When the agent has to walk between their friend's house, drug dealer and their home, they are able to explore more of the city on the way. This gives them a much greater knowledge of the area when they need to commit a burglary.
When they use public transport to travel, on the other hand, they do not explore the surrounding area as they would do if they were walking. Therefore the burglaries are much more clustered around their anchor points (home address, friend's house and drug dealer).
Although this is a simple result I'm hoping that this type of experiment will be very useful when trying to predict real burglary rates.
4 comments:
When agents are using own vehicle. On there way agents may also interact with open/free places where they meet drug dealers. You sould also consider this idea to get more promising results.
At the moment I have the model configured so that when the burglar is on public transport they don't add the areas that they pass through to their awareness space. This is why the agent who has to use public transport is more likely to commit crime near the transport hubs; they aren't familiar with as much of the city as the agent who has access to a car.
I am overall impressed with the elegance of your model.
Would you agree that 'burglary hot-spots" that are located away from transport routes are likely to be caused but people on foot?
Thomas, that's a difficult question to answer. I think research suggests that most burglaries aren't committed by people on foot because they have difficulties carrying the stolen goods. But it is possible that they became aware of the opportunity whilst walking around and then returned with a vehicle later. I wouldn't be surprised if there is some research that compares hotspots' spatial locations to the means that people used to commit crimes there (this might show if hotspots away from transport routes were created by people on foot), but I'm not aware of any..
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