Abstract for: Urban Dynamics Modeling Methods for Regional Health Equity

How do we understand urban dynamics to effectively inform community and population health? Improving urban health outcomes in the United States is closely tied to understanding and applying knowledge to regional health equity and trends. This paper addresses this gap by proposing the use of disaggregated geospatial or community unit level data in addition to novel person-oriented analyses with community participation as a central to developing insights that translate into policy. We combine two different and easily accessible methods and illustrate their use across two urban contexts. First, we show how historical effects of “red lining” in Cleveland can be readily included into Stella interfaces by focusing on simulating a geographic community using extant data. Second, we show how configural frequency analysis (CFA) can yield novel insights into clustering of longitudinal patterns of municipal trends in St. Louis County. We draw on two different projects to illustrate our approach. The first focuses on work developed for Cleveland, Ohio across a variety of projects (including projects that extend beyond Cleveland in terms of methods). The second focuses on characterizing trends using CFA from an earlier study (pre-Ferguson) highlighting different clusters of longitudinal patterns. Reframing urban dynamics, and specifically geospatial inequities through the lens of system dynamics is not only feasible but highlights a critically important gap where disaggregated dynamics play a critical role to understanding regional health inequities.