Abstract
Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach.
Highlights
By 2050 the United Nations (UN) predicts that around 66% of the world’s population will be living in urban areas [1]
Planners have traditionally used aggregate models such as spatial interaction models (e.g., [2]) for formulating policies and plans for the design and growth of cities. These have several drawbacks due to their aggregate treatment of individuals and their lack of dynamics and behavioral realism [3,4]. This has led to an increased interest in using individual-based approaches from Geocomputation such as cellular automata [5] and agent-based modeling [6] for improving our understanding of the processes and dynamics within cities, and in particular simulating how cities may grow in the future
We need an abundance of data to allow calibrate and validate potentially thousands of heterogeneous agents operating distinctly individual rule sets. It is clear through the review undertaken that, evaluation is attempted to some extent in most models, there are no published guidelines or standard approaches that researchers can draw on to evaluate an agent-based modeling (ABM)
Summary
By 2050 the United Nations (UN) predicts that around 66% of the world’s population will be living in urban areas [1]. Planners have traditionally used aggregate models such as spatial interaction models (e.g., [2]) for formulating policies and plans for the design and growth of cities These have several drawbacks due to their aggregate treatment of individuals and their lack of dynamics and behavioral realism [3,4]. Within the geographical modeling community, many urban problems—such as traffic [13] and urban growth [14]—were tackled from the CA perspective Under this paradigm, the world is represented as a series of cells that possess individual “states”. This model explored the evolution of settlements and laid the foundations to more complex urban growth models [18] These early models suffered from a lack of detailed data and computational power to simulate more than a few thousand agents operating complex rules. ConcludesEntities the paper with a discussion of all the main questions posed
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