A review of the use of geosocial media data in agent-based models for studying urban systems

Since the rapid growth of urban populations, the study of urban systems has gained considerable attention from researchers, decision makers, governments, and organizations. Urban systems are complex and dynamic such that they produce emergent patterns such as self-organization and nonlinearity. Agent-based modelling presents an approach to simulating and abstracting urban systems to reveal and study emergent patterns from urban-related entities. However, agent-based models are difficult to effectively optimize and validate without high quality real-world data. Geosocial media data provides agent-based models with location-enabled data at high volumes and frequencies. Integrating agent-based models with geosocial media data presents opportunities in advancing and developing studies in urban systems. This paper provides a general overview of concepts, review of recent applications, and discussion of challenges and opportunities in the context of using geosocial media data in agent-based models for urban systems. We argue that ABMs focused on studying urban systems can benefit greatly from geosocial media data, given that research moves towards standard guidelines that enable the comparison and effective use of ABMs, and geosocial media data under appropriate circumstances and applications.