This project is focused on development of a hybrid automata model for testing ideas and hypotheses relating to urban gentrification dynamics. We focus on the agency of relocating households in dynamic property markets as the theoretical basis for construction of the model. The methodology employed makes use of hybridized cellular- and agent-automata that allow for representation of co-interaction among fixed and mobile entities in urban settings across multiple scales. Simulations run with the model are based on various hypotheses from gentrification theory and these hypotheses are tested in simulation by running the model through theory-informed scenarios. The usefulness of this scheme is demonstrated through application of the model to a historically under-invested area of Salt Lake City in Utah that is undergoing recent transformation. Our results show that the hybrid approach is useful in representing human behavior in complex adaptive urban systems. Moreover, our model proves to be a useful test-bed for studying gentrification.

The nuts-and-bolts of the choice model that drives gentrification dynamics

The study area, in downtown Salt Lake City's gateway district

Sample graphic from the model, showing the evolution of the area's demographic and market dynamics

Sample output from the model: social, economic, and demographic profiles for the simulated area under varying scenarios

Dynamic physics for built infrastructure

Moving agents through space and time

Modeling riots

Validating agent-based models

Machine-learning behavioral geography

Accelerating agent-based models

Megacity futures

Immersive modeling

Space-time GIS and analysis

A toolkit for measuring sprawl

Modeling time, space, and behavior

Simulating crowd behavior