About Me

I integrate:

  • complex systems theory
  • geography
  • geographic information science (GIScience)
  • network science
  • data science
  • and computer science

to model, analyze, and better understand a variety of systems. Currently I am focused on modelling urban systems.

Most real-world systems can be studied using a complex systems approach. Using this perspective, we can visualize the system from the bottom-up, starting with the set of components that make up the system (individual economic transactions, animal species, people) that interact with each other and with their environment over time. The behavior of the system as a whole (the market, the ecosystem, and social systems) is said to emerge from these interactions.

Insect infestation as a complex system. Large-scale patterns of spread emerge from interactions between insects and their host trees over space and time.

Because the components that make up the system are heterogeneous, adaptive, and sometimes behave unexpectedly, complex systems exhibit non-linear behavior. Office politics may cause a sudden shift in company organization, changes in supply and demand may cause a sudden change in the market, and adaption and evolution in ecological systems may generate new patterns of species distributions.

Because of their complexity, these types of systems are hard to understand and predict. However, a specific group of computer models (called complex systems models) can help with this because they are able to capture the system’s inherent complexity. Common complex systems models include cellular automata and agent-based models.

A classic cellular automata model called the Game of Life, developed by John Conway generates complex, fractal-like patterns from a set of very simple rules. You can learn more about the Game of Life here. The Game of Life is theoretical, meaning it doesn’t represent a real phenomenon. However, cellular automata have been used to simulate real-world phenomena in the same way such as landscape dynamics, pedestrian movements, tumor-immune system interactions, forest fires and many more. In these models, simple rules, meant to represent local dynamics between system components, are used to generate emergent behavior, characteristic of the system as a whole.

Agent-based models or multi-agent systems are models that explicitly represent the individual components of the system, their decision-making, behavior, and interactions. This is a little like the classic EA computer game “The Sims”, where different people interact with each other and their environment over time. Agent-based models are also applied to represent a variety of real systems such as insect infestation, economic systems, and disease spread. A classic agent-based model is the Boids model, developed by Craig Reynolds. He uses a set of simple rules to simulate the flocking behavior of birds.


As a geographer and a GIScientist, I am primarily interested in how geographic space plays a role in the emergence of complex systems. Complex systems models such as cellular automata and agent-based models that explicitly represent geographic space are referred to as Geographic Automata Systems (GAS).

I apply GAS approaches to represent and understand complex geospatial phenomena including the spread of forest insect infestation across the landscape, the spread of infectious disease through the human population, and spatial patterns of individuals in an urban context. These models are useful because they provide a useful, realistic, and flexible medium to forecast and evaluate the outcome of policy and decision making processes employed to overcome societal or environmental challenges.

More recently, I focus on developing novel GAS modeling approaches by integrating complex systems theory, GIScience, and network theory. A wide array of complex systems such as ecological, social, and urban systems can be represented as spatial networks. The “boiling down” of these complex systems

to networks invites an additional lens and toolkit for analysis. Specifically, network analysis can be applied to mathematically characterize the spatial structure of these systems which can be used to better understand system dynamics, as a way to compare between systems, or to compare between the same system over time.

For more information, check out my projects!


My pedagogical approach is to provide students with valuable hands-on training with cutting-edge GIS technologies while also grounding their technical skills with the theoretical foundation of GIScience that will challenge them to consider the implications of their analyses. I am committed to creating a welcome, supportive, and inclusive environment and actively transforming oppressive university structures.


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