Culture of Data 2025

The fight against food insecurity is led by diverse distribution agencies, often with limited
coordination and intentional design across providers. Data science can be used to guide
decisions and make the system more effective at tackling inequities. In New Jersey a
cross-sector partnership has employed an innovative methodology powered by data to address
food insecurity. Trenton Health Team (THT), in partnership with local stakeholders, constructed
a food insecurity index to highlight the intensity of food need across the county. Using publicly
available data, geospatial methodologies, and cluster analysis, the Index assigned a relative
food insecurity score to each census block group. Stakeholder feedback was centered
throughout the process, resulting in an interactive map which transformed fifteen variables into
an accessible, visual tool.
The Food Insecurity Index has been utilized for both short and long term change. THT
worked with the regional food bank to deploy mobile pantries to areas with higher need but
limited resources, and plans are being finalized for a new brick and mortar food hub located in
the center of the most under-resourced neighborhood. Emphasizing the project as a
collaborative process not only allowed THT to incorporate valuable local insight in the tool, but
also established trust and increased buy-in from community partners. This framework of
participatory data-based decision making to build consensus will continue to be used by THT as
the Index expands to cover the entire state of New Jersey, and is applicable to an array of
projects across public health and nationwide.
Attendees of this presentation will learn how to apply the Index methodology to their
local geography, as its backbone of census data allows it to be utilized anywhere in the USA.
Attendees will also learn how they can translate data projects into actions via participatory
processes with diverse stakeholders.