tools for police investigation - law.kuleuven.be · • step 1: the police of the region of...
TRANSCRIPT
Stefan Bogaerts, Marjolein Misller, & Marinus Spreen
Tools for police investigation
Content
Tools FSNA
Sweetie-2
• Real life crimes: starting points for police investigation:
• A direct/indirect victim
• Offender-traces (f.e., DNA, …)
• Crime scene
• Eye-witness
• However, very often, there are no traces of perpetrators, no eye-witness, little information from the victim due by shock effect and more generally: unreliability of eyewitness
Social Network Analysis
• What can Forensic Social Network analysis offer police investigators?
• Typical for online and offline child (sex) offenders:– Hidden population
– Detecting and identifying is not easy
– Method as capture-recapture are not helpful and only interesting to estimate the hidden populatie
– So, we have to develop a method to improve and help police investigators.
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Social Network Analysis
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Social Network Analysis
• This method is: Bipartite Graph analysis
1
5
2
6
3
4 7
1,2, 3 and 4: registered
sex-offender (White
vertices)
5-7: hidden population
but in connection with
known population
Si i
i
Si i
d
y
d
y1
1ˆ
• CASE:
Suppose that the city of Rotterdam is startled by a series of rapes on female adults in
the last four weeks. Two interesting data sources can be consulted to help the police
investigation: first, the local police database of Rotterdam and second, databases of
forensic psychiatric centres in Rotterdam where rapists, forced by the judge, must
follow mandatory inpatient or outpatient treatment. The question is whether a
method exists that can deepen the investigation.
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Social Network Analysis
Two starting questions are interesting:
• (1), these known sex offenders can perhaps give information about a potential suspect because, for example, someone behaves rather strange in the last few weeks, or someone was unannounced not present during the treatment or probation appointment,
• (2) Those questioned network members can designate other fellow network members which may also provide significant information. This method is based on the B-graph sampling method and makes use of the snow ball sampling method.
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Social Network Analysis
• Following questions:
a. What is the number of the known convicted rapists in the region of
Rotterdam?
b. Who is currently under supervision of a psychiatrist/psychologist or
probation?
c. Of those who are under supervised, are there violations of conditions in
the past period parallel to the start of the offenses?
d. What police information is available at an individual level?
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Social Network Analysis
• What’s the first product?
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Social Network Analysis
• Step 1: the police of the region of Rotterdam interviewed 5 persons (A to E in the row and column). None of the 5
respondents had contact with the unknown with or information of the target and there were no contacts between themselves.
• Step 2: each of the 5 respondents were asked to indicate 2 names (from Fa to Oe in the row and column) of known
(past) sex offenders or/and perpetrators who can be related to each other or can probably give some information about the
unknown target. After interviewing these respondents “Fa to Oe”, the police received more information about potential
network relations (ties). For example, in the spreadsheet, we see that person Hb has some information of person B, Fa, Ga,
and Ib. We also see that person Kc has information of C and Ld. Further and more interesting, we see that 3 network
members, namely Jc, Ld and Oe appointed – in their view - a suspicious person.
• Very interesting is that these three network members, independent of each other because the police interview took
place at the same time, referred to a same person. The reason for this was that they have noticed something suspicious to this
person.
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Social Network Analysis
• Disadvantage of the shown spreadsheet is the absent of a global overview
of network relationships and a lack of insight in network tides (density).
Therefore, we have used Ucinet 6 for windows. This software program is free to
download. There is also a learning application on YouTube. Ucinet 6 offers the
advantage that the information in the spreadsheet becomes transformed to
“visual network relationships” in which the nods and the ties are visible.• https://sites.google.com/site/ucinetsoftware/downloads• Borgatti, S.P., Everett, M.G. and Johnson, J.C. 2013. Analyzing Social Networks. Sage Publications UK.• http://www.search.ask.com/search?psv=&apn_dbr=ie_11.0.9600.17239&apn_dtid=%5EOSJ000%5EYY%5ENL&itbv=12.15.5.30&p2=%5EBBE%5EOSJ000%5EYY%5ENL&apn_ptnrs=BBE&o=APN11406&gct=hp&pf=V7&tpid=ORJ-SPE&trgb=IE&pt=tb&apn_uid=CA0DB5EC-0EC1-4C09-8427-C83680B37044&doi=2014-08-31&q=ucinet+downloaden&tpr=10&ctype=videos
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Social Network Analysis
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Social Network Analysis
Figure: Network relations of interviewed network members and their relationship with a suspect person
• 750,000 online predators
• Approx. 40,000 chat
• 8 weeks: 20,000 hits (only 19 chat rooms)
• In 10 weeks: 1,000 predators were identified (IPs); from 76 countries
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World wide online Predators!
Sweetie-1
• Sweetie-2
• Terre des Hommes, Tilburg University,
ICT specialists, international law
enforcement
• Goals: developing 6,000 chat rooms
• provoking?? Is that legal?
• How are the targets?
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World wide online Predators!
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World wide online Predators!
Deter
Yellow card
Track and trace
Cooperation with LE,
Extended criminal
Investigation procedures
• Information mapping is important for criminal investigation
• Police capacity is a real problem!
• Online crimes: there are possibilities but: police officers, prosecutors, judges are often confronted with country-related Rules what can cause delay
• One policy, one voice in Europe
• Criminals are smart and very fast; geographical mobility.
• ….
• Thank you!
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Conclusions
Internet & primary school children
S. Bogaerts, M.B.J. Brusselaers, M.A. Missler,
K. Demeijer, J.D. Schilder
Young children and the internet
• Content
• Contact
• Commercial
De Moor and colleagues 2008
Negative side
When do we intervene?
Afname 1
1437 kids
Afname 2
812 kids
Afname 3
812 kids
Our Study
Flemish primary school children5th and 7th grade 6th and 8th grade
Role of the parents
Circumventing internet parental control and online pretending in
primary school children: Family related differences and predictors
Brusselaers, M. B. J., Bogaerts, S., & Demeyer, K.
The influence of parental supervision on online risk behavior among
young children: a gender perspective
M.A. Missler & S. Bogaerts
What children think and do
‘I know more than both of my parentstogether’
• Rules & restrictions
• Circumventing control
– Absence parents (45.8%)
– Secret profiles (18.7%)
– Code words (15.1%)
• Online pretending
– Older (31.1%)
– Someone else (2.9%)
– Both (6.9%)
– More than once (17.1%)
Gender, Age, & Family
• Parents originated outside the EU
• Grade
• Boys vs Girls
Role of schools
The Effectiveness of an Intervention to promote Awareness and
Reduce Online Risk Behavior of Belgian Primary School Children
Janneke D. Schilder, Marjolein B. J. Brusselaers, & Stefan Bogaerts
Afname 1
1437 Ch.
Afname 2
812 Ch.
Afname 3
812 Ch.
Intervention
Flemish primary school children5th and 7th grade 6th and 8th grade
Repeated measures not possible in our design
Intervention
The effect
Time 1
Intervention = ↑ Risk Awareness (β =.39, t(806)= 9.54, p= <.001)
Intervention ≠ ↓ Risk Behavior (β = .013, t(803)= 1.00, p= .317)
Time 2
Intervention = ↑ Risk Awareness (β =.13, t(790)= 2.93, p= .004)
Intervention = ↑ Risk Behavior (β = .033, t(790)= 2.23, p= .026)
Gender
Boys ↓ risk aware, and ↑ risk behavior
Recommendations
- More investment of primary schools
- Integration in the educational system
- More research concerning effective interventions
Conclusions
• Positive aspects
• ‘learn how to swim’
• Parental supervision
• Primary school
Young adults (students): online
protection and behavior
Stefan Bogaerts & Karel Demeyer & Lynn de Vuyst
Content
Privacy Concerns
Risk Concerns
Information Protection
Research group
Law Informatics Criminology total
gender:
Male 79 (34,2%) 42 (79,2%) 40 (19,6%) 161 (33%)
Female 152 (65,8%) 11 (21,8%) 161 (80,4%) 327 (67%)
Frequently used SNS:
Facebook 228 (98,7%) 51 (96,2%) 202 (99%) 481 (98,6%)
My Space 1 (0,4%) 0 (0,0%) 0 (0,0%) 1 (0,2%)
Orkut 0 (0,0%) 0 (0,0%) 1 (0,5%) 1 (0,2%)
Google + 1 (0,4%) 2 (3,8%) 1 (0,5%) 4 (0,8%)
Netlog 1 (0,4%) 0 (0,0%) 0 (0,0%) 1 (0,2%)
grade:
First year 57 (24,7%) 14 (26,4%) 38 (18,6%) 109 (22,3%)
others 174 (75,3%) 39 (73,6%) 166 (81,4%) 379 (77,7%)
Tabel: Demografic characteristics of the research group
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Privacy concerns
Parameter B
Std.
Error
95% Confidence
Interval
Sig. Exp(B)
95% Confidence
Interval for Exp(B)
Lower Upper Lower Upper
threshold 1 -6,967 1,5415 -9,988 -3,946 ,000
,004
,115
,784
,224
,224
,008
,303
.
000
,000
,030
,.
,001 4,594E-5 ,019
threshold 2 -4,319 1,5191 -7,296 -1,341 ,013 ,001 ,261
threshold 3 -2,381 1,5109 -5,343 ,580 ,092 ,005 1,786
threshold 4 -,419 1,5288 -3,415 2,578 ,658 ,033 13,170
extraversion -,383 ,3152 -1,001 ,235 ,682 ,367 1,264
unpleasant experience
privacy invasion
females
males
age
law students
informatics students
criminology students
-,285
,556
,208
0a
-,145
-,814
-,737
0a
,2347
,2084
,2021
.
,0399
,1896
,3396
.
-,745
,147
-,188
.
-,223
-1,186
-1,402
.
,175
,964
,604
.
-,067
-,442
-,071
.
,752
1,743
1,231
1
,865
,443
,479
1
,475
1,159
,829
.
,800
,306
,246
.
1,191
2,622
1,830
.
,936
,642
,931
.
Table: Parameter estimates of the effect of ‘extraversion’, ‘unpleasant experience’, ‘privacy invasion’,
‘gender’, ‘age’, and ‘education type’ on ‘privacy concerns’
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Risk concerns
Parameter B
Std.
Error
95% Confidence
Interval
Exp(B)
95% Confidence Interval
for Exp(B)
Lower Upper Sig. Lower Upper
threshold 1 ,582 1,4943 -2,347 3,510 ,697 ,697 1,789 ,096
threshold 2 2,879 1,5003 -,061 5,820 ,055 ,055 17,798 ,940
threshold 3 5,292 1,5208 2,311 8,273 ,001 ,001 198,751 10,087
extraversion ,178 ,3171 -,443 ,800 ,574 ,574 1,195 ,642
unpleasant experience 1,006 ,2411 ,534 1,479 ,000 ,000 2,735 1,705
privacy invasion -,403 ,2083 -,811 ,005 ,053 ,053 ,668 ,444
females ,283 ,2007 -,110 ,677 ,158 ,158 1,328 ,896
males 0a . . . . . 1 .
age -,002 ,0410 -,082 ,079 ,971 ,971 ,998 ,921
law students ,698 ,1899 ,326 1,070 ,000 ,000 2,009 1,385
informatics students ,583 ,3387 -,081 1,247 ,085 ,085 1,792 ,923
criminology students 0a . . . . . 1 .
Table: Parameter estimates of the effect of ‘extraversion’, ‘unpleasant experience’,
‘privacy invasion’, gender’, ‘age’, and ‘education type’ on ‘risk concerns’
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Information protection = behavior!
Parameter B
Std.
Error
95% Confidence
Interval
Exp(B)
95% Confidence Interval
for Exp(B)
Lower Upper
Sig.
Lower Upper
threshold 1 -2,317 2,0980 -6,429 1,795 ,269 ,099 ,002 6,020
threshold 2 1,847 2,0775 -2,225 5,919 ,374 6,341 ,108 371,993
threshold 3 6,006 2,1612 1,770 10,242 ,005 405,992 5,873 28064,252
extraversion ,955 ,4425 ,087 1,822 ,031 2,598 1,091 6,184
unpleasant experience ,434 ,3215 -,196 1,065 ,177 1,544 ,822 2,900
privacy invasion -,082 ,2740 -,619 ,455 ,765 ,921 ,539 1,576
females -,146 ,2870 -,708 ,417 ,612 ,864 ,493 1,517
males 0a . . . . 1 . .
Age -,128 ,0617 -,249 -,007 ,038 ,880 ,780 ,993
law students ,794 ,2666 ,272 1,317 ,003 2,213 1,312 3,731
informatics students ,471 ,4320 -,376 1,317 ,276 1,601 ,687 3,733
criminology students 0a . . . . 1 . .
Table: Parameter estimates of the effect of ‘extraversion’, ‘unpleasant experience’, ‘privacy invasion’, ‘gender’,
‘age’, and ‘education type’ on ‘information protection’
