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Setting the neighborhood on fire
Helena Bohman
Malmö University
Background
A series of fire incidences in different parts of
Malmö, Sweden’s 3rd largest city, peaking in
2008
Part of a larger research project aiming at
explaining the outburst and spread of fire
incidences.
Purpose: provide an overall picture of the
underlying factors
600
500
400
300
200
100
1990
1995
2000
2005
Year
Inside of buildings
Outside of buildings
Figure 3. Number of intentional malicious fire
incidences in Malmö 1990-2006. Source: Swedish
Civil Contingency Agency
Previous studies
Criminological studies point to both
indiviualistic and environmental factors. In
general, firesetting is a juvenile crime ranging
from children’s play with fire to adolescents
more or less deliberate actions.
Motives: Expressive and instrumental
Origin: external or internal
0
20
30
200
400
cu1
50
40
600
60
70
800
Copper prices and fires in industrial areas
1990
1995
Copper price
Total number of fires
2000
year
2005
2010
Total number of fires, Industrial areas
Total number of fires
Source: London Metal Exchange.Index base year: 1990. Copper prices measured as
Copper, grade A, electrolytic wire bars/catho
Figure 2. Copper fires in industrial areas, total number of
fires and world market copper prices. Source: London
metal exchange and Swedish Civil Contingency Agency
Group of
variable
Chhetri et al.
(Chhetri et al.
2010)
Corcoran et al.
(Corcoran et al.
2007)
Type of fires
Building fires
Property,
vehicles,
abandoned
property and false
alarm
Unemployment
Percentage of
unemployed
Education
Ethnicity
Family
Housing
Car
Commodity
prices
Proportion of
indigenous
population
Families living in
separate
dwellings
One parent
Families with
small children
Corcoran et al
(Corcoran, Higgs
& Higginson
2011)
Building,
secondary,
vehicle, hoax
High education le
Share of white
population
High education
Non-whites
Childless couples
Couple families
crowding
% elderly and lowincome
car ownership
rates
Owner occupancy
Sidebottom et al.
(2011)
Cable burnings
car ownership
rates
Copper prices
Data
• Unbalanced panel on neighborhood level,
1992-2008
• Data primarily from municipality and
Swedish Civil Contingency Agency
Model specification
• Fires are incidence data -> Poisson or
related distribution
– Negative binomial model is used
Youth
(i)
(ii)
(iii)
(iv)
(v)
(vi)
(vii)
(viii)
(ix)
(x)
0.005
(8.83)**
0.004
(6.12)**
0.005
(5.18)**
0.005
(5.08)**
0.004
(4.01)**
0.003
(2.56)*
0.004
(2.65)**
0.004
(2.91)**
0.005
(2.71)**
0.006
(4.38)**
0.969
0.913
0.756
0.808
-0.203
0.390
0.861
2.353
1.797
(2.82)**
(2.11)*
(1.80)
(1.85)
(0.26)
(0.49)
(1.15)
(2.69)**
(2.47)*
-2.397
(4.09)**
-2.533
(2.97)**
-2.683
(2.75)**
-2.494
(2.83)**
-3.534
(3.29)**
-4.257
(4.23)**
Crowdedness
EduH
-3.226
(6.50)**
EduL
2.107
(5.84)**
0.000
(0.11)
Income per
capita
Households
on welfare
0.000
(1.31)
3.328
(1.85)
HH
Ethno1
0.633
(1.03)
Ethno2
1.754
(2.37)*
0.000
Copper
prices
Constant
Obs.
No. of areas
0.625
(6.73)**
0.056
(0.24)
0.812
(2.40)*
-0.464
(1.52)
(2.57)*
0.672
(1.95)
1.130
(2.22)*
0.963
(1.85)
0.559
(1.05)
0.176
(0.29)
0.440
(0.62)
1613
103
1443
100
975
100
975
100
975
100
589
99
578
97
553
93
456
93
687
100
Table 3. Regression results from negative binomial panel.
Dependent variable: total number of fires. Absolute value of z statistics in parentheses.
* significant at 5%; ** significant at 1%
Concluding remarks
Results are surprisingly robust considering the
type of data. The number of youths in the area
seems to be
Model specification has considerable impact
However, the geographic dimension needs to
be included in order to provide policy
implications
Thank you!
• References
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