Abstract for: Standardizing Data for Evidence-Based Food Systems

Many policy makers and community leaders value quantitative data for making informed decisions; however, most publicly available data are often organized in a manner that is not conducive to system dynamics. Data dashboards tend to emphasize geospatial visualizations of health disparities at a particular point in time or contrasting differences, but typically do not make longitudinal and timeseries data productive to defining a reference mode. Furthermore, synergizing of multiple information sources with varying levels of measurement and spatial and temporal scales of aggregation for evidence-based modeling is time consuming and may introduce distortions when modeled as a ratio variable in system dynamics. A few papers have posited the need for symbiotic communication between traditional statistical approaches to population health research and system dynamics modeling to understand the non-linear, interdependent complex systems that produce poor health outcomes, although a set of principles has not yet been developed. In an interdisciplinary data landscape, data need to be presented for exploration to support reference mode generation, setting initial conditions, and determining parameters and their dimensions. In this project, we are creating an interdisciplinary guide for data acquisition to support transparent and replicable modeling of food system dynamics.