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Mark,

Congrats.  Things like this should count a serious publications.

//
Bill



On 10/27/11 4:36 PM, Mark Coletti wrote:
> GeoMason version 1.1 has been released.  It includes minor changes to 
> compensate for the release of MASON 16 as well as adding a new 
> |ArcInfoASCGridImporter.ingest()| that can load data sets as Java 
> resources.  Moreover, six new GeoMason demos have been added, which 
> are described below:
>
>
>       Sillypeds
>
> This model demonstrates how one can use GeoMason to explore 
> evacuations from a building. The simulation starts by reading raster 
> data describing a building layout (converted from CAD files). The 
> simulation randomly places a number of agents on walkable areas within 
> side of the building. Once the agents have been placed on the ground, 
> they follow the lowest cost path to the exit (in this example there is 
> only one).
>
>
>       Water World
>
> Inspired by NetLogo's Grand Canyon Model 
> <http://ccl.northwestern.edu/netlogo/models/GrandCanyon>. The aim of 
> the model is to show how data in the form of a elevation, can be used 
> as a foundation of a simple spatial agent-based model. Similar to the 
> Netlogo model, the elevation data comes from the National Elevation 
> Dataset <http://seamless.usgs.gov/>. It was converted from an ESRI 
> Grid into an ASCII grid file using ArcGIS.
>
> Similar to Sillypeds, the elevation data acts as our terrain, in this 
> case its Crater Lake in Oregon. Agents within the model (in this case 
> water) fall at random over the terrain and then flows downhill over 
> the terrain. If the water cannot flow downhill, it pools up and once 
> the gradient is sufficient, the water flows.
>
>
>       GridLock
>
> This basic traffic model explores how agents travel to Tyson's Corner, 
> Virginia for work. The idea is that if you increased the number of 
> agents (people) more congestion will arise. To some extent this is 
> similar to the GeoMason |sim.app.geo.campusworld| example.The model 
> demonstrates how you can make agents move along networks (in this case 
> road lines in the form of ESRI shapefiles) from their origin to their 
> destination via a shortest path algorithm.
>
> The number of agents is based census tract information 
> <http://www.census.gov/mp/www/spectab/stp64.txt> i.e. the number of 
> people who work in Tyson's Corner and their corresponding home 
> locations which is restricted to Washington DC, Virginia and Maryland.
>
>
>       Schelling Polygon
>
> In this model we demonstrate how one can use polygons (such as census 
> tracks) to create an abstract Schelling model stylized on Washington 
> DC. The model reads in a ESRI Polygon shapefile and uses attributes of 
> the shapefile to create Red and Blue agents and a number of Unoccupied 
> areas. As with the traditional Schelling model, Red and Blue 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 the neighbors that share a common 
> edge to the agent in question. If an agent is dissatisfied with its 
> current neighborhood, it will move to a random Unoccupied polygon, 
> regardless of whether or not this new location meets its preference.
>
>
>       Point Schelling Model
>
> 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 
> <http://www.cs.gmu.edu/%7Eeclab/projects/mason/extensions/geomason/IJGIS_Crooks_10.pdf>). 
> 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).
>
>
>       SLEUTH: Urban Growth Model
>
> This model shows a basic urban growth model based loosely on the 
> SLEUTH model <http://www.ncgia.ucsb.edu/projects/gig/>. In the sense, 
> that we have only implemented the four growth rules (spontaneous, new 
> spreading centers, edge and road-influenced growth) and not the self 
> modification element 
> <http://www.ncgia.ucsb.edu/projects/gig/About/abGrowth.htm> of the 
> SLUETH model. The model demonstrates how different layers (e.g. slope, 
> land use, exclusion, urban extent - urbanized or non-urbanized, 
> transportation, hillshade) can be read into a model to provide cells 
> with multiple values. This simulation shows a specific growth scenario 
> under specific coefficients (parameters) for Santa Fe, New Mexico.
>
>
> Also, the GeoMason web site has a new look.  You can see it at: 
> http://www.cs.gmu.edu/~eclab/projects/mason/extensions/geomason/ 
> <http://www.cs.gmu.edu/%7Eeclab/projects/mason/extensions/geomason/>
>