1. Comments Task AS1 Tasks 12
Given a collection of boolean spatial features, the co-location pattern discovery process finds the subsets of features that are frequently located together.
Moreover, regional collocation mining approaches have been proposed that assess the strengths of collocation of sets of features in the geographical space. The determine the scope of a collocation patterns which is the area
There are many approaches to define the strength of a collocation pattern AB but we suggest to use density-based approaches that use non-parametric density functions A and B as follows:
AB are collocated in region R is A and B are both high in R.
AB are ani-collocated in region R of one of A and B is high and the other one is low.

2. Comments Task AS1 Tasks 12+13
Tasks are more open ended---you are not getting told what you exactly have to do: the goal is to develop data analysis and/or data visualization techniques that characterize the degree of change in crime density (task 13) and the strength of a collocation involving two types of crime (task12)
You might just use grid-based approaches for either task
Alternatively, you might directly compare the two density function or combine the two density functions e.g. using A*B or using max(A/B ,B/A) (or simply |B- A| for measuring the strength of anti-collocation) and then display the values of the combined density functions.
You might also employ sampling and ranking (of A and B) to shed light on the questions raised by Tasks 12 and 13.