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Introducing the Facility List Coder: A New Dataset/Method to Evaluate Community Food Environments

Equity Focus

The study applied the Facility List Coder (FLC), a new tool for assessing the food environment in communities.  The result is a list of facilities in a given location that allows for an assessment of key research questions on the impact of a community's food environment on various health outcomes

Case Summary

The Facility List Coder (FLC) is a new tool to asses community food environments that was validated and presented. It was developed in Python 3.7 combining GIS analysis with standard data techniques. It offers a low-cost, scalable, efficient, and user-friendly way to indirectly identify community nutritional environments in any context. The FLC uses the most open access information to identify the facilities (e.g., convenience food store, bar, bakery, etc.) present around a location of interest (e.g., school, hospital, or university). As a result, researchers will have a comprehensive list of facilities around any location of interest allowing the assessment of key research questions on the influence of the community food environment on different health outcomes (e.g., obesity, physical inactivity, or diet quality). The FLC can be used either as a main source of information or to complement traditional methods such as store census and official commercial lists, among others.

Lessons Learned

• Once a specific place is identified within a keyword for a pre-established category, the FLC estimates different indicators of relative distance to the LI. In particular, the FLC provides information on: (i) geographic distance (in kilometers) considering the road network using both Google API and OSM; (ii) the average time walking distance (in minutes), taking into account traffic density using Google API; and (iii) the average time cycling distance (in minutes), based on the traffic as well as road structure. As a main output, the FLC offers a detailed dataset for all the classified facilities located around each interest point.

LINK TO CASE STUDY