(546f) Urban Systems and Sustainability: Comparative Analysis of Multiple Metropolitan Statistical Areas in Ohio, USA
Urban systems have a number of factors (e.g., economic, social and environmental) that can potentially impact their growth, change and transition. As such, assessing and managing these systems is a complex challenge. While, tracking trends of key variables may provide some insight, identifying the critical characteristics that truly impact the dynamic behavior of these systems is difficult. As an integrated approach to evaluate real urban systems, this work contributes to the research on scientific techniques for assessing sustainability. Specifically, it proposes a practical methodology based on the estimation of dynamic order, for identifying stable and unstable periods of sustainable or unsustainable trends using Fisher Information (FI). In this work, the dynamic behavior of main six Metropolitan Statistical Areas (MSAs) in Ohio was evaluated by using 29 social and 11 economic variables to characterize each system from 1970 to 2009. For a complete regional sustainability analysis, these Ohio MSAs were compared to each other according their stability and population size over the period of study, to assess the impact of social and economic characteristics on system stability at geographic scales, which are critical to policy and management. Results from this multi-year assessment indicated that the Columbus MSA is the most stable of the MSAs understudy, followed by the Cincinnati MSA. Further, while there were unique trends in each MSA, changes in family structure and income appeared to correspond with the patterns experienced, particularly near the end of the study period (2000 – 2009). While the approach presented is not a cause and effect analysis, it points out characteristic features of complex systems that are otherwise very difficult to see by inspecting the individual variables, which characterize the system over time. Further, this approach intends to aid in locating significant deviations in the system’s trajectory and determine whether these shifts corresponds to a new path characterized by sustainable (e.g., desirable growth rate) or unsustainable trends (e.g., decrease in the income growth rate). This work contributes holistically to the research on evaluating complex urban systems and affords the ability to identify and distinguish local and regional drivers of change. The methodology described can be used by scientists, planners and policy makers interested in evaluating dynamic changes in complex systems and identifying potential drivers at multiple spatial and temporal scales.