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From "Suresh Marru (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SIS-324) Develop Hidden Markov Model to predict criminal moments
Date Thu, 24 Mar 2016 18:17:25 GMT

    [ https://issues.apache.org/jira/browse/SIS-324?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15210697#comment-15210697
] 

Suresh Marru commented on SIS-324:
----------------------------------

Hi Jesse,

* In your draft you have phrased it using "we". Once you do the work thats the right thing
to say. But this is a proposal you as in indiviual are applying to GSoC, so I suggest using
"I" instead. 

* Can you indicate how at the end of each milestone (the two week period) the demonstrations
will be? I mean you need to write instruction by means working example at end of the milestone
so others can get upto speed.

* Can you also include in your timeline, time for you to write documentation to the code and
also unit level test coverage? You should not wait for these to the end, but do it on a ongoing
basis.

Suresh

> Develop Hidden Markov Model to predict criminal moments  
> ---------------------------------------------------------
>
>                 Key: SIS-324
>                 URL: https://issues.apache.org/jira/browse/SIS-324
>             Project: Spatial Information Systems
>          Issue Type: New Feature
>            Reporter: Suresh Marru
>         Attachments: GSoC.pdf, GSoC.pdf
>
>
> The proposed project extends https://issues.apache.org/jira/browse/SIS-97.
> The Agent based modelling project during GSoC 2013 used a probabilistic model that was
hardcoded. It served as the basis to predict criminal’s movements which infer crimes. This
proposed project should do the inverse. Predict the probabilistic model that controls the
criminals’ behavior using data about his movements and crimes.
> The project should be preliminary work for data mining. From a sample anonymised emergency
call (911 data) a criminal should be uniquely identified. Hidden Markov Model, a probabilistic
state transition system, i.e., we define states such as “at home”, “in office etc”,
“roaming mode” etc, and there are probabilistic transitions between them. We can associate
some behavior to a particular state. Thus, the probabilistic model that was hardcoded by Nadeem
(in GSOC 2013) was a Markov model (this is a little indirectly). When we only have the crime
data and we want to find the model that dictates the criminal’s behavior, the Markov model
is hidden to us.  There are algorithms that can do this. These algorithms need to have their
parameters set by humans – such as the number of states. So this would need some amount
of experimentation.



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