track space and time data points on all devices within the range of the WiFi routers and signal amplifiers, whether connected to the internet or not. The pilot system ran on three layers of data: Layer One Every smartphone has a unique media access control (MAC) address that the WiFi routers installed in the markers use to identify how many MAC addresses visit the markets over time, how many return to the market and how often, and how markets differ on these metrics. This data is collected even if the smartphone is not connected to the WiFi network. Layer Two When a smartphone user connects to the free WiFi, they are prompted to answer a series of questions depending on their user type (vendor, customer, etc.). For example, a user that identifies as a vendor is asked questions regarding sales of specific commodities which will allow for sales to be characterized across time and space. Layer Three To validate findings in Layer One and Two, in-person surveys were conducted with vegetable, pork and rice sellers in five traditional markets in Hanoi mac: An anonymized version of the MAC. All the MAC address were anonymized through a SHA-3 256 hashing function. The hashed mac ensure anonymity while is consistent across all markets and during the whole period of the analysis. We can therefore ensure that a given mac found in two different dataset will correspond to the same phone. market: The name of the market where the phone was seen role: Self-identified role if the user connected to the wifi and filled-out the layer 2 form gender: Self-identified role if the user connected to the wifi and filled-out the layer 2 form median_first_seen: The median time when the user is first seen in the markets (in minutes starting at 0 from midnight) (e.g. the time the user entered the market) median_last_seen: The median time when the user is last seen in the markets (in minutes starting at 0 from midnight) (e.g. the time the user left the market) average_time_day: The average number of time the user visited the market. A period of time of at least 2 hours between two consecutive observation of the user in the market is needed to be counted as a different visit. average_duration_day: The average duration spent on the market daily. average_day_week: The average number of visits per week. average_total_day_seen: The total number of days a user was seen on the market. total_durantion: Total duration spent by a single user on the market.

https://bigdata.cgiar.org/inspire/inspire-challenge-2018/revealing-informal-food-flows-through-free-wifi/#news-resources