"This week, we examined the simplest theory of spatial criminology – that crimes are distributed at random. When spatial criminologists want to test observed data for randomness, they test that data’s distribution against the Poisson distribution and look for a statistically significant difference. What is the null hypothesis in this situation? What conclusion would we draw from rejecting the null hypothesis?"

New contributor
Brielle is a new contributor to this site. Take care in asking for clarification, commenting, and answering. Check out our Code of Conduct.

If the occurrence of events is homogeneous and independent then the number of events per unit of exposure (e.g. area) will have a Poisson distribution, so you could test for a null of that particular kind of randomness by doing a goodness of fit test of a Poisson distribution.

You can then phrase the null either as a null of the process being random (a Poisson process) or as having a Poisson distribution.

If your null is of a non-homogeneous process then things become more complicated than that.

I would add that this seems a highly implausible model and I see no good reason to be testing for it.


Your Answer

Brielle is a new contributor. Be nice, and check out our Code of Conduct.

By clicking "Post Your Answer", you acknowledge that you have read our updated terms of service, privacy policy and cookie policy, and that your continued use of the website is subject to these policies.

Not the answer you're looking for? Browse other questions tagged or ask your own question.