GeoMason: Geospatial Support for MASON

MASON (Multi-Agent Simulator Of Neighborhoods) developed jointly at George Mason University's Evolutionary Computation Laboratory and the Center for Social Complexity has recently added an extension called GeoMason which allows spatially explicit models to be incorporated into the MASON.

Over the last few months I have been experimenting with GeoMason in terms of using both raster and vector data to build spatially explicit agent-based models. Overall, I have been impressed by the speed of which such models run at and the example models that come with it GeoMason are really useful.

Below are some of the test models we have developed for a course on entitled GIS and Agent-based modeling which was taught in the Department of Computational Social Science at GMU.

The first model is based on importing a geotiff of the National Elevation Dataset at 1 arc second from the National Map of Crater Lake, Oregon. After which water is added (which could be considered loosely as agents) and flows from high to low elevations, if the water can not flow over the surface it pools. As the movie below shows, over time, Crater Lake slowly fills up until the water breaches the caldera rim which allows the water to flow out. This model was inspired by the NetLogo Grand Canyon Model.

Moving towards vector data, below is a simple example of using GeoMason for a spatially explicit Schelling style of model of segregation where neighborhoods are not based on a regular square lattice but on irregular cells and in this case stylized on Washington DC. The model uses polygon boundaries of census tracks from the 2000 US census. In the model, each census track knows which other census tracts are its neighbors and only one agent occupies such a track. As in the normal Schelling styles of models, agents want to be located in neighborhoods where a certain percentage of their neighbors are of the same type. If an agent is dissatisfied with its neighborhood it moves to a new location. This movement causes more movement and the result is that areas become segregated.

The next movie, highlights a variant of the Schelling model. Whereby we take aggregate data and use this to populate the model with agents. The reason for this is 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 can take this data along with other information and use it as a basis of spatially explicit agent-based model. Basically, this model extends the polygon model above and creates agents based on attribute data held within the shapefile. While the model is highly stylized it demonstrates how data can be read into the model, how to create agents based on this data and how to link points (agents) to polygons along with some basic geographical operations (such as union, point in polygon, buffer).

To download GeoMason click here (note there a number of model examples here). For more information on GeoMason there is a technical report:

Sullivan, K., Coletti, M. and Luke, S. (2010), GeoMason: GeoSpatial Support for MASON, Department of Computer Science, George Mason University, Technical Report Series, Fairfax, VA.

Also when I have time I will provide more details on the models above and others using GeoMason.

Multi-disciplinary approach of complexity, networks, geosimulations

A short note to say that the videos and presentations from the Multi-disciplinary approach of complexity, networks, geosimulations workshop held between 9th - 11th June 2010 at the University of Lausanne are available online and for download: http://www.unil.ch/citadyne/page78227.html. Presentations include those from Prof. Peter Allen entitled "The Complexity of Structure, Strategy and Decision Making" and Prof. Itzhak Benenson entitled "Geo-simulations of urban phenomena"

Space-Time Dynamics in Scaling Systems

To quote from Mike Batty:

"A rank clock is a device for visualising the changes over time in the ranked order of any set of objects where the ordering is usually from large to small. The size of cities, of firms, the distribution of incomes, and such-like social and economic phenomena display highly ordered distributions. If you rank order these phenomena by size from largest to smallest, the objects follow a power law over much of their size range, or at least follow a log normal distribution which is a power law in the upper tail."

The idea behind the Rank Clock is:

"... despite the fact that such distributions are so regular even through time, when one examines how objects within these distributions change over time, it is quite clear that somehow these systems remain stable at the aggregate level but with objects which composes them shifting quite dramatically from time period to time period. The Rank Clock is a device that shows how such distributions change over time and it is a natural complement to the rank size distribution which is called a Zipf Plot."

Below are some movies of the Rank Clock in action (further details can be found here). The first is an animated rank clock showing how the rank of cities in the USA changed between 1790 and 2000.

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Rank clock for US cities from Michael Batty on Vimeo.

While the movie below shows an animation of a rank clock showing how the ranks of the top 100 high buildings in New York change between 1912 and 2008.

Rank clock of the top 100 high buildings in New York from Michael Batty on Vimeo.

The Rank Clock Software can be downloaded from the CASA Web Site (here), more information can also be found here and more animations here

Geospatial Revolution - Episode 1

Via the VerySpatial Blog I came across the first episode (below) of the Geospatial Revolution Project and thought it was worth sharing. To quote from the site:

"The mission of the Geospatial Revolution Project is to expand public knowledge about the history, applications, related privacy and legal issues, and the potential future of location-based technologies"

A prototype migration model

This week saw many members of the Computational Social Science Department and the Center of Social Complexity attend the 3rd World Congress on Social Simulation in Kassel, Germany. Chris Rouly and myself presented some ongoing work entitled "A prototype, multi-agent system for the study of the Peopling of the Western Hemisphere". Below is the abstract of the paper:

"We describe the interim state of development of a prototype, multiagent system (MAS) model for studying the Peopling of the Western Hemisphere. The model is part of a computational analysis of proxy evidence associable with late Pleistocene human migrations. In particular, we examine an out-of-Europe migratory theory some suggest occurred late in the Pleistocene.

The migratory theory we examine is the Bradley-Stanford Solutrean-Clovis Hypothesis [1]. To date, natural decay and terrestrial location has produced only limited circumstantial [3], genomic [2], and lithic [4] evidence supporting conclusions pertaining to this specific theoretic event. The work described here constitutes the foundation steps for a coherent body of computational social science whose intent is a thorough investigation of the several hypothesized routes often suggested as migratory thoroughfares for early hunter-gatherer peoples into the Western Hemisphere. We use a biologically detailed, temporally articulated, spatially accurate, and empirically driven MAS."

While this research is ongoing if you would like to read more, see the paper below:

Rouly, O. V. and Crooks, A. T. (2010), A Prototype, Multi-agent System for the Study of the Peopling of the Western Hemisphere, in Ernst, A. and Kuhn, S. (eds) Proceedings of the 3rd World Congress on Social Simulation (WCSS2010): Scientific Advances in Understanding Societal Processes and Dynamics, Kassel, Germany. (pdf) 

Part of the model is modeling the extent of the ice sheet ("Deep Freeze") component of the model. The movie below shows the growth of the simplified ice sheet used in the simulation during the last ice age (from 25,000 to 16,000 years ago):

In addition to showing the total simulation of the Ice Sheet we also model the annual ice sheet movement (fluctuation), in the sense that while we model the growth and decline of the ice during the last glaciation we also model the ice sheets yearly flux:

The agents in the model are individual hunter-gatherers who move around the spatially explicit environment. The can form cohorts/groups. They forage for food and can migrate over the environment. We try to highlight this in the movie below:




To provide an idea of the simulation environment we are currently developing the movie below shows the GUI of the model focusing on a simulated year where our hunter-gatherers forage for food:

As noted at the beginning of this post, this is some initial research, a foray if you like and the model is classed as a prototype. It is not the final model by any stretch of the imagination. Any thoughts or comments are most welcome.

References

1. Bradley, B., Stanford, D.: The North Atlantic Ice-edge Corridor: A Possible Paleolithic Route to the New World. World Archaeology. 36 (2004) 459–478
2. Fagundes, N. J. R., Kanitz, R., Eckert, R., Valls, A. C. S., Bogo, M. R., Salzano, F. M., Smith, D. G., Silva Jr., W. A., Zago, M. A., Ribeiro-dos-Santos, A. K., Santos, S. E. B., Petzl-Erler, M. L., Bonatto, S. L.: Mitochondrial Population Genomics Supports a Single Pre-Clovis Origin with a Coastal Route for the Peopling of the Americas. The American Journal of Human Genetics. 82 (2008) 583–592
3. Goebel, T., Waters, T., O’Rourke, D.: The Late Pleistocene Dispersal of Modern Humans in the Americas. Science. 319 (2008) 1497–1502
4. Lowery, O., O’Neal, M., Wah, J., Wagner, D., Stanford, D.: Late Pleistocene Upland
Stratigraphy of the Western Delmarva Peninsula, USA. Quaternary Science Reviews. 29 (2010) 1472–1480