Agent-based model of residential location

Multi-agent system (MAS) is a powerful tool capable of analyzing and simulating complex systems, and has been extensively applied in the regime of social sciences. In this paper we present an agent-based model of residential location (ABMRL) and apply this model to study the dynamic changes of residential location and land price, aiming to explore and simulate the complicated spatial decision-making behaviors in residential location as well as the evolution process of urban residential segregation, which is resulted from interactions among residents and between residents and geographical environment. The ABMRL model consists of agent layer representing various classes of residents and cell automata layer representing geographical environment, which correspond to the two basic elements in man-earth relationship, i.e., human being and natural environment. In this model, psychological concepts were introduced for study of the behaviors of residential location, as it is generally considered that residential relocation is facilitated by internal social and economic pressure and external residential environment. ABMRL model was used to simulate and validate a series of classic urban theories such as residential segregation, concentric urban space structure, gentrification, etc., and to simulate the evolution of residential segregation and dynamic changes in land price in Haizhu District of Guangzhou City, which was taken as a test example for the study.