Magnifying Drivers of City Transformation through System Dynamics Models

Abstract

Population dynamics and its associated impacts in the cities derive from the synergistic effects of primary and secondary factors. These factors work together to effect the transformation of cities through population increase. In the past, these factors have been studied discretely. With the emergence of system dynamic models and their abilities to capture and magnify relationships between variables, it has been demonstrated that secondary factors like income, which positively influence urban migration (as a primary factor), have direct effect on the attractiveness of a city. This is linked to population growth, which ultimately transforms the outlook of the city. This finding has brought forth a consideration that continuous transformation of cities is due to a number of interrelated factors that work together to identify city characteristics. These in most cases are expressed through various forms such as urban congestion, residential demand, city decay, environmental degradation or high levels of crime. This study has shown that relationships between factors that bring city transformation through population growth are, in fact, indirect and cryptic. Through system dynamic models and their abilities to magnify and express relationships between variables using causal loops and feed-back diagrams these cryptic relationships have been clearly magnified.