Policing the Homeless: An Evaluation of
Eorts to Reduce Homeless-Related Crime
Richard Berk
Department of Statistics
Department of Criminology
University of Pennsylvania
John MacDonald
Department of Criminology
University of Pennsylvania
September 11, 2009
Abstract
Summary | Police ocials across the United States are increas-
ingly relying on place-based approaches for crime prevention. In this
article, we examine the Safer Cities Initiative, a widely publicized
place-based policing intervention implemented in Los Angeles’s \Skid
Row” and focused on crime and disorder associated with homeless
encampments. Crime reduction was the goal. The police division in
which the program was undertaken provides 8 years of times series
data serving as the observations for the treatment condition. Four
adjacent police divisions in which the program was not undertaken
provide 8 years time series data serving as the observations for the
comparison condition. The data are analyzed using a generalized ad-
ditive model. On balance, we nd that this place-based intervention
is associated with meaningful reductions in violent, property, and nui-
sance street crimes. There is no evidence of crime displacement.
Policy Implications |This study provides further evidence that ge-
ographically targeted police interventions can lead to signicant crime
1
prevention benets, with no evidence that crime is simply displaced to
other areas. Criminologists and the media have given the Los Angeles
Police (LAPD) little credit for major reductions in crime that have
occurred over the past ve years following a number of major policy
reforms. We suggest that researchers should look more closely at the
targeted interventions the LAPD has undertaken for evidence-based
examples of eective policing. Importantly, this work suggests that
crime associated with homeless encampments can meaningfully re-
duced with targeted police actions. However, law enforcement actions
do not address the roots of homelessness nor most of its consequences.
Getting tough on the homeless should not be confused with policies
or programs that respond fundamentally to the social and personal
problems that homelessness presents.
1 Introduction
A growing body of evaluation research supports the practical benets of
placed-based policing. Yet, scholars are often critical of such approaches in
part because place-based interventions can shift the burden of crime to other
areas. Perhaps most directly, crime displacement can result from geographically
targeted police-crackdowns (Sherman, 1990), although displacement
appears to be less problematic when place-based policing is attuned to the
local criminal environment (Weisburd et al., 2006). As Weisburd and colleagues
(2006) note, crime does not just move around the corner. More
fundamentally, there is little reason to expect that police interventions alone
change underlying social and economic factors that contribute to crime. Effective
police interventions can make a short-term dierence in a given locale
(Skogan, 1990), but on larger spatial and temporal scales, there will usually
be little change.
These issues are particularly noteworthy for this paper. For years, downtown
Los Angeles has been characterized in part by a sizable homeless population
(Berk et al., 2008) concentrated in an area commonly referred to as
\Skid Row” (Magnano and Blasi, 2007). Crime and disorder have been a
stable feature of Skid Row life. Open-air drug markets, prostitution, nightly
robberies, drug overdoses, theft, and vandalism have been a common. Local
merchants have long lobbied for a tougher response from the LAPD (see
Harcourt, 2005 for some history). In the fall of 2005, they got their wish.
The majority of Los Angeles City services for the homeless are located
2
in downtown Los Angeles. These providers and other stakeholders speak
with many dierent voices about the needs of the homeless and how best to
respond (Koegel et al., 1988; Wenzel et al., 2000). There are also, not surprisingly,
some basic disagreements and battles over turf. But no one would
argue that a crack down on the homeless could be more than a temporary and
partial measure, and some complain that law enforcement resources might be
better spent providing additional homeless services (Blasi, 2007). As important
as they debates may be, they neglect three logically prior key questions.
Did the LAPD interventions intended to reduce the crime associated with
the Skid Row encampments really reduce crime? If they did, how large were
the reductions and were they true reductions or merely displacements? In
this paper, we attempt to provide answers.
2 Background
That crime and place are closely linked is indisputable. Over a century of
empirical research in the United States and Europe has demonstrated that
crime is geographically concentrated (seeWeisburd, Bruinsma, and Bernasco,
2009 for an historical overview). Theoretical explanations at various spatial
levels have followed (Sampson, 1995; Taylor, 2001; Welsh and Hoshi, 2002).
Practical applications of this knowledge have also been developed. Perhaps
most notable is a focus of police resources on crime hot spots or areas of
particularly high crime concentrations (Sherman et al., 1989). While police
actions directed at specic persons or crimes have produced mixed-results
(Sherman, 1990), a number of studies point to consistent eects from spacebased
police interventions (see Braga, 2001; Weisburd and Eck, 2004 for
reviews).
A common criticism for place-based policing strategies is that they are
myopic. The underlying causes of crime are not addressed, although several
notable policing scholars have argued that the police can in principle aect
systemic issues under certain circumstances (Eck and Spellman, 1987; Goldstein,
1990). Perhaps more to the point, reductions in crime are a desirable
goal and one for which police are well suited. Also, there is nothing about
police initiated attempts to reduce crime that necessarily preclude more fundamental
interventions by other agencies. In short, there is little that the
police can do to \solve” the problem of homelessness. But perhaps welldesigned
policing strategies can reduce some of its undesirable consequences.
3
Whether in this case the resources allocated to the police might be better
used to address root problems (National Law Center on Homelessness and
Poverty, 2009) is an important issue, but not one that can be appropriately
addressed here.
There is a marked increase in victimization when individuals nd themselves
in settings where excessive drinking of alcohol, drug use, and other
risky behaviors are prevalent (Felson, 2002). It is not surprising, therefore,
that homeless individuals have signicantly higher rates of criminal victimizations
than individuals who have a place to live (Koegel et al., 1988; Kushel,
Evan, et al., 2003). It is also not surprising that homeless encampments can
be especially problematic for the homeless and others because there is a concentration
of prospective perpetrators and victims (Meithe and Meyer, 1990;
Wenzel et al., 2000; Sampson and Lauritsen, 1990). A key implication for
crime that is probably more important than the number of homeless in a
jurisdiction is their spatial density.
3 The Safer Cities Initiative
Los Angeles (LA) County has the largest number of homeless individuals of
any county in the United States (Berk et al., 2008). Consistent with historical
trends (Rossi, 1989), the population of homeless is very heterogeneous.
According to LA Almanac, (2009) the County homeless population:
1. has an average age of about 40;
2. nearly 50% with a high school education;
3. is at least 33% female;
4. is at least 20% composed of families;
5. has about 20% with disabilities;
6. has at least 16% employed;
7. has about 25% mentally ill; and
8. has at least 33% with substance abuse problems.
4
A large fraction of the LA County’s homeless can be found in the City of
Los Angeles, with the highest concentration by far on Skid Row. It is likely
that the Skid Row population has many features in common with the County
population, with perhaps a greater density of disadvantage. Crime problems
that go well beyond mere nuisance are rampant. Open-air drug markets,
prostitution, robbery gangs, theft, and vandalism are common. Until recently,
these Skid Row conditions went largely unaddressed.
Starting in 2004, media and public policy attention began to highlight
the conditions surrounding homelessness in downtown LA’s Skid Row (Los
Angeles Times, 2009). Notable stories in the Los Angeles Times described
dirty needles littering the streets, prostitution conducted in public portapotties
placed on Skid Row, robbery gangs targeting homeless individuals for
their disability checks, drug-related overdoses, and frequent violence (Lopez,
2005). Local merchants and private developers were particularly vocal about
the these and a variety of related problems (Harcourt, 2005).
In September 2005, the Los Angeles Police Department pilot tested an
eort to clean up the area. The intervention was ocially named the \Safer
Cities Initiative” (SCI) and was a hallmark of Chief William Bratton’s \broken
windows” approach (Bratton, 1989; Wilson and Kelling, 1982) targeting
well-dened dened geographical locations. Under the direction of Captain
Jodi Wakeeld, a pilot program called the \Main Street Project” was intended
to reduce the density of homeless encampments through nes and
citations. Encampments located in the \historic core” section downtown LA
(S. Main Street between 4th and 7th streets) were deemed to be a public
health nuisance and sanctions were authorized under LA city statute 1
The LAPD also cracked down on crimes like public intoxication, drug
use, and prostitution that made the area congenial for criminal predators.
Starting in October, the LAPD placed 4 to 5 ocers on foot in the historic
core section of downtown, who were to focus exclusively on general nuisance
crimes and basic order maintenance. There followed a further increase in
police presence with the introduction of one of the LAPD’s mobile police
command stations, commonly referred to a \Big Blue.” In addition, undercover
vice teams were placed in the areas known for open air drug markets
1The LA municipal code notes, \No person shall sit, lie or sleep in or upon any street,
sidewalk or other public way.” (L.A., CAL., MUN. CODE 41.18(d). The use of this
municipal code to explicitly arrest homeless persons was deemed a violation of the 8th
amendment by the 9th Circuit Court of Appeals (Jones v. City of Los Angeles, 444 F.3d
1118, 1120 (9th Cir. 2006)
5
and for prostitution. Finally, a special undercover squad focused on local
robberies (Wakeeld, 2009).
After the pilot phase was implemented largely as designed and with anecdotal
evidence of success, the LAPD ocially launched the full-scale version
of the SCI on September 17th, 2006 by placing 50 full-time ocers on the
street in downtown LA. Just as in the Main Street Project, the ocers were
deployed only to the historic core of downtown LA. The ocers worked eastward
through the Skid Row section, breaking up homeless encampments,
issuing citations and making arrests for violations of the law. The plan was
to clear out specic street areas, maintain a visible police presence for at
least a week, and then move onto other parts of downtown. The dedicated
ocers also worked closely with two vice units (Narcotics Buy Team and
Field Enforcement Section).
The immediate goals of the SCI were demonstrably achieved. The Skid
Row homeless encampments were cleared. The concentration of homeless
individuals was dispersed. The debris they left behind was removed. But
what about crime in the downtown LA? Was there a reduction? According
to LAPD internal documents and media reports (see Los Angeles Police,
2008a,b), homeless-related drug overdoses, murders, and reported crimes
dropped the year after the intervention.
However, the program was not independently and rigorously evaluated,
and there were many skeptics (Blasi, 2007). In this paper, we report the
results of an independent, empirical evaluation. We consider the possible
crime reduction impact of the Main Street Project, which served as the pilot,
and of the SCI. Three broad kinds of crime are examined: (1) nuisance crime,
(2) violent crime, and (3) property crime. We also consider possible eects
on four areas adjacent to downtown.
4 Research Methods
4.1 Research Design
The unit of observation and analysis for this study is the Police Division.
The Los Angeles Police Department is organized into four Police Bureaus and
within them, nineteen divisions. The LAPD intervention was implemented
in the Central Division, which is part of the Central Bureau. The Central
Division contains much of the downtown area, including large oce and city
6
government buildings, their support services, light industry and a mix of
residential housing. The Central Division also contains several square blocks
of shops patronized by people of modest means living near the downtown.
And the division contains the LA’s Skid Row. The Central Division is the
experimental unit.
As shown in Figure 1, the Central Division is bordered by four other
Central Bureau Police Divisions: Northeast, Rampart, Hollenbeck and Newton.
These divisions contain a mix of moderate to low income households
and a mix of industrial and commercial establishments. The four divisions
contiguous to the Central will serve as the comparisons units because of
their proximity to the downtown, their broad similarities with many important
spatial and demographic features with the Central Division, and their
shared command structure under the Central Bureau.
For each of the divisions, we have three time series of crime counts by
week: the number of violent crimes, the number of property crimes, and the
number of nuisance crimes. Nuisance crimes often are associated with the
homeless.2 We consider dierent kinds of crimes because although common
lore has the homeless as perpetrators of a wide variety of nuisance crimes,
they can also be perpetrators and victims of very serious crimes. An important
question, therefore, is whether all crimes can be aected by the interventions
or just nuisance crimes. Although the immediate goal of dispersing
the homeless was achieved, the homeless are hardly the only crime victims
and perpetrators in downtown Los Angeles. Even if homeless-related crimes
are aected, other factors may dominate any local trends for certain kinds
of crimes.
The three time series for the Central Division serve as three outcome
variables for the experimental group. The three time series for each of the
four contiguous divisions serve as three \non-equivalent no-treatment control
group time series” in a quasi-experimental design (Shadish et al., 2002:
181-184). Crime patterns over time that the control divisions share with the
experimental division should not be a direct result of interventions that were
introduced only in the experimental division. But if ignored, they risk being
confounded with any eects of the interventions. In principle, the confound-
2Violent crime included aggravated assault (57.7%), homicide (1.4%), rape (2.3%),
robbery (38.6%). Property crimes included burglary (12.8%), theft from vehicles (25.3%),
attempted burglary (25.7%), grant theft auto (21.2 %), and larceny theft (14.5 %). Nui-
sance crime included disturbance (0.8 %), lewd conduct (1.3 %), petty theft (40.4 %),
pickpocketing (1.8 %), trespass (4.6 %), and vandalism (51.1 %).
7
Figure 1: Spatial Organization of the LAPD and Central Bureau Divisions
8
ing can be removed by regression-based covariance adjustments. Precisely
how we will do this is discussed shortly.
There are 419 weeks of data starting on January 1st of 2000 and ending
on December 31st of 2007. The rst and last weeks are dropped because they
were not a full seven days. The timing of the LAPD Main Street Project for
Skid Row and the SCI was determined through several conversations with
the LAPD captain in charge of both, who in turn consulted sta activity logs
(Wakeeld, 2009).
The formal start of the SCI was easy to document. It began in week
351 and continued through the end of 2007. The start of the pilot program
was far more ambiguous. On September 27, 2005 the Main Street Project
ocially began (week 300). Skid Row porta-potties were removed beginning
on October 26 (week 304). On November 16, 2005 (week 307), the LAPD
added 43 recruit ocers to walk beats en mass in Skid Row. However, several
weeks before the ocial announcement, the proposed pilot program was being
widely discussed by the media. Moreover, a variety of stakeholders were
already involved so that by the middle of August 2005 it was widely known
that a police initiative to clear Skid Row of the homeless was immanent. It
may well make sense, therefore, to make week 294 (or so) the starting date
for the pilot program. Week 294 is six weeks before the Main Street Project
began ocially.
4.2 The Statistical Model
Because we do not know the functions by which the crime time series for
the comparison divisions may be related to crime time series for the experimental
division, we apply the generalized additive model. The generalized
additive model (GAM) allows functional forms for the relationship between
quantitative regressors and the response to be inductively determined from
the data (Hastie and Tibshirani, 1990).3 The generalized additive model in
this application can take the following form separately for each of three crime
categories.
yc;t = e[c;0+c;1I1;t+c;2I2;t+
P4
d=1 fc;d(xc;d;t)] + “c;t; (1)
where
c is an index for one of the three kinds of crime;
3There are no functional form issues for the categorical predictors because they are not
quantitative. However, transformations of the response are on the table.
9
t is an index for week;
d is an index for one of the four comparison divisions,
yc;t is the response crime count by week for one of the three kinds of
crime,
I1;t an indicator variable coded as \1″ for the period in which the Main
Street Project was in place, and \0″ otherwise;
I2;t an indicator variable coded as \1″ for the period in which the Safer
Cities Initiative was in place, and \0” otherwise;
fc;d(xc;d;t) is an inductively generated relationship between the crime
time series c for comparison division d and the response crime series c;
and
“c;t is a random Poisson disturbance meeting the usual regression assumptions.
The parameter c;0 is the usual intercept for crime type c. The parameter
c;1 will capture the direction and size of any average shift up or down in
yc;t associated with the introduction of the Main Street Project. As such,
it can provide an estimate of the pilot program’s treatment eect for crime
type c. The parameter c;2 will capture the direction and size of any average
shift up or down in yc;t associated with the introduction of the SCI. As such,
it can provide an estimate of the SCI’s treatment eect for crime type c.
More complicated treatment functions can be formulated and if necessary,
time-related patterns that are unique to the Central Division, but not associated
with the intervention, can be removed through additional terms in
equation 1. The systematic part of equation 1 is exponentiated consistent
with the conventional log-link function for Poisson regression (Hastie and
Tibsharini, 1990: 139).
There can be no regression coecients associated with the functions of
quantitative covariates. Any information in a single regression coecient
would be automatically incorporated in the inductively constructed functional
form.4 Moreover, for nonlinear functions, there is not one regression
coecient but many; indeed a limitless number under many circumstances.
The derivative of the function at each predictor value is a slope.
4Formally speaking, they are not identied.
10
In practice, the inductive functions are likely to be rather smooth. Consequently,
the covariance adjustments will control for relatively smooth temporal
patterns in crime that the experimental division shares with the comparison
divisions. Sharp discontinuities are usually not well captured or can
be missed altogether. We will return to this issue later.
Key elements of equation 1, as in any regression model, are the properties
of “t. In particular, there is here the real possibility of dependence within
the disturbances of the response time series. Such dependence can undermine
statistical inference. Equation 1 assumes implicitly that such dependence is
removed by covariance adjustments using the four comparison time series.
Insofar as the temporal dependence for the crime counts in any of the comparison
divisions is similar to the temporal dependence for the crime counts
in the experimental time series, it will be eliminated. But the possibility of
any dependence remaining is an empirical matter we will address below.
4.3 Estimation Procedures
Obtaining estimates of the regression parameters of equation 1 while also
obtaining estimates of the functional forms for the covariates complicates
matters. In the same spirit as the generalized linear model, a form of iteratively
reweighted least squares is used. However, the usual regression sum of
squares is \penalized” (Wood, 2008: 500).5 A penalty is introduced so that
an appropriate degree is smoothing is achieved for the functional forms to be
estimated. The intent is to balance the bias that comes from not smoothing
enough against the variance that comes from smoothing too much. The
more weight given to the penalty, the smoother the function. The less weight
given to the penalty, the rougher the function. The weight arrived at is usually
determined by some measure of out-of-sample performance, such as the
cross-validation statistic.
5The function to be minimized is jjW(z?X)jj2+
P
j jTSj, whereWis a diagonal
weight matrix as usual, z is the adjusted response for the given iteration, X is a matrix of
regressors including the design matrix for the splines associated with each of the smoothing
functions, is a vector of coecients whose values are to be estimated, j is a weight
given to the penalty function for the jth function that is also to be estimated, and Sj is
a matrix of constants representing the basis functions. The term on the left is just the
usual weighted sum of squares. The term on the right imposes a penalty that increases
as the functions to be estimated become more complex. It is the second term that makes
estimation dierent from iteratively reweighted least squares applied to the generalized
linear model.
11
To summarize, separately for each crime type c we are seeking the values
of c;0 through c;2, and the four functions of crime in the comparison
divisions that iteratively minimize a penalized sum of squares. A formal
discussion penalized regression splines can be found in Green and Silverman
(1994), in Wood (2006), and in Hastie and his colleagues (2009: Section
5.4). The GAM implementation we used is discussed in Wood (2008). Berk
(2008: Chapter 2) provides a very accessible introduction to penalized tting
functions.
5 Results
The analysis was undertaken in several steps. These steps are presented in
some detail for the nuisance crime outcome to help make our methods more
transparent. We proceed much more rapidly through the results for violent
crimes and property crimes.
Figure 2 shows the three crime time series for the Central Division. A
vertical line for the start of each intervention is overlaid. It is clear that all
three time series varied a lot over the study period. Property crime (in green)
ranged from a low of 21 crimes in a week to a high 181 crimes in a week. There
is a very large drop in all three crimes about the time when the (pilot) Main
Street Program began and a much smaller downward shift about the time
the SCI was introduced. The three time series for the Central Division have
broadly similar temporal patterns with between series correlations ranging
from 0.50 to 0.59.
One might be tempted to conclude that the Main Street Project and
the SCI had important eects on all the three time crime types. In other
work (Berk and MacDonald, 2009), however, we have found some patterns
citywide that are also evident in the four comparison divisions. For example,
Figure 3 shows the comparable plot for the Hollenbeck Division. Note that
the large increase around week 200 and the large decrease around week 300
corresponds substantially to the dramatic rise and subsequent fall in Figure 2.
5.1 Results for Nuisance Crime
Figure 4 plots the nuisance crime time series by week and overlays a lowess
smoother. There is again a dramatic increase several months before any of
the interventions are in place followed by a signicant drop in crime associ-
12
Crime Multiple Times Series For The Central Division
Week
Crime Count
0 100 200 300 400
50 100 150
Safer Cities
Main Street Project
Figure 2: Crime Counts for The Central Division: Green = Property Crime,
Black = Nuisance Crime, Red = Violent Crime
Crime Multiple Times Series For The Hollenbeck Division
Week
Crime Count
0 100 200 300 400
0 50 100 150 200 250 300
Safer Cities
Main Street Project
Figure 3: Crime Counts for the Hollenbeck Division: Green = Property
Crime, Black = Nuisance Crime, Red = Violent Crime
13
ated with the Main Street Project and and perhaps a small drop in crime
associated with the SCI. However, as already suggested, much of the variation
over time is shared with the four adjacent police divisions and even the
city overall. Were that variation removed, the patterns might dier substantially.
For the requisite covariance adjustments, we turn to the generalized
additive model. The nuisance crime counts by week for each of the four comparison
divisions serve as the covariates. The intervention variables are not
yet included.
0 100 200 300 400
20 40 60 80 100
Central District Nuisance Crime Over Time
Week
Central District Nuisance Crime Count
Main Street Project
Safer Cities
Figure 4: Nuisance Crime Counts for Central Division With No Covariacne
Adjustments And An Overlaid Lowess Smoother
Figure 5 shows for didactic purposes the residualized nuisance crime produced
by the model. Once again, a lowess smoother is overlaid. The difference
between Figure 5 and Figure 4 is striking. For example, the large
shift upwards around week 260 and the large shift downwards around week
300 are both gone. That pattern was shared with other police divisions in
the Central Bureau and has been removed. Visually at least, the covariance
adjustments appear to be eective, and there are suggestions of relatively
small intervention eects.
Yet, there is also some evidence for a gradual downward trend unique
to the Central Division. Were that not taken into account, the average of
14
0 100 200 300 400
-20 0 20 40 60
Residualized Central District Nuisance Crime Over Time
Week
Central District Nuisance Crime Count
Main Street Project
Safer Cities
Figure 5: Nuisance Crime Counts for Central Division After Covariance Adjustments
With An Overlaid Lowess Smoother
0 100 200 300 400
-20 0 20 40 60
Residualized Central District Nuisance Crime Over Time
Week
Central District Nuisance Crime Count
Main Street Project
Safer Cities
Figure 6: Nuisance Crime Counts for Central Division After Covariance Adjustments
Include Week With An Overlaid Lowess Smoother
15
the number of nuisance crimes before the two interventions would be higher
than the average number of crimes after the two interventions because of the
trend, not because of the intervention. Prudence dictates addressing that
time trend, which apparently is unique to the Central Division. Again for
didactic purposes, Figure 6 is the result when a counter for week is included
as an additional covariate whose functional relationship with the response
is, as before, determined empirically. It seems now that the time series is
very well behaved. Large scale trends, even highly nonlinear trends, that
the experimental Central Division shares with the four adjacent comparison
divisions have been removed. So has the overall downward trend that is
unique to the Central Division. We are now is a good position to consider
any treatment eects characterized by change in level.
We do this by applying the generalized additive model much as in equation
1. The residualizing process just described goes on the behind the scenes
so that covariance adjusted estimates of any treatment eects can be properly
obtained. The model includes two treatment indictor variables, and ve
covariates, one for the weekly totals for nuisance crimes in each of the four
control police divisions and one that is a counter for week. The t is constructed
from the contributions of all seven regressors, only two of which are
intended to capture any intervention eects. Overall, the t is quite good,
at least to the eye, and 64% of the deviance is account for by the model.
Figure 7 is a plot of the time series of the number of nuisance crimes in the
Central Division with the GAM tted values overlaid.
Table 1 contains the key parameter estimates. As noted earlier, a penalized
regression spline smoother (represented by an \s” in the table) was
used for each covariate as part of the tting process. Conditional Poisson
disturbances were assumed along with the canonical log-link function.
Consider rst the ve covariates. They are of little substantive interest.
Their role is to remove crime trends that the Central Division shares with
any of the four adjacent divisions and overall time trends that are unique
to the Central Division. Moreover, their relationships with the response are
very dicult to interpret because they are the result of several covariance
adjustments. How would one think about, for example, the relationship between
the number of nuisance crimes in the Central Division and the number
of nuisance crimes in the Hollenbeck Division with the crime trends in the
other three comparison divisions held constant?
Rather, the issues are methodological. All but one have eective degrees
16
0 100 200 300 400
20 40 60 80 100
Central District Nuisance Crime Analysis
Week
Central District Nuisance Crime Count
Main Street Project
Safer Cities
Figure 7: Nuisance Crime Counts for Central Division With Fitted Values
Overlaid
Variable Estimate EDF P-Value
Constant 3.74 1.0 >.001
Main Street Pilot -0.27 1.0 .002
Safer Cities -0.36 1.0 .004
s(Week) | 6.5 >.001
s(Hollenbeck) | 4.0 0.159
s(NorthEast) | 2.2 .002
s(Newton) | 1.0 0.089
s(Rampart) | 7.9 .014
Table 1: GAM Resuls for Nuisance Crime in the Central Division: Log link
and Poisson Disturbances (N = 417, 64% of the Deviance Accounted For)
17
of freedom (EDF) greater than 1.0.6 This indicates that all of the partial
response functions except for the Newton Division are nonlinear, some dramatically
so.7 Had a conventional parametric form of regression been used,
such as Poisson regression, the assumed functions for the covariates would
likely be very wrong. Incomplete covariance adjustments would have followed
along with badly biased estimates of any treatment eects.
As an example, consider the partial response function for the number of
nuisance crimes in the Rampart Division, which is shown Figure 8. The solid
line is the estimated function, and the dotted lines are 95% error bands.8
There is a rug plot at the base of the the gure to show roughly how the
regressor is distributed. The vertical axis metric is that of the tted values in
centered log units. The label indicates that a smoother has been applied to
the Rampart covariate and that the function uses up 7.9 degrees of freedom.
Taking the error bands in account, Figure 8 shows that for the Rampart
Division the partial response function is approximately at until about 60
nuisance crimes per week. Between 60 and nearly 100 crimes per week, the
function is increasing. There are so few weeks with more than 100 nuisance
crimes that, as the very wide error band indicates, the apparent decline
should not be taken seriously.
Figure 9 shows the partial response function for week, which captures any
overall time trends in the Central Division with temporal crime patterns in
the comparison divisions held constant. The trend is down or at, except
for the period between about week 250 and week 320. The sharp downward
trend beginning around the time that the pilot study was fully in place may
actually represent a treatment eect. An upward trend in nuisance crimes
was reversed. However, our model assumes that any treatment eects are
changes in level, not slope, and we hesitate to respecify after looking at the
results. That would be data snooping (Freedman, 2005: Section 4.9). Should
the change in slope be a real treatment eect, the likely consequence for our
6Eective degrees of freedom (EDF) is a generalization of the usual degrees of freedom
concept. Loosely speaking, the EDF indicates how many degrees of freedom are being
\used up” by the estimated function. The EDF does not have to be a whole number.
7When the EDF is 1.0, the functional form is linear.
8The error bands are constructed point by point. They are an attempted to represent
approximately the interval in which 95% of the tted values would fall were it possible to
repeat the study, independent of all other studies, a very large number of times. It is not
a 95% condence interval because there is likely to be some unknown amount of bias in
the tted values.
18
20 40 60 80 100
-0.4 -0.2 0.0 0.2 0.4
Partial Response Plot for Rampart Division
Number of Nuisance Crimes
s(Rampart,7.9)
Figure 8: Partial Response Function for Nuisance Crimes in the Rampart
Division
0 100 200 300 400
-0.4 -0.2 0.0 0.2 0.4
Partial Response Plot for Week
Week
s(Week,6.51)
Figure 9: Partial Response Function for Week
19
model is to underestimate any benecial eects for the two interventions.
The top three rows of Table 1 contain results for the constant and the
two treatment indicators. The parameter estimates for the interventions can
be interpreted just as one would for Poisson regression within the generalized
linear model. The Main Street Project regression coecient of -.27 translates
into a count multiplier of .76. With the introduction of the pilot program, the
number of nuisance crimes in the Central Division is on the average about
75% of what it had been before the pilot program was introduced. The
associated p-value is .002. The SCI regression coecient of -.36 translates
into a count multiplier of .70. With the introduction of SCI, the number
of nuisance crimes is on the average about 70% of what it had been before
either of the interventions had been introduced. The associated p-value is
.004.
Recall that the starting date for the Main Street Project was dicult to
precisely dene and perhaps could have been moved several weeks in either
direction. Not surprisingly, the estimated eects of both programs depend
on the starting date for Main Street Project. As the starting date is moved
toward the starting date for the Safer Cities Initiative (SCI), the variance of
the Main Street Project indicator is necessarily reduced. A reduction in a
regressor’s variance can introduce greater instability into any treatment eect
estimates and reduces statistical power. Moreover, because in this case all
of the regressors are correlated, changing one estimate aects all estimates.
Suce it to say, moving the starting date of the Main Street Project a week or
two in either direction makes no important dierence. This is good because
our chosen start date is probably accurate within plus or minus a week or
two. But moving the starting date several weeks forward or back can change
the story. Indeed, moving the starting date for the Main Street Project six
weeks forward in time can essentially remove any nuisance crime treatment
eects for either intervention. But in so doing one would have to assume that
the Main Street Project was sprung on the Central Division with no advance
warning whatsoever and without any prior media coverage and related public
controversy. Facts to the contrary are readily available.
Interpreting the treatment eect p-values also requires some caution. As
noted earlier, statistical inference for GAM can be dicult to justify because
the model is arrived at inductively (Leeb and Potscher, 2005; 2006; 2008;
Berk et al., 2009).9 However, the p-values for the two interventions are so
9There is also a bit of dependence in the disturbances. The estimate correlation at a
20
small that even if they biased downward by a factor of 10, one would still
reject the conventional null hypothesis.10 For these, the conclusions from the
statistical tests are perhaps sound. Moreover, one gets the eectively the
same results assuming a normal generalized additive model.11 The analyses
to which we now turn provide futher support for our procedures.
5.2 Results for Violent Crime
Having gone through the analysis of nuisance crimes in considerable depth,
we can move very quickly through the analyses for violent crime and property
crime. Applying the same steps and same analysis procedures as used for
nuisance crimes, Table 2 shows the results for violent crimes. All of the
preliminary and intermediate results anticipating Table 2 broadly replicate
the preliminary and intermediate steps for nuisance crime. And as before,
the story is to be found in the regression coecients for the Main Street
Project and the SCI.
Variable Estimate DF or EOF P-Value
Constant 3.70 1.0 >.001
Main Street Pilot -0.17 1.0 .023
Safer Cities -0.50 1.0 >.001
s(Week) | 5.2 >.001
s(Hollenbeck) | 5.4 >.001
s(NorthEast) | 6.9 >.001
s(Newton) | 1.7 0.102
s(Rampart) | 2.6 0.227
Table 2: GAM Results for Violent Crime in the Central Division: Log link
and Poisson Disturbances (N = 417, 68% of the Deviance Accounted For)
The average treatment eect estimates of roughly the same as found for
nuisance crime. The Main Street Pilot regression coecients of -.17 translates
lag of one week is .06. This is small by most any standard and will not have much impact
on statistical tests.
10For the two interventions, a one tailed test is used.
11For large counts as we have here, a conditional Poisson distribution and a conditional
normal distribution are much the same.
21
into a count multiplier of .84. With the introduction of the pilot program, the
number of violent crimes in the Central Division is on the average about 84%
of what it had been before the pilot program was introduced. The associated
p-value is .02. The Safer Cities Initiative (SCI) regression coecients of -.50
translates into a count multiplier of of .61. With the introduction of SCI,
the number of violent crimes is on the average about 61% of what it had
been before either of the interventions had been introduced. The associated
p-value is substantially less than .001. However, the same cautions noted for
nuisance crimes still apply.12
5.3 Results for Property Crime
Table 3 shows the results for property crime. Again, all of the preliminary
and intermediate results anticipating Table 3 broadly replicate the preliminary
and intermediate steps for property crimes. The Main Street Project
regression coecients of -.25. The count multiplier is .78. With the introduction
of the pilot program, the number of property crimes in the Central
Division is on the average about 78% of what it had been before the pilot
program was introduced. The associated p-value is .005. The SCI regression
coecients is -.43, which translates into a count multiplier of .65. With the
introduction of SCI, the number of property crimes is on the average about
65% of what it had been before either of the interventions had been introduced.
The associated p-value is less than .001. In short, for all three kinds
of crime the estimated treatment eects are rather similar.
5.4 Spillover Spatial Eects
Finding treatment eects in the Central Division leaves unaddressed any
impact the Main Street Project and the SCI may have had on the four
adjacent police divisions. There are three clear possibilities. First, there
may have been no impact whatsoever outside of the Central Division.
Second, there may have been crime displacement manifested by crime increases
in the four adjacent police divisions about the same time the LAPD
intervened in the Central Division. Before looking at the data, we were
somewhat skeptical of the displacement hypothesis because of our view that
12The estimate correlation between the residuals a week apart was less than .10 and far
to small to be of any real concern.
22
Variable Estimate DF or EOF P-Value
Constant 4.57 1.0 >.001
Main Street Pilot -0.25 1.0 .005
Safer Cities -0.43 1.0 .001
s(Week) | 8.4 > .001
s(Hollenbeck) | 1.0 .222
s(NorthEast) | 8.0 >.001
s(Newton) | 6.8 >.01
s(Rampart) | 6.1 .319
Table 3: GAM Results for Property Crime in the Central Division: Log link
and Poisson Disturbances (N = 417, 81% of the Deviance Accounted For)
crime associated with homelessness depended substantially on homeless density,
and there were apparently no new, large homeless encampments in the
comparison divisions after the interventions. There was certainly nothing on
the scale of Skid Row.
Third, the interventions in the Central Division may have had spillover
eects manifested by crime reductions in the four adjacent police divisions
about the same time as the LAPD intervened in the Central Division. Although
there were no large scale encampments on the adjacent divisions,
there were areas where homeless individuals tended to congregate. Perhaps
these would areas would be aected. Before looking at the data, spillover
eects seemed like a real possibility because word of the police crackdown
would spread quickly among the homeless and related stakeholders. In addition,
police ocers in the comparison divisions might have been empowered
and motivated to aggressively intervene with their own homeless populations.
To address these three possibilities, we proceeded much as before. The
response variables were the three kind of crime counts in each the four comparison
divisions. The same two intervention variables were dened. Week
and the corresponding crime count in the Central Division were the covariates.
We essentially reversed the logic of the previous analyses. A total of
twelve analyses followed (three kinds of crime by four comparison divisions).
In general, the same sort of intervention eects were found. There was absolutely
no evidence for crime displacement. There was evidence for spillover
eects. As a summary, Table 4 uses the sum of the three kinds of crime in
23
Variable Estimate DF or EOF P-Value
Constant 6.91 1.0 >.001
Main Street Pilot -0.26 1.0 >.001
Safer Cities -0.23 1.0 >.001
s(Week) | 8.62 > .001
s(Central Total Crime) | 8.51 > .001
Table 4: GAM Results For Total Crime For The Comparison Divisions Using
Total Crime in the Central Division and Week as Regressors: Log link and
Poisson Disturbances (N = 417, 87% of the Deviance Accounted For)
the four comparison divisions as the response variable. The sum of the three
kinds of crime for the Central Division and week are to two regressors. The
two treatment eect estimates imply multipliers of about .75, about what we
found previously.
There were no Main Street Project of SCI interventions in any other
division but the Central. According to the LAPD, there were also no other
law enforcement interventions in those divisions that could be confounded
with what was going on in the Central. Nor were there apparently any other
sorts of interventions in the four comparison divisions starting around week
300. It is important to stress that for the crime reductions in the comparison
divisions to be other than indirect spillover eects from the Central Division,
the timing would have to be very similar. There would need to be a sharp
beginning to any confounded intervention around week 300.
6 Discussion
We have shown that broad time trends in certain crimes that the Central
Division shares with its immediate neighbors can be eectively removed. It is
then possible in principle to isolate processes unique to the Central Division.
This is at least a good start. It follows that treatment eect estimates are
consistent with meaningful but modest crime reductions for both the Main
Street Project and the SCI for nuisance, violent, and property crimes.
Consider rst the overall eects in the Central Division. Everything depends
on the quality of the covariance adjustments that GAM implements.
Highly nonlinear relationships with the response variable can be eectively
24
taken into account even when they are unknown before the data are examined.
Therefore, the key vulnerability is that there are one or more confounders
having highly local temporal impacts on the amount of crime in
the Central Division and that the local eects just happen to materialize
around the time when both police initiatives were introduced. For example,
had the City of Los Angeles instituted signicant new ways to provide for
the homeless at the same time that the police were trying to disperse them,
it would be dicult to determine which interventions, if any, were reducing
crime. Under such circumstances, the bias in our treatment eect estimates
could be positive or negative. We might attribute to either of the interventions
crime reduction eects that were not real or fail to nd intervention
eects that were. We know of no such confounders, nor do our informants
within the LAPD. However, we cannot rule them out categorically. What
is clear is that crime in the Central Division dropped very soon after both
police interventions were introduced.
The apparent spillover eects complicate matters. One interpretation
is that the Main Street Project and the SCI implemented in the Central
Division led to more widespread but unocial changes in police practices. It
is also plausible that media and word-of-mouth descriptions of events in the
Central Division dispersed collections of homeless individuals in at least the
adjacent police divisions. These are true spillover eects that one might call
indirect.
Another interpretation has already been anticipated. In all ve police
divisions in the Central Bureau or in the comparison divisions alone, one or
more other interventions may have been introduced coinciding in time with
the Main Street Project and the SCI. It is important to emphasize that such
interventions would need to be be sharply implemented around the same
time as the two known police actions. The impact of any interventions with
more gradual eects over longer time spans would likely be absorbed in the
covariance adjustments. Again, we can nd no evidence of interventions that
the covariance adjustment would not be able to handle.
Some many nd the treatment eects unsurprising. The large concentration
of homeless individuals in downtown Los Angeles, and especially on Skid
Row, contained a substantial pool of potential crime victims who were easy
targets. Likewise, there was a substantial pool potential crime perpetrators.
The area was also a magnate for individuals with lawless inclinations. Dispersing
the homeless population would seem on its face an obvious way to
reduce crime in downtown Los Angeles. As already noted, more important
25
for crime than the number of homeless in a city may be how much they are
spatially concentrated. Areas characterized by a high density of homeless
people may overwhelm local resourses. It is one thing, for example, for a
shopkeeper to ask a single homeless person not to camp right in front of the
entrance to his or her establishment, and quite another to ask that of several
individuals at once many times over the course of a working day. In the rst
case, there may be little disruption of customer trac. In the second case,
the disruption could be substantial. Therefore, one can view the two LAPD
interventions primarily as a way to reduce the spatial density homeless individuals,
and one could have walked through Skid Row soon after the police
intervened and plainly seen that the homeless encampment was no more.
Unaddressed, however, is whether it is good public policy more generally
to have police break up concentrations of homeless individuals living on the
streets. In our view, the wisdom of such police action depends on what
services are made available for the homeless. There might be little objection
to aggressive policing in the short term, for example, if there were a sucient
number of adequate shelters where homeless individuals might safely eat and
sleep. Getting the homeless o the streets and into shelters is probably a
sensible stop-gap approach. In practice, however, there will often be too
few shelter beds. Then, the appropriate public policy will require dicult
tradeos. There is also the matter of costs. Police resources reallocated
to Skid Row are not available elsewhere. In addition, there are secondary
costs that come from processing homeless individuals who are arrested and
homeless individuals who are incarcerated. Far more is involved than the
wages of patrol ocers reassigned to Skid Row.
7 Conclusions
On balance, there is evidence in the Central Division for modest but meaningful
reductions across a wide range of crimes that can attributed to the
Main Street Project and the SCI. The analysis does not have the \gold standard”
internal validity of a randomized experiment, but our non-equivalent
no-treatment control group time series design has real merit, and we can
nd no alternative explanations for our main ndings. It also helps that
there is unequivocal evidence that the Skid Row homeless encampment was
completely cleared. A key intervening requirement for crime reduction was
demonstrable fullled.
26
The evidence for benecial spillover eects in the comparison divisions is
less compelling. The estimated eects are roughly comparable, but in contrast
to the interventions in the Central Division, we have no data whatsoever
on what the police may have done in the comparison divisions. We can only
speculate on what may have happened. Although our post hoc account has
some plausibility, it is no substitute for real measures of police practices in
the comparison divisions and of responses of the homeless across the entire
Central Bureau to interventions in Central Division.
At the same time, the usefulness of our study depends on the knowledge
base from which the Main Street Project and the SCI were implemented.
The scientic gold standard is not the only yardstick. Another yardstick is
the evidence about any treatment eects that would otherwise be available.
By that measure, our nding are certainly not denitive, but still a step
forward.
Still, there are some strong caveats to keep in mind. The modest crime
reductions can imply that much of the crime, perhaps even most crime, in the
Central Division was not driven by the Skid Row homeless encampment. If
the goal is crime reduction overall, dispersing the homeless is at best a start.
In addition, whether the ndings can be usefully generalized to other cities
with their own kinds of homeless problems is an empirical question. For
example, the interventions in the Central Division were premised on large
homeless encampments thought to be a key factor in local crime. Finally,
there is the matter of cost-eectiveness. Even if one only considers the costs
of the intervention, the arrests, the prosecutions, and the incarcerations, it is
not apparent that clearing the Skid Row homeless encampment was a good
use of police resources. It may have been, but that case remains to be made
in real economic terms.
Crime reduction is no doubt an important policy goal. The high victimization
rates for homeless individuals implies that the Main Street Project
and the SCI probably had some direct benets for the people formerly living
on Skid Row streets. There were also the benets for local shopkeepers, their
customers, and residents in dwellings near by. We emphasize again, however,
that police interventions of the sort undertaken by the Main Street Project
and the Safer Cities Initiative do not solve the problem of homelessness. At
best, they can only address one of its possible manifestations.
27
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You communicate with the writer and know about the progress of the paper. The client can ask the writer for drafts of the paper. The client can upload extra material and include additional instructions from the lecturer. Receive a paper.
The paper is sent to your email and uploaded to your personal account. You also get a plagiarism report attached to your paper.
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Read moreEach paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.
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