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1
Wildland Arson Crime Functions
David T. ButryNational Institute of
Standards and TechnologyGaithersburg, MD
Jeffrey P. Prestemon Southern Research Station
USDA Forest ServiceResearch Triangle Park, North
Carolina
and
2
Introduction There are 500,000 arson fires/year (wildland plus structural) in the
US, $3 billion in damages (National Fire Protection Association). Wildland arson is the leading single cause of wildfires in Florida. Arson ignitions on national forests have trended down over the past
1-2 decades, as have all causes. Area burned by accidental fire starts has trended upward over time,
apparently, in aggregate, although arson area burned has not trended.
Few have rigorously evaluated the underlying causes of short- or long-term temporal patterns.
3
Number of Ignitions by Fire Source on National Forests
0
2,000
4,000
6,000
8,000
10,00019
70
1974
1978
1982
1986
1990
1994
1998
2002
Lightning Ignitions
Other Ignitions
Arson Ignitions
4
Area Burned by Ignition Source on National Forests (FS + Protection)
9
10
11
12
13
14
15
1970
1974
1978
1982
1986
1990
1994
1998
2002
Ln
(Acr
es B
urn
ed)
Ln(Ligntning Acres)
Ln(Other Acres)
Ln(Arson Acres)
5
Crime and Arson It is apparent that arson is following
patterns similar to major crimes committed in the U.S.
Recent research shows that wildland arson is similar to violent crime in its response to law enforcement, criminal sanctions, and economic variables.
6
Crime Trend: Nationwide, Plus Wildland Arson on National Forests
0
1,000
2,000
3,000
4,000
5,000
6,000
7,00019
72
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
All-Crime Index
Violent CrimeIndex
Non-ViolentCrime Index
National ForestArson Index
7
Changes in Crime in Florida, 1972-2004
-0.30
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.501
97
3
19
76
19
79
19
82
19
85
19
88
19
91
19
94
19
97
20
00
20
03
Murder Index
Rape Index
Robbery Index
Assault Index
Burglary Index
Larceny Index
MVT Index
Arson Index/10
Average
8
Today’s Presentation Provide background information on
Florida’s arson situation Outline our econometric models Report results Describe implications for fire forecasting
9
Why is Learning About Arson Important? Arson fires threaten large values
More often in the WUI Arson wildfires are part of a larger
ecological process Behave similarly in response to management,
weather, fuels Evidence suggests that arson fires appear
to be clustered in both time and space.
10
Background on Arson Wildland arson has a long history
Especially in the South Florida has over 1,400 arson ignitions and 45,000
arson-ignited burned acres per year
Wildland arson is linked to demographic factors Old research quantifying the role of law enforcement Research identifying some links to socioeconomic
factors
11
Background on Arson Recent research links Arson to physical factors
Arson fires follow other fires in timing: Mostly during fire season (January-July) Peak in ignition rate in mid-afternoon Are more common in dry weather Respond to previous wildfires in the area
But arson fires differ from others: More ignitions on weekends Concentrated in spatial distribution—perhaps, closer to roads
and urbanized areas
12
Arson wildfire theory Serial and copycat arson behaviors imply a contagion process.
Current arson could be explained by previous arson ignitions. Other research identifies these behaviors for other kinds of crimes.
Law enforcement may play a role. Florida’s number of police officers per capita increased 12% between 1982 and 2001 but has
declined by 2% since 1995; trends vary by county. Much recent research identifying a negative relationship between law enforcement and crime.
Weather and land management may affect it. Dry weather makes firesetting easier Fuels management can affect success rates and opportunities
Leisure time could help explain it. Socioeconomic factors should explain some of it.
Population level should be related—more people, more arsonists? Poverty has been linked to other crimes. Arson models should control for this. Labor factors might explain it—wages, unemployment—affecting crime opportunity costs
(Becker, new research in AER, elsewhere).
13
Crime Model (Becker)
Oi is the number of offenses committed
πi is the probability of being caught and convicted
fi is the wealth loss experienced by the criminal if caught and convicted
ui measures other factors influencing the decision and success of completion of the crime
),,( iiiii ufOO
The decision to commit a crime is described as:
14
Arsonist’s Expected Utility from a Successful Ignition (Becker)
)()1()),(()( iiiiiiiiiiiii cgUwWfcgUOEU
Oi is the number of offenses committed
πi is the probability of being caught and convicted
gi is the arsonist’s psychic and income benefits from illegal firesetting
ci is the production cost for the firesetting
fi(Wi,wi) is the loss from being caught and convicted of the crime is a positive function of income while employed
Wi is the employment status
wi is wage
15
TERM DEFINITION FUNCTION OF:
Probability of being caught Law enforcement
f Loss from being caught and convicted Wage rateEmployment status
c Production cost of firesetting Time available Unemployment statusFuels and weatherVariables related to
ignition success
g Psychic and income benefits
from illegal firesetting
16
Arson Poisson Autoregressive Model PAR(p) Daily Ignition Model
jtjeyYyEp
iij
p
iitjijtjtj
βx '
1,
1,,1,,
,1]|[
yj,t is a vector of daily arson ignitions for location j
xj,t is a vector of independent variables (including a constant)
βj is a vector of associated parameters
j,i’s are the autoregressive parameters
17
Empirical Models County-level daily time scale Poisson Autoregressive models of
order p, PAR(p) Five high-arson county pairs in Florida 1994-2001
Locational daily time scale PAR(p) with spatio-temporal components Six high-arson Census tracts in Florida 1994-2001
Annual fixed-effects cross-section time series panel Poisson model Most Florida Counties 1994-2001
California national forests daily time scale PAR(p) 1993-2002
18
Study Locations
Spatio-temporal Analyses
19
Daily Time Series Model: Spatio-Temporal Analysis
The PAR(p) relates current day’s fires to Previous days’ fires, Presence of neighboring arson
Local—arson in surrounding Census tracts Regional—arson in Census tracts in same and
surrounding counties Long-term annual wildfires in the area (1-12 yr), Prescribed fire permits in the area (0-2 yr lags), Current fire danger index (KBDI), Seasonal factors: days of the week, months Socioeconomic factors: population, full-time equivalent
police officers per capita, poverty rate
20
Data Wildfire and prescribed fire from the
Florida Division of Forestry Socioeconomic data
U.S. Bureau of the Census University of Florida-Bureau of Economic
and Business Research Florida Department of Law Enforcement
Climate and weather from NOAA
21
Daily Locational Model Results Broadly significant variables
(significant across 3 or more models) Previous ignitions (up to 4 days) Previous local ignitions (up to
11 days) Previous regional ignitions (up
to 4 days) KBDI Some months Previous wildfire area (up to 5
years)
Significant variables
(significant across 1 or 2 models) Weekend days Poverty rate Unemployment rate Retail Wage Police Some months Previous prescribed fire
22
Daily Pooled Model Results*
Significant variables Previous ignitions (up to 10 days) Previous local ignitions (up to 11
days) Previous regional ignitions (1 day) KBDI Saturday Most months Previous prescribed fire (up to 1
year)
Insignificant variables Sunday Population Poverty rate Unemployment rate Retail wage Previous wildfire
*All variables interacted with population except AR terms, local ignitions, and regional ignitions.
23
Daily Model Results: Daily Autocorrelations
24
Simulated Outbreak Response Assume one unexpected arson ignition occurred
on April 30, 2005 Analyze using the pooled model results and with
continuous variables set at the pooled model means
Examine variation in response when outbreak occurs at different locations Same Census tract Local Census tract Regional Census tract
25Day after outbreak
Simulation—Response of an unexpected arson ignition on April 30, 2005.
26
Response to Outbreak 15.7 additional arson ignitions when
outbreak occurs in same Census tract 18.3 additional arson ignitions when
outbreak occurs in a neighboring “local” Census tract
17.6 additional arson ignitions when outbreak occurs in a neighboring “regional” Census tract
27
We Also Evaluated Effects of Law Enforcement Saturation Strategies
Ongoing work is seeking to develop hot-spotting models for law enforcement
28
Summary We have extended results from newly published
work in AJAE: wildland arson, at least in Florida, is spatially and temporally autoregressive.
Hence, wildland arson is a predictable process after an ignition occurs, potentially allowing for successful and effective law enforcement action.
Also implies that ignitions should be modeled that recognizes at least temporal and probably spatio-temporal autocorrelation (depends on the spatial scale of modeling) within daily time frames.
29
Questions
30
Law Enforcement Saturation Given an outbreak, examine how varying
levels law enforcement saturation affects future arson Levels of saturation are consecutive days,
following the outbreak, of arson prevention Saturation supposes perfect ability to control
arson ignitions (i.e., when there’s saturation, no arson ignitions occur)
31
Law Enforcement Saturation
32
Effect of Saturation Although an outbreak can have long-lasting effects
(several weeks), eleven days of saturation prevents any new arson ignition
Saturation has different effects depending on locational source of the outbreak (significance of differences across neighboring locations not evaluated)
On average, the following number of ignitions are prevented for each day of saturation 1.4 if outbreak occurred in same Census tract 1.7 if outbreak occurred in neighboring “local” Census
tract 1.6 if outbreak occurred in neighboring “regional”
Census tract
33
Law Enforcement Implications Focus enforcement on locations with recent and nearby arson
fires. Concentrate enforcement where arson fires have been ignited in last
ten days. Concentrate enforcement around where arson fires have been
ignited in last 2 days. Pay attention to weather trends.
Periods of hot, dry weather associated with higher arson risk Perhaps this is associated with the success of ignition, lower
expected “time and effort needed to obtain a successful ignition. There is a Saturday effect.
Count on Saturdays—lower opportunity costs of firesetting? This result is consistent with an economic model of crime, at least
for this variable.
34
Fire Management Implications
The use of prescribed fire is not found to be associated with lower arson risk
Locations with lots of wildfire are at lower arson ignition risk.
As other ignition risks, arson risk is closely tied to time of year and fuel flammability.