[Corpora-List] Open Research Position (M.S. / Ph.D. / post-doc): Analyzing Routine Activities for Crime Prediction
Markus Heller
heller at relix.de
Wed Apr 23 01:05:05 UTC 2014
Scientific interest taken for granted. But where are we going?
Preemptive arresting as a standard for modern society? And based on
because somebody has said something? This rather reminds me of the very
dark (not only) medieval ages in Europe... It can only pave the way to
dictatorship. No excuses here, please.
br
markus
Am Dienstag, den 22.04.2014, 20:23 -0400 schrieb Zoltan Boka:
> 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?
>
> Sent from my iPhone
>
> On Apr 22, 2014, at 14:39, Adam Kilgarriff <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> 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> 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 adam at lexmasterclass.com
> >
> > Director Lexical Computing Ltd
> >
> > Visiting Research Fellow University of Leeds
> > Corpora for all with the Sketch Engine
> > DANTE: a lexical database for English
> >
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