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Offender insight into the
Australian stolen goods
markets
The DUMA survey as a longitudinal window into
property offenders’ target selections
Dr Joe Clare UWA School of Law
Dr Rick Brown AIC
Applied Research in Crime and Justice Conference
12-13 February 2020
Brisbane, Queensland
I would like to thank the Western Australian Police Force
and the Australian Institute of Criminology for making their
data available for this analysis
Acknowledgements
1 2
• Background to burglary – some data and the security
hypothesis
• Is security the whole story?
• How else can we explore this? What do offenders say?
• What does any of this mean?
What I want to cover
1 3
1. Background to
burglary – trends and
the security
hypothesis
Residential burglary is in decline
1 5 Source: CSEW domestic burglary in a dwelling
0%
10%
20%
30%
40%
50%
60%
70%
-
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
Domestic burglary in a dwelling Percent with entry Percent with loss
6.5% in 1993
1.7% in 2019
254,500
225,700
230,500
140,000
160,000
180,000
200,000
220,000
240,000
260,000
2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18
12-month burglary victimisation, 2010-2018 ABS
Burglary Attempted burglary
Australian trends – victim surveys
1 6
9% decline in
absolute number
Source: Extracts from Table 20 and 21 of Crime Victimisation Australia (ABS 4530), various years
14/02/2020
2
208,098 215,009
168,031
64.0%
65.0%
66.0%
67.0%
68.0%
69.0%
70.0%
0
40,000
80,000
120,000
160,000
200,000
240,000
2010 2011 2012 2013 2014 2015 2016 2017 2018
UEWI Victims, 2010-2018 ABS data
Unlawful entry with intent % UEWI with property loss
Australian trends – police recorded crime
1 7
19% decline in
absolute number
Source: Extracts from Table 1 Recorded Crime Victims – Australia (ABS 4510), 2019
WA trends – police recorded crime
1 8
19% decline in
absolute number
4 percentage
point decline
Source: WAPF data provided for research purposes to UWA
2.9%
2.1%
0%
10%
20%
30%
40%
50%
60%
70%
80%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Annual WA residential burglary victimization – WAPF data
Rate % with loss % with cash loss % jewellery loss
10 principles of opportunity and crime
1. Opportunities play a role in causing all crime
2. Crime opportunities are highly specific
3. Crime opportunities are concentrated in time and space
4. Crime opportunities depend on every day movements
5. One crime produces another
6. Some products offer more tempting crime opportunities
7. Social and technological changes produce new crime opportunities
8. Opportunities for crime can be reduced
9. Reducing opportunities does not usually displace crime
10. Focused opportunity reduction can produce wider declines in crime
9
Source: Felson, M., & Clarke, R.V. (1998). Opportunity makes the thief: practical theory for crime prevention
8. Opportunities for crime can be reduced
• The Security Hypothesis as an explanation for the Global Crime Drop
– Parsimonious to local changes and global trends
– See work by Farrell and colleagues (2010 onwards) for more detail
– Works very well for vehicle theft and electronic immobilisers
• The opportunity reducing methods of situational crime prevention can be
applied to all aspects of everyday life, but they must be tailored to specific
situations
– Risk, reward, effort, plus excuses and provocations
10
8. Opportunities for crime can be reduced
• The Security Hypothesis as an explanation for the Global Crime Drop
– Parsimonious to local changes and global trends
– See work by Farrell and colleagues (2010 onwards) for more detail
– Works very well for vehicle theft and electronic immobilisers
• The opportunity reducing methods of situational crime prevention can be
applied to all aspects of everyday life, but they must be tailored to specific
situations
– Risk, reward, effort, plus excuses and provocations
11
Tseloni et al. (2017) Security Journal
Used CSEW (2008-12) to estimate security
protection influence of target hardening
strategies
Most effective individual devices:
external lights and door dead locks
Door/window locks + external lights + security
chains 20 times greater protection
(relative to no devices)
Fewer successful entries in UK
1 12 Source: CSEW domestic burglary in a dwelling
63%
66%
56%
63%
59%
63%
61% 63%
61%
58% 59%
0%
10%
20%
30%
40%
50%
60%
70%
-
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
Domestic burglary in a dwelling Percent with entry Percent with loss
Security?
(Risk/Effort)
14/02/2020
3
254,500
225,700
230,500
203,700
170,800
205,500
140,000
160,000
180,000
200,000
220,000
240,000
260,000
2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18
12-month burglary victimisation, 2010-2018 ABS
Burglary Attempted burglary
More attempted burglaries in Australia
1 13
1% increase in
absolute number
Source: Extracts from Table 20 and 21 of Crime Victimisation Australia (ABS 4530), various years
14
Source: Brown, 2015, Trends and Issues 495, Australian Institute of Criminology
8. Opportunities for crime can be reduced
2. Is security the whole
story?
Also fewer burglaries with loss recorded
1 16 Source: CSEW domestic burglary in a dwelling
63%
66%
56%
63%
59%
63%
61% 63%
61%
58% 59%
53%
46%
55%
37%
40%
0%
10%
20%
30%
40%
50%
60%
70%
-
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
Domestic burglary in a dwelling Percent with entry Percent with loss
Less stuff worth
taking?
(Reward?)
69.3%
65.3%
64.0%
65.0%
66.0%
67.0%
68.0%
69.0%
70.0%
0
40,000
80,000
120,000
160,000
200,000
240,000
2010 2011 2012 2013 2014 2015 2016 2017 2018
UEWI Victims, 2010-2018 ABS data
Unlawful entry with intent % UEWI with property loss
4 percentage
point decline
Also fewer burglaries with loss recorded
1 17 Source: Extracts from Table 1 Recorded Crime Victims – Australia (ABS 4510), 2019
Also fewer burglaries with loss recorded
1 18
19% decline in
absolute number
4 percentage
point decline
Source: WAPF data provided for research purposes to UWA
2.9%
2.1%
66%
69%
64%
55%
0%
10%
20%
30%
40%
50%
60%
70%
80%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Annual WA residential burglary victimization – WAPF data
Rate % with loss % with cash loss % jewellery loss
14/02/2020
4
1. Opportunities play a role in causing all crime
2. Crime opportunities are highly specific
3. Crime opportunities are concentrated in time and space
4. Crime opportunities depend on every day movements
5. One crime produces another
6. Some products offer more tempting crime opportunities
7. Social and technological changes produce new crime opportunities
8. Opportunities for crime can be reduced
9. Reducing opportunities does not usually displace crime
10. Focused opportunity reduction can produce wider declines in crime
19
Source: Felson, M., & Clarke, R.V. (1998). Opportunity makes the thief: practical theory for crime prevention
Why are people getting in and not taking
things?
• Not all products are equally at risk for theft
• Products are more attractive to thieves when they are
20
6. Some products offer more tempting
crime opportunities
C oncealable – easier to remove, transport, and dispose
R emovable – you have to take it to steal it
A vailable – macro - , meso - , and micro - level availability
V aluable – without value, it is not worth stealing
E njoyable – relates to disposability of items – greater demand
D isposable – stolen goods are generally converted into cash/drugs
• Not all products are equally at risk for theft
• Products are more attractive to thieves when they are
• CRAVED items can change over time as a function of market forces
(the life-cycle theory, Wellsmith and Burrell, 2005, Brit.J.Crim)
– Stable CRAVED – jewellery, gold, cash
– Variable CRAVED – small electronic goods, clothes
21
6. Some products offer more tempting
crime opportunities
C oncealable – easier to remove, transport, and dispose
R emovable – you have to take it to steal it
A vailable – macro - , meso - , and micro - level availability
V aluable – without value, it is not worth stealing
E njoyable – relates to disposability of items – greater demand
D isposable – stolen goods are generally converted into cash/drugs
1 22
Is the decline in cash use contributing to
the trends?
Cash declines, jewellery stable…
1 23
19% decline in
absolute number
4 percentage
point decline
Source: WAPF data provided for research purposes to UWA
2.9%
2.1%
66%
69%
64%
55%
23%
12%
22% 22%
0%
10%
20%
30%
40%
50%
60%
70%
80%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Annual WA residential burglary victimization – WAPF data
Rate % with loss % with cash loss % jewellery loss
1 24
What about the influence of a shortening
product life cycle?
• “Technology based commodities such as mobile phones and computers
have shorter innovation cycle so that the previous generation becomes
obsolete faster, either functionally or psychologically.”
Lebreton & Tuma (2006) A quantitative approach to assessing the profitability of car and truck tire
remanufacturing. Int J Production Economics
• “Product life cycle in electronic industry is shorter than before due to
technology advances, and as a result, an outdated product could reach its
end-of-use even if it is still in a good condition.”
Hsueh (2011) An inventory control model with consideration of remanufacturing and product life cycle.
Int J Production Economics
• Implications for the market-side (value/disposability) of CRAVED
14/02/2020
5
1 25
0
2
4
6
8
10
12
14
16
0
100
200
300
400
500
600
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Playstation 2
Playstation 2 Console (theft) Playstation 2 Console (sales)
0
2
4
6
8
10
12
14
16
0
200
400
600
800
1,000
1,200
1,400
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Playstation 3
Playstation 3 Console (theft) Playstation 3 Console (sales)
0
5
10
15
20
25
0
100
200
300
400
500
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Playstation 4
Playstation 4 Console (theft) Playstation 4 Console (sales)
0
2
4
6
8
10
12
14
16
0
100
200
300
400
500
600
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Playstation Portable
PlayStation Portable (theft) PlayStation Portable (sales)
r = 0.98 r = 0.78
r = 0.89 r = 0.92
What about the influence of a shortening
product life cycle?
26
Source: Brown, 2015, Trends and Issues 495, Australian Institute of Criminology
What about the influence of a shortening
product life cycle?
What about the influence of a shortening
product life cycle?
27
Source: Brown, 2015, Trends and Issues 495, Australian Institute of Criminology
Shaw et al. (2015) Crime and the Value of
Stolen Goods (Home Office Report)
Analysis of the CSEW over a 20 year period
Found the average value of a single theft
declined by 35%
Concluded value, availability, and
disposability were key drivers in what gets
stolen
AKA REWARD
3. How else can we
explore this? What
do offenders have to
say?
The DUMA surveys as a source of
information
1 29
• Drug Use Monitoring in Australia (DUMA) survey
o Established 1999
o Quarterly survey of police detainees at multiple sites across Australia
o Participation voluntary and confidential
o Independent of police – administered by trained researchers
• DUMA stolen goods survey addenda undertaken 7 times since 2002
• N = 6,079 arrestees across the 7 iterations
• 21% said they had stolen something in the last 12 months
o Of these, 13% stole daily and 49% stole less than once a month
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
1 2 3 4 5 6 7
• 84% male
• Average age 31 years
• 17% Indigenous
• 14% spent time in youth detention
• 40% had previously been in prison
Who participated?
1 30
0% 10% 20% 30%
Violence
Property
Drugs
DUI
Traffic
Disorder
Breach
Other
Current charge (%)
0% 10% 20% 30% 40%
Nearly every day
2 to 4 times a week
Once a week
Monthly
Less than monthly
None in the last 6 months, but have…
Never in my life
6-month property offending frequency (2017 only)
14/02/2020
6
What did they prefer to take?
1 31
0%
10%
20%
30%
40%
50%
60%
70%
200
2 (Q
2)
200
3 (Q
2)
200
3 (Q
4)
200
5 (Q
4)
200
7 (Q
1)
201
2 (Q
2)
201
7 (Q
1)
Trends in what people usually take
Consumer electricals (% yes, usually) Cars (% yes, usually)
Computers (% yes, usually) Personal effects (% yes, usually)
Tools (% yes, usually) Clothes (% yes, usually)
Cigarettes (% yes, usually) Food (% yes, usually, 2007, 2012, and 2017 only)
What did they prefer to take?
1 32
0%
10%
20%
30%
40%
50%
60%
70%
200
2 (Q
2)
200
3 (Q
2)
200
3 (Q
4)
200
5 (Q
4)
200
7 (Q
1)
201
2 (Q
2)
201
7 (Q
1)
Trends in what people usually take
Consumer electricals (% yes, usually) Cars (% yes, usually)
Computers (% yes, usually) Personal effects (% yes, usually)
Tools (% yes, usually) Clothes (% yes, usually)
Cigarettes (% yes, usually) Food (% yes, usually, 2007, 2012, and 2017 only)
2017-specific data also showed
• 9% usually stole bikes
• 21% usually stole cosmetics
• 22% usually stole alcohol
• 16% usually stole drugs
Where did they prefer to take things
from?
1 33
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 6 6 11
2017-specific
Consumer electricals (22%) 81 6 6 3 3
Cars (12%) 0 44 39 0 17
Bikes ( 9%) 0 23 69 0 0
Personal effects (21%) 45 19 6 10 16
Tools (15%) 55 14 9 5 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 3 0 0 10
Clothes (32%) 96 2 0 0 2
Cigarettes (13%) 53 11 0 11 26
Alcohol (22%) 94 3 0 3 0
Food (64%) 98 1 1 0 0
Drugs (16%) 4 22 0 0 74
Where did they prefer to take things
from?
1 34
Sampling period Shop
(%)
House
(%)
Public place
(%)
Car
(%)
Person
(%)
2002-2012 weighted avg. 66 12 11
2017-specific
Consumer electricals (22%) 81
Cars (12%) 44 39 17
Bikes ( 9%) 23 69
Personal effects (21%) 45 19 10 16
Tools (15%) 55 14 18
Cash (20%) 13 23 13 13 23
Cosmetics (21%) 87 10
Clothes (32%) 96 2
Cigarettes (13%) 53 11 11 26
Alcohol (22%) 94
Food (64%) 98
Drugs (16%) 22 74
What did they do with the things they
took?
1 35
44%
11%
20%
64%
0%
10%
20%
30%
40%
50%
60%
70%
200
2 (Q
2)
200
3 (Q
2)
200
3 (Q
4)
200
5 (Q
4)
200
7 (Q
1)
201
2 (Q
2)
201
7 (Q
1)
Trends in what people do with things they take
Sell them Swap them for drugs Spend on other things Consume them Keep/use them Other
What did they do with the things they
took?
1 36
0%
10%
20%
30%
40%
50%
60%
70%
200
2 (Q
2)
200
3 (Q
2)
200
3 (Q
4)
200
5 (Q
4)
200
7 (Q
1)
201
2 (Q
2)
201
7 (Q
1)
Trends in what people do with things they take
Sell them Swap them for drugs Spend on other things Consume them Keep/use them Other
14/02/2020
7
Who did they sell/trace the things they
took with?
1 37
26%
51%
0%
10%
20%
30%
40%
50%
60%
200
2 (Q
2)
200
3 (Q
2)
200
3 (Q
4)
200
5 (Q
4)
200
7 (Q
1)
201
2 (Q
2)
201
7 (Q
1)
Trends in who people sell/swap with
Family member/friend/acquaintance Stranger Drug dealer
Fence Pawnbroker/second-hand dealer Other business
Other
Who did they sell/trace the things they
took with?
1 38
0%
10%
20%
30%
40%
50%
60%
200
2 (Q
2)
200
3 (Q
2)
200
3 (Q
4)
200
5 (Q
4)
200
7 (Q
1)
201
2 (Q
2)
201
7 (Q
1)
Trends in who people sell/swap with
Family member/friend/acquaintance Stranger Drug dealer
Fence Pawnbroker/second-hand dealer Other business
Other
• 21/35 respondents (2017) said they didn’t
know what happened to the goods after
they sold them
• 2% of 2012 respondents said they had sold
goods over the internet
• Increased to 9% of 2017 respondents
• EBay, Facebook, and Gumtree
How much did they get for the things
they took?
1 39
20%
40%
26%
0%
10%
20%
30%
40%
50%
60%
200
2 (Q
2)
200
3 (Q
2)
200
3 (Q
4)
200
5 (Q
4)
200
7 (Q
1)
201
2 (Q
2)
201
7 (Q
1)
Trends in prices attained for items
Less than one-quarter of what they're worth About one-quarter of what they're worth
About one-third of what they're worth About half of what they're worth
More than half of what they're worth About what they're worth
More than what they're worth
How much did they get for the things
they took?
1 40
0%
10%
20%
30%
40%
50%
60%
200
2 (Q
2)
200
3 (Q
2)
200
3 (Q
4)
200
5 (Q
4)
200
7 (Q
1)
201
2 (Q
2)
201
7 (Q
1)
Trends in prices attained for items
Less than one-quarter of what they're worth About one-quarter of what they're worth
About one-third of what they're worth About half of what they're worth
More than half of what they're worth About what they're worth
More than what they're worth
While frequency of selling
has declined
No clear indication that
prices have declined over
time
How does this relate to offending
patterns?
1 41
61%
18%
8%
4% 4%
8%
72%
12%
7%
2%
6%
1%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Shoplift Rob a person or shop Residential burglary Theft from a motorvehicle
Theft of a motor vehicle Sell stolen goods
Most frequent crimes in previous 12 months
2012 (Q2) 2017 (Q1)
4. What does any of this
mean?
14/02/2020
8
• Burglary continues to decline
o Attempts increasing – risk/effort
o Entry with no loss increasing – reduced reward
• Who were our arrestees?
o 10% offending at least weekly but one-third not for the last 6-months
• Target selection
o Huge preference for shops over houses
o Increases in food/clothes/personal consumables
o Declines in electrical/computing
• Use of goods – increases in ‘keeping’ and declines in ‘selling’
• Selling to – drug dealers and people they know
o Some indication internet being used
o Value consistent and low – typically 1
4 to
1
3 of original value
Summing it up
1 43
• Reducing reward is likely reducing offender motivation to commit
burglaries
• This idea is compatible with the security hypothesis (risk/effort)
• Also compatible with explanations of offending that draw on cognitive
psychology (domain-specific expertise) and economics
o Offenders have reduced opportunity to ‘learn’ how to be an
effective burglar – fewer available targets (macro- and meso-level)
and less chance to gain financial reward
o A no-result burglary and/or failed attempts to sell items that are
stolen may well influence the perceived utility of burglary
o Reducing the attractiveness of engaging in this crime in the future
Implications for theory
1 44
• Clear implications for targeted prevention interventions
• Continue to design-in/create techniques to reduce disposability of
stolen goods
o e.g., phone kill switches, immobilizers, preferred purchasers of
second hand metal, bike registers
• Disrupt stolen goods markets (traditional and emerging e-markets)
o The market reduction approach (see Sutton)
o Think ‘crime scripts’ and link with drug dealers as fences
• Potential for long-term sustainable crime prevention without arrest
or conviction (much like car immobilisers)
Practical implications
1 45
• Use available data to monitor trends
• Find out more about the stolen goods market in Australia
o The more we know, the more we can do to disrupt it
• Explore what is increasing in houses and shops
• Compare these signatures with other jurisdictions
Where to next?
1 46
Dr Joe Clare UWA School of Law
+61 8 6488 7956
Thank you
1 47
