Moving agents around a city - followup

Following on from a previous post about how to move agents around a road network using Repast Simphony, I have just finished developing a prototype version which others can download. The program loads a small hypothetical town with a few simple agents, some houses and some roads. The agents choose a random house and then travel there.




The code and some description about how it works are here. I hope it will be useful for others who are developing agent-based models or crime simulations in particular.

Artificial Crime Analysis Systems book

I have recently come across this new book, edited by Lin Lui and John Eck: Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems.

It is a collection of papers by the authors of new agent-based crime models as well as discussions surrounding other issues with crime simulation (such as validation, verification etc). This book is long overdue and very relevant, I recommend it as the first place to look for anyone interested in crime simulation.

Presentation at the British Society of Criminology conference

I have just given a presentation entitled "Improving burglary-reduction initiatives through a realistic agent-based model." at one of the "Geographies of Crime" sessions, organised by Professor Alex Hirschfield at the University of Huddersfield.

The abstract and slides are on my School of Geography webpage.

Presentation: Realistic Crime Simulation.

I recently attended a workshop at the National Centre for e-Social Science Fourth International Conference and gave a short presentation entitled "Realistic Crime Simulation: Predicting crime rates through a sound model of offender behaviour coupled with an accurate virtual environment" (it can be downloaded from my School of Geography webpage). The abstract is as follows:

Understanding the relationship between crime and the environment is an important research area in criminology. However, human systems are highly complex and influenced by a huge number of individual components, complex spatio-temporal interactions and human decisions which are at best complex and at worst seemingly irrational. As with other human systems, the complexity of the crime system, therefore, makes it extremely difficult to understand and predict crime patterns and to generate effective crime reduction policies.

To credibly model crime it is essential to be able to model the complex, dynamic interactions of the individuals and their environment which constitute a crime event. However, most studies to date suffer from an inability to model these micro-level interactions. Agent-based modelling is a methodology which works from the “bottom-up” and is able to capture emergent phenomena such as city-wide crime patterns which are built up from a multitude of individual crime occurrences. Such techniques are becoming very popular in the fields of urban studies in geography, but are still rarely used in criminology or by policy makers.

This paper will present an advanced agent-based model which couples a realistic framework for modelling offender behaviour with an accurate GIS virtual environment using the Repast Simphony toolset. We will use the model to evaluate and predict the success of genuine crime reduction scenarios implemented in the city of Leeds.

Visualising Agent Movements with GeoTime

I've recently started using GeoTime (r), produced by Oculus who were kind enough to kind enough to provide a student license. I think the software is intended for the police but it works very well for visualising any type of movement in a space. It's particularly good for analysing the movements of agents. It provides plug-ins for Excel and ArcGIS so it's easy to send model results to GeoTime and then visualise them.

These screen shots* show a burglar agent who is searching for a potential victim. The horizontal axis is used to show movement in space and the vertical axis shows movement over time. You can use Arc to project a map on the spatial plane. The bar at the bottom of the screen allows you to restrict the events to a particular temporal range and it can be dragged to show how movements vary dynamically (will try to make a video of this).






The software is particularly useful for debugging and validation. You can look at the behaviour of the agents very carefully and see if they are behaving as they should. For example, the agent above walked up and down the same street about 10 times. This is probably not accurate and shows that work is needed on the behaviour of the agents to bring it in to line with criminology theory.

You can also use the software to visualise multiple agents. This will be particularly useful when agents can interact because it is easy to see where groups of agents cluster spatially and temporally.



*GeoTime® software used courtesy of Oculus Info Inc. All GeoTime rights reserved.

Repast Simphony "no-GUI" Tutorial

UPDATE (7th March 2013): The link below doesn't work any longer but as the new Repast tutorials have been improved I don't intend to update it. Have a look at the Repast homepage.

I've been using Repast Simphony to build my agent-based crime model. Repast is a tool kit which has lots of really useful functions for organising agents, creating displays, scheduling events, working with GIS data, drawing charts, outputting data etc. Although Simphony is excellent I think there's quite a steep learning curve so I've written a short (1 hour) tutorial which will hopefully help to get people started:

http://portal.ncess.ac.uk/access/wiki/site/mass/simphony%20tutorial.html

Simphony now has a GUI which you can use to build models (the main tutorial goes through how to use this) but I've avoided this, preferring to program models directly.

Using Repast to move agents along a road network

Introduction

Some people have asked how I have used Repast Simphony to create a virtual city in which agents’ movements are restricted to a road network. So here I’ll briefly outline the method I used. I was still very new to Repast when I worked this out so I'm sure there’ll be much more efficient methods, comments/criticisms are welcome.

The basic idea is to create a GIS projection to hold the spatial locations of all the roads and a Network projection to store the topographical relationships between the roads. So if an agent needs to get to a destination we ask the Network projection which roads it has to travel along, then actually plot a course using the GIS projection.

Firstly I’ll outline a bit more about the contexts and projections and then briefly discuss how the agents move around the city.

Creating the city – contexts and projections

There are two types of objects I use to represent the road network: Roads and Junctions (or “Intersections” if you prefer). Junctions are created at the points where two roads meet and will form the Nodes in a Network projection. I used two separate contexts to store these objects called: RoadContext and JunctionContext. I also created two GIS projections and a Network projection from these contexts:



I have used separate contexts because I only want Junction objects in the RoadNetwork projection (they will form the nodes of the network) and couldn't see a way of doing this if Roads and Junctions were kept in the same context.

To read in the road data and add them to the context I saved the road network as an Arc Shapefile and then used ShapefileLoader. Simphony has made this really easy, it only takes a few lines of code.

To create the Junction objects I iterate over all the Road objects and create a Junction wherever two roads meet. Then I add the Junctions to the JunctionContext (so they are also automatically added to the RoadNetwork projection) and create an edge between them (setting the weight as the distance of the road segment). So now there are Road objects in the RoadGeography and repast edges in the RoadNetwork. It's important to keep a list of roads and edges so we know which road each edge corresponds to, this is how we can link the GIS projection to the Network projection.

That’s all that is required to create the city road network. Now we have the spatial information (the RoadGeography) and topographical information (the RoadNetwork) about the roads. In the next section I’ll outline how to move agents around the city.

Moving around the city

I will assume that the agent is starting on a road segment. If the agent isn’t on a road (they could be at home for example) then a few extra steps will be needed to move them onto the nearest road first. The routing algorithm works by firstly building a list of coordinates which the agent must pass through to get to their destination and then moving along the planned route.

Planning the route

The following diagram illustrates a hypothetical route and the junctions which the agents will pass through to get between their origin and destination.



The following steps briefly outline how to create a route:

1. Find the nearest Junctions to the agent’s starting position and the destination.
2. Calculate a list of edges (in the RoadNetwork projection) which make up the bulk of the route (I use the repast.simphony.space.graph.ShortestPath.getPath() function).
3. Create a list of coordinates which the agent must pass through to get from the origin to the destination. This is done by:
1. Iterating over all the edges which make up the route and:
2. Finding the Road object (in the RoadGeography GIS projection) which corresponds to the edge (this is why it is necessary to keep a list of edges and their corresponding road when building the city).
3. Adding all the coordinates which make up the geometry of the road to the list.
   

(If the road network is a true manhattan grid then there are a number of ways of improving the efficiency of the routing algorithm. This is rarely the case in European cities though so I assume the road network is much less ordered and that roads have bends in them. Also, I assume that the agent will want to get to destinations which are part-way along a road segment, if this restriction is removed (so the agent only travels to Junctions) then the algorithm can be made even simpler.)

So now the agent will have a list of coordinates which will take the agent from an its origin to its destination, restricted to roads.

Moving

My agents are only allowed to move a certain distance each turn, so the last step which is required is to calculate whether or not the agent can make it all the way to the next coordinate in it’s list and if not, where it will end up. This is fairly straightforward using a vector.



If the agent cannot make it all the way from coordinate (b) to coordinate (a), you can move it the maximum remaining distance it is allowed to travel in the direction of (a). Then in the next iteration it can continue on towards (a) as normal.


That's it, I’m sorry if that isn’t very clear but I hope it will help. Please email me if you have any questions.