[Corpora-List] Open Research Position (M.S. / Ph.D. / post-doc): Analyzing Routine Activities for Crime Prediction

Alexander Yeh asy at mitre.org
Wed Apr 23 05:57:54 CEST 2014


Zoltan Boka wrote:
> Predictions are only as good as the initial data they're based on. In
> this case that data could be incomplete or limited or biased (for
> instance, lets say that one data point is the number of arrests made on
> street x: lets also say that its police policy to harass and arrest
> people living on street x- you can see how this can infect the process.)
>
> But even if it can be done without bias and with a high accuracy, the
> question remains- are some people literally destined to commit crime and
> if so can that destiny be altered short of preemptively arresting them?

If you have a guess at a who is likely, you could concentrate undercover police to where (and when) that person tends to go and also to follow that person around. With enough evidence, you could also get search warrants and warrants to intercept their phone, etc.


>
> Sent from my iPhone
>
> On Apr 22, 2014, at 14:39, Adam Kilgarriff <adam at lexmasterclass.com
> <mailto:adam at lexmasterclass.com>> wrote:
>
>> If a clever system can predict who is going to predict a crime - with
>> good, but far from 100%, accuracy, is the use it
>> a) rational policing practice
>> b) discriminatory
>> to use that information?
>>
>> Seems to me, it's both.
>>
>> Marek says
>> > But there are definitely many ways to abuse this technology as well.
>>
>> I don't feel abuse is the main issue. Any use of it is
>> discriminatory. Should we trade off? Tough question.
>>
>> Adam
>>
>>
>> On 22 April 2014 11:34, Marek Rei <marek.rei at gmail.com
>> <mailto:marek.rei at gmail.com>> wrote:
>>
>> Here's an interesting article about how Chicago police is already
>> applying such technology (in somewhat troubling ways):
>>
>> http://www.theverge.com/2014/2/19/5419854/the-minority-report-this-computer-predicts-crime-but-is-it-racist
>>
>> I wouldn't say crime prediction technology by itself is evil, it's
>> more a question of how it's used. For example, I wouldn't have a
>> problem with a system that can prioritise a large list of likely
>> suspects after a crime has been committed, or is able to flag a
>> social media message calling for a hate crime. But there are
>> definitely many ways to abuse this technology as well.
>>
>> Marek
>>
>>
>>
>> On Tue, Apr 22, 2014 at 10:55 AM, Christian Pietsch
>> <chr.pietsch at googlemail.com <mailto:chr.pietsch at googlemail.com>>
>> wrote:
>>
>> Hi Matthew,
>>
>> so you want to build a heuristic precrime detector based on
>> routine
>> activities observed on social networks. Does that mean that
>> if, say, I
>> tend to update my status at the same time as some terrorist in
>> your
>> training set, your software will label me as a likely
>> terrorist and
>> put me on a no-fly list? Will I get a chance to prove my
>> innocence?
>>
>> When you have some spare time, try to watch Minority Report.
>> Or did
>> this movie inspire your project? Then you must have
>> misunderstood its
>> message.
>>
>> Your suspect
>> Christian
>>
>>
>> On Mon, Apr 21, 2014 at 11:34:11AM -0400, Matthew Gerber wrote:
>> > Hello,
>> >
>> > A new research position has opened within our lab, and we
>> are seeking M.S.,
>> > Ph.D., and post-doc researchers.
>> >
>> > One-sentence summary: We are mining social media for
>> indicators of
>> > individual routine activities for the purpose of improved
>> crime prediction.
>> >
>> > Longer summary: This project focuses on the spatiotemporal
>> prediction of
>> > localized attacks carried out against individuals in urban
>> areas. We view
>> > an attack as the outcome of a point process governed by the
>> interaction of
>> > attackers, targets, and the physical environment. Our
>> ultimate goal is to
>> > predict future outcomes of this process in order to increase
>> the security
>> > of human populations and U.S. assets and interests. However,
>> achieving this
>> > goal requires a deeper understanding of how attack outcomes
>> correlate with
>> > the routine activities of individuals in an area. The
>> proposed research
>> > will generate this understanding and in doing so will answer
>> questions such
>> > as the following: What are the dimensions along which
>> individuals’
>> > activities should be quantified for the purpose of attack
>> prediction? How
>> > can measurements along these dimensions be taken
>> automatically and with
>> > minimal expense (e.g., via social media)? What are the
>> implications of such
>> > measurements for attack prediction performance? Subsuming
>> these questions
>> > is the issue of geographic variation: do our answers change
>> when moving
>> > from a major U.S. city to a major U.K. city? There has been
>> plenty of
>> > previous work on spatiotemporal attack prediction (see our
>> Asymmetric
>> >
>> Threat<http://ptl.sys.virginia.edu/ptl/projects/asymmetric-threat-prediction>project);
>> > however, these basic questions remain unanswered, leaving a
>> > substantial gap in our understanding of attack processes and
>> their
>> > relationships with individuals’ routine activities.
>> >
>> > More information can be found
>> >
>> here<http://ptl.sys.virginia.edu/ptl/projects/routine-activities-analysis-for-crime-prediction>
>> > .
>> >
>> > Sincerely,
>> >
>> > Matthew S. Gerber, Ph.D.
>> > Research Assistant Professor
>> > Department of Systems and Information Engineering
>> > University of Virginia
>>
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>> --
>> ========================================
>> Adam Kilgarriff <http://www.kilgarriff.co.uk/> adam at lexmasterclass.com
>> <mailto:adam at lexmasterclass.com>
>> Director Lexical Computing Ltd <http://www.sketchengine.co.uk/>
>> Visiting Research Fellow University of Leeds <http://leeds.ac.uk>
>> /Corpora for all/ with the Sketch Engine <http://www.sketchengine.co.uk>
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