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Agent Based Model running on the CityScope Volpe table using the GAMA Platform.
| File Type | Type |
|---|---|
| Buildings.shp | Polygon |
| Roads.shp | Line |
| table_bounds.shp | Polygon |
| Amenities.shp | Point |
| Bounds.shp | Polygon |
| Attribute Name | Values |
|---|---|
| Usage | ‘O’: office, ‘R’: Residential |
| Scale | 'S': small, 'M': Medium, 'L': large |
| Category | 'Uni': university, 'Shopping': commercial area, 'Restaurant': dining area, 'Night': nightlife, 'Park': communal greenspace, 'HS': high school, 'Cultural': areas of cultural significance |
| Floors | From 0 to 100 |
Empty attribute table.
This layer represents the bounds of the CityScope physical table grid inside of the simulation.
Point representing lunch/dinner break options for each agent in the simulation. Each point will represent the location of a restaurant, convenience store, or supermarket for example - anywhere someone can sit down and eat.
| Attribute Name | Description |
|---|---|
| Type | The type of amenity this is (e.g. ‘restaurant’, ‘pub’, ‘fast food’) |
| Name | The name of the amenity (e.g. ‘Saul’s Deli’, ‘In-N-Out Burger’) |
| Usage | Each object should have this set to 'A' |
| Scale | TBD |
Represents the bounds of the simulation. Agents will not be able to exit this perimeter. Empty attribute table.
Here is some litterature about the Agent Based Modelling approach.
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Introduction to ABM: Railsback, S. F., & Grimm, V. (2012). Agent-based and individual-based modeling: a practical introduction. Princeton university press.
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ABM in Geo Spatial Simulation: Crooks, A., Castle, C., & Batty, M. (2008). Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems, 32(6), 417-430.
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Agent Based Visualization: Grignard, A., & Drogoul, A. (2017). Agent-Based Visualization: A Real-Time Visualization Tool Applied Both to Data and Simulation Outputs
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Gama Platform: Grignard, A., Taillandier, P., Gaudou, B., Vo, D. A., Huynh, N. Q., & Drogoul, A. (2013, December). GAMA 1.6: Advancing the art of complex agent-based modeling and simulation. In International Conference on Principles and Practice of Multi-Agent Systems (pp. 117-131). Springer, Berlin, Heidelberg.
"When researchers want to study a particular phenomenon, they have several strategies. They can collect data on this phenomenon and study it using statistical tools, they can conduct experiments, and finally they can create an artificial reproduction of the phenomenon using a simulation. ABM is a simulation technique that represents explicit entities and behaviors in a program (Railsback and Grimm 2011). Agent-based models are composed of autonomous entities, called agents, operating in an environment interacting and organizing themselves (Treuil, Drogoul, and Zucker 2008). Such agents have attributes, behaviors, and capabilities of perception and communication. These agents might represent a multitude of phenomena ranging from the particle and cell to individuals or groups. ABM are characterized by their complexity, their dynamic, heterogeneous and multiscale" from Grignard, A., & Drogoul, A. (2017). Agent-Based Visualization: A Real-Time Visualization Tool Applied Both to Data and Simulation Outputs.
GIS Editor: We recommend using QGIS software (see http://www.qgis.org/en/site/) to manipulate your GIS data
GIS Data: For detailled GIS data http://download.geofabrik.de/
Alonso, Luis, et al. "Cityscope: a data-driven interactive simulation tool for urban design. Use case volpe." International Conference on Complex Systems. Springer, Cham, 2018.