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Introduction

The CPG_CityScope_GAMA pedestrian simulation currently relies on GIS (Geographic Information System) data. In order to successfully run the CPG_CityScope_GAMA on an area of interest, it is necessary to properly gather and format the relevant GIS data. The following should provide a detailed documentation on how to gather and label the five GIS data layers required to successfully run the CPG_CityScope_GAMA simulation on any real world area of your choice.

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/

Basic GIS Layer Structure

In order to create a GIS Map compatible with the CPG_CityScope_GAMA simulation, you will need 5 GIS data ‘layers’.

File Type	Definition
layer.shp	The main shapefile containing the actual geometry of the layer
layer.shx	A file containing the positional index of each shape in the main shapefile
layer.dbf	A file containing the attribute table for each shape in the main shapefile
layer.prj	A file containing coordinate system and projection format for the main shapefile
layer.cpg	A file containing the character encoding for the .dbf file

The files for each GIS layer can be obtained online either for free or through a purchase. Many online datasets do not provide complete data, so gathering and editing your own might be necessary in order to model the area you are interested in. For detailed information on the file extensions mentioned above, please refer to https://en.wikipedia.org/wiki/Shapefile.

File Type	Type
Buildings.extension (.shp, .shx, etc.)	Polygon
Roads.extension (.shp, .shx, etc.)	Line
table_bounds.extension (.shp, .shx, etc.)	Polygon
Amenities.extension (.shp, .shx, etc.)	Point
Bounds.extension (.shp, .shx, etc.)	Polygon

Building

Attribute Name	Input Type	Input Length	Description
Usage	String	‘O’: office, ‘R’: Residential
Scale	String	'S': small, 'M': Medium, 'L': large
Category	String	'Uni': university, 'Shopping': commercial area, 'Restaurant': dining area, 'Night': nightlife, 'Park': communal greenspace, 'HS': high school, 'Cultural': areas of cultural significance
Floors	int	From 0 to 100

Road

The “Roads” layer has an empty attribute table.

Table Bounds

This layer represents the bounds of the CityMatrix grid inside of the simulation. This layer is a polygon type GIS vector dataset. If you wish to use the CityMatrix grid in your simulation, then choose the specific area you would like to modify using the CityMatrix grid during the simulation. If not, then choose any arbitrary space within the area of interest you have bounded, and draw a single polygon inside.

The “table_bounds” layer has an empty attribute table.

Amenities

This layer represents 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. This layer is a point type GIS vector dataset.

Attribute Name	Input Type	Input Length	Description
Type	String (text)	20 characters long	The type of amenity this is (e.g. ‘restaurant’, ‘pub’, ‘fast food’)
Name	String (text)	100 characters long	The name of the amenity (e.g. ‘Saul’s Deli’, ‘In-N-Out Burger’)
Usage	String (text)	1 character long	Each object should have this set to 'A'
Scale	String (text)	1 character long	TBD

Bound

This layer represents the bounds of the simulation. Agents will not be able to exit this perimeter. This layer is a polygon type GIS vector dataset.

The “Bounds” layer has an empty attribute table.

#Agent Based Litterature

Here is some litterature about the Agent Based Modelling approach.

• Introduction to ABM: Railsback, S. F., & Grimm, V. (2012). Agent-based and individual-based modeling: a practical introduction. Princeton university press.

• 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.

• Agent Based Visualization: Grignard, A., & Drogoul, A. (2017). Agent-Based Visualization: A Real-Time Visualization Tool Applied Both to Data and Simulation Outputs

• 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.