Schelling Space

Contact: Andrew Crooks, Sarah Wise, or Mark Coletti

This model in a sense extends the Schelling Polygon model, however, instead of the polygon being the agent we take attribute data from the polygon model and create individual agents (see Crooks, 2010). This is based on the notion that much of the data we have comes at an aggregate level and often in some sort of vector representation of space such as census data. However, if we want to model the individuals or groups of individuals, we need to disaggregate the data.

To do this we create a number of Red and Blue agents based on population counts held within the polygon shapefile. As with the previous model, all agents want to be located in neighborhoods were a certain percentage of their neighbors are of the same type. However, instead of using a Moore or Von Neumann which is common practice in cell based models. Here neighborhoods are calculated using buffer distance from the agent in question. If an agent is dissatisfied with its current neighborhood, it will move to a random location, regardless of whether or not this new location meets its preference. Moreover, the model demonstrates how to link points (agents) to polygons along with some other basic geographical operations (such as union, point in polygon, buffer).