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Dogs and Crime: Reduced Rates of Property Crime in Homes with Dogs in Milwaukee, WI

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Abstract

Previous attempts to quantify the role that dogs play in mitigating household property crime rates have produced mixed results. GIS and spatial analysis methods to conduct such an investigation are not well-represented in the anthrozoological academic press for an entire city. This study seeks to address these gaps using a GIS-based case study of dog license locations and reported property crime locations for land parcel data in the City of Milwaukee, WI, for 2011. We found that parcels with reported licensed dogs experienced property crime at rates of between 1.40 and 1.71 percentage points lower than the property crime rates for parcels that were zoned the same and did not have licensed dogs. This association between the presence of dogs and reduced property crime suggests dogs have a deterrent effect on property crime; more comprehensive analysis is encouraged to draw more concrete conclusions.
   () -
©   , , | ./-
brill.com/soan
Dogs and Crime
Reduced Rates of Property Crime in Homes with Dogs in Milwaukee, 
Wes Grooms
Department of Urban and Public Afairs, University of Louisville, Kentucky
wes.grooms@louisville.edu
DJ Biddle
Department of Geography & Geosciences Center for ,  Center,
University of Louisville, Kentucky
Abstract
Previous attempts to quantify the role that dogs play in mitigating household property
crime rates have produced mixed results.  and spatial analysis methods to conduct
such an investigation are not well-represented in the anthrozoological academic press
for an entire city. This study seeks to address these gaps using a -based case study of
dog license locations and reported property crime locations for land parcel data in the
City of Milwaukee, , for 2011. We found that parcels with reported licensed dogs ex-
perienced property crime at rates of between 1.40 and 1.71 percentage points lower than
the property crime rates for parcels that were zoned the same and did not have licensed
dogs. This association between the presence of dogs and reduced property crime rates
suggests dogs have a deterrent efect on property crime; more comprehensive analysis
is encouraged to draw more concrete conclusions.
Keywords
dogs – property crime – property crime reduction – efect of dogs on crime
Introduction
Canines assume myriad roles in human societies including companion, protec-
tor, and rescuer. Without dispute, dogs have served to provide companionship
 ./- |   
   () -
and/or labor for many hundreds, if not thousands, of years (Anderson, 2008;
Coppinger & Schneider, 1995; Mithen, 1999; Morey, 2010; Podberscek, Paul, &
Serpell, 2000). Dogs are employed by police departments (Chapman, 1990;
Lanting, n.d.), border and travel control agencies (Levine, 2013), and armed
services organizations (Takara & Harrell, 2014; Morey, 2010; Anderson, 2008)
to provide protective services, and they provide companionship and valuable
services to individuals with disabilities (Beck, Seraydarian, & Hunter, 1986).
They are also viewed as being benecial to household security, whether that
perception is justied or not (Fielding & Plumridge, 2004).
Densely populated urban areas and their associated canine populations re-
sult in “exchanges between people and animals [being] central to the ongoing
ow of contemporary social life” (Sanders & Arluke, 2007, p. 66); yet the impres-
sive role that dogs and other nonhuman animals play in our society is “largely
taken for granted” (Arluke & Sanders, 1996, p. 1). As such, better understanding
of the specic efects dogs impart upon the urban environment could elucidate
the many potential benets of dogs in urbanized places (Urbanik & Morgan,
2013). This is truer than ever, as the rates of .. households with non-human
companion animals tripled to 47% between 1970 and 2012 (Humane Society,
2014; Podberscek et al., 2000), although some estimates suggest as many as 63%
of households have non-human companion animals (Anderson, 2008).
Conversely, dogs’ wellbeing is “absolutely and intimately linked to the
health and good standing” (Beirne & South, 2013, p. xiv) of—and the policies
and practices that inform their treatment in—urban environments. Benets
that might come from better understanding the mutually constitutive role that
dogs and the urban environment—including their human companions
(Benton, 2007, p. 15)—play in shaping the conditions of each include an en-
hanced societal understanding of dogs and their needs, the development of
more efective public policies regulating the presence and care of dogs, and
the identication of any crime prevention benets for urban residents who
reside with dogs.
It is the third benet of canines in urban settings—the provision of house-
hold crime reduction—that is the primary focus of this analysis. Specically,
we conducted an analysis to identify the relationship, if any, between licensed
dog location and reduced property crime rates.
A Geographic Information System () was used to conduct this analysis
to bring to bear a diferent methodological approach in seeking to understand
dogs’ role in reducing or preventing property crimes. The use of  allows
for hyper-local spatial analysis of data. Perkins, Wandersman, Rich, and Taylor
(1993) note, “Independent and systematic environmental assessment method-
ology is important for reasons of validity and reliability” (p. 32). Comparing
   |  ./-
   () -
specic parcels associated with addresses that incurred property crimes to
those specic parcels where dog licenses are registered allowed our analysis to
empirically answer a simple question: do dogs appear to have any deterrent ef-
fect on the rate of property crimes committed in the homes where they reside
as compared to the rate of property crime where there are no dogs?
The present study includes all residential addresses in the city of Milwaukee
that are zoned for single- or two-family use, applying the 2011 dog license data
and crime occurrence data. This study sought to identify the relationship, if
any, between the locations of licensed dogs and the locations of reported prop-
erty crimes for an entire city. We hypothesized there would be an inverse rela-
tionship between these variables.
Literature Review
Dogs are generally considered to have protective natures and other behav-
ioral characteristics—both naturally occurring and the result of domestica-
tion (Mithen, 1999)—that work to serve the needs of people (Coppinger &
Schneider, 1995). Dogs have been associated with reduced rates of crime in the
households where they reside and are used by police departments, the armed
services, colleges, and housing projects in the provision of protective services
(Chapman, 1990; Cromwell, Olsen, & Avary, 1991; Furton & Myers, 2001; Levine,
2013; Montoya, Junger, & Ongena, 2014; Takara & Harrell, 2014).
Indeed, police departments and community/neighborhood associations—
an example of each being the Rexburg, Idaho, Police Department, and the
Spokane, Washington, Community Oriented Policing (....)—explicitly
suggest that getting a dog can be an efective means of preventing property
crime. The Rexburg Police Department’s (2015) crime prevention tips website
states: “If you like animals, consider getting a dog. A dog has the ability to sense
family and friends from strangers that even the best security systems cannot.
In many cases the mere presence of a dog may discourage a burglar. A ‘Beware
of Dog’ sign or window sticker may help as well.” The Spokane .... (2015)
website on burglary prevention also suggests that the presence of dogs—
especially the noise they create when intruders stray into their territory—can
be an efective deterrent to crime. Further, Police Departments have asked
residents walking dogs to remain alert (serving as eyes on the street) to crime,
and to select their routes so they will pass through predicted crime “hot spot”
locations to prevent anticipated crime (Friedersdorf, 2014; Whittenberg, 2015).
Research results corroborate these assertions of dog perceptivity; dogs have
been found to be naturally more averse to angry human faces and to readily
 ./- |   
   () -
learn to tell, and remember, the diference between angry and happy human
faces or expressed emotions (Buttelmann & Tomasello, 2013; Müller, Schmitt,
Barber, & Huber, 2015). Dogs have also demonstrated an ability to discrimi-
nate between helpful (pleasant) and unhelpful (unpleasant) people based on
their witnessing of how these people treated their guardians, and are generally
keenly attuned to human emotions (Arluke & Sanders, 1996), and facial and
eye gestures (Anderson, 2008; Morey, 2010).
Cromwell et al. (1991) and Montoya et al. (2014), in their research into how
burglars choose targets and pursue ofenses, note burglary is most often an
opportunistic crime, suggesting that potential ofenders may nd a Beware of
Dog sign to be a sucient reason to target other residences. Further, Montoya
et al. (2014) cite research nding that occupancy (or guardianship)—and prox-
ies for occupancy, such as dog presence—“seem(s) to be a substantial deter-
rent since burgled houses are less likely to have dogs” (p. 6). In their own study,
Montoya et al. (2014) found only a tendency, and only at night, for the presence
of dogs to deter burglary. Mixed results were also obtained in a large telephone
survey study conducted with over 5,300 residents living in 600 blocks within
Seattle, , by Miethe (1991); homes with dogs had lower rates of burglary,
mixed rates of vandalism, and higher rates of property theft than homes with-
out dogs in the sample.
International studies also present conicting results regarding whether
the presence of dogs decreases or prevents crime. Fielding and Plumridge
(2004) found no signicant relationship between the presence of dogs in New
Providence, Bahamas, and reduced rates of break-ins, though pure-bred dogs
were associated with lower rates of crime than the local free-ranging mongrel;
the pure-bred dogs were kept indoors much more frequently, thus likely serv-
ing to deter more break-ins. The authors concluded that free-roaming dogs
provided no protective benet.
Logie, Wright, and Decker (1992) found that the presence of dogs prevent-
ed only opportunistic, non-professional burglars from breaking and entering
in Britain. Marzbali, Abdullah, Razak, and Tilaki (2012) conducted a mixed-
methods analysis in upper-middle class neighborhoods in Penang, Malaysia,
and determined that Access Control (which included locks, security systems,
physical barriers—and dogs kept specically to provide security) was the most
predictive of reduced rates of break-ins of all factors measured. This diver-
gence of results suggests further research—such as the research conducted
herein—is warranted.
Analyzing crime in a spatial context is not new, despite  only recently
rising in prominence. Canter (2000) indicates that the use of maps to under-
stand crime distribution can be traced back to at least 1833. Spatial evaluation
   |  ./-
   () -
of crime has occurred regularly since then, Canter (2000) tells us, by citing
many other researchers in the U.S. and Europe through the 1970s. This work
has contributed to a better understanding of community policing needs and
the built environment, the development of criminological theories, and pos-
tulations about criminal etiology. Social ecology and ofender travel patterns
have also been informed through geographical crime distribution analysis
(Canter, 2000).
Analysis of crime and spatial data also informs police managerial decisions,
notes Canter (2000), who informs us that crime data has been used since the
Berkeley, California, police department began using it for both short- and long-
term planning purposes. Crime data is now routinely used to assist in decision-
making on policy development and implementation, budgets, and enhancing
problem understanding and resolution, to name but a few uses (Canter, 2000).
This study uses 2011 crime data and dog licensure data for the City of
Milwaukee, , to conduct  and spatial analysis to identify whether an
inverse relationship exists between parcels with licensed dogs and locations
of property crimes throughout the entire city of Milwaukee. Dog license data
were only available for 2011. We are unaware of this type of study having been
conducted via  in any other research. This study contributes to the growing
body of narrowly focused discovery of “ner, more detailed attributes of the
environment of crime” (Perkins et al., 1993, p. 29). Our aim is to overcome
the mixed results of previous research to better elucidate the apparent efect,
if any, that dogs have on property crime.
Materials and Methods
Data
The data used to conduct this analysis were obtained from the .. Census
Bureau, the Milwaukee County Land Oce, the City of Milwaukee Police
Department, and the American Geographic Society Library at the University of
Wisconsin–Milwaukee. All data were downloaded in digital format from their
respective source websites. (s for all data sources are listed in Appendix A.)
The data les obtained for this study, and their sources, are listed in Appendix B.
Crime report data for calendar year 2011 were imported into the  ArcGIS
Desktop software package in tabular format. These data provide the street ad-
dress of each incident along with up to ve unique ofenses per reported in-
cident. For example, if the most serious ofense for an incident is aggravated
assault, but burglary/breaking and entering was also committed, both ofenses
(up to a total ve ofenses) are reported for that incident.
 ./- |   
   () -
Each of the ve ofense elds in the comprehensive crime report dataset
was queried to extract property crime ofenses only. Each resulting incident is
treated as a property crime whether the property crime ofense was listed as
the primary ofense or a subsequent ofense. Incident records that listed
more than one property crime were counted as a single property crime due
to our focus on the occurrence of property crime in general, not specic types
of property crimes. The data queries resulted in 14,604 incidents of property
crime in Milwaukee for 2011. Guidance in determining the coded ofense val-
ues that should be considered property crimes was provided during a conver-
sation between the rst author and a representative of the Milwaukee Police
Department Oce of Public Relations. (See Appendix C for the crime type
assessment matrix.)
The resulting property crime dataset was geocoded to the address level
using  Milwaukee’s Master Address Index () Geocoding Service, which
geolocates an address to a point in the centroid of the associated parcel (City
of Milwaukee, 2015). Geocoding the crime data resulted in a 95% success rate.
The remaining 5% of property crime records could not be accurately geo-
coded due to inconsistencies in address formatting and were excluded from
analysis.
Dog license records for 2011 were imported into  ArcGIS Desktop soft-
ware in tabular format. These data provide street addresses and other informa-
tion about the licensed non-human animal and his/her guardian, including
dog age and breed(s). This study does not consider the relationship between
crime rates and specic breeds, treating all dogs equally. Dog license records
were geocoded to the address level using  Milwaukee’s  Geocoding
Service, resulting in a 95% success rate. Again, the remaining 5% that could
not be geocoded were excluded from analysis. Some addresses were associated
with multiple dog license records. In these instances, only one point was gen-
erated, indicating the presence of at least one dog. As such, this study does not
consider the efects of multiple dogs versus single dogs.
Land parcel data were received in native  shapele format. Land par-
cel data describe the geographic boundaries of land parcels within the City
of Milwaukee and related attributes including but not limited to owner-
ship, tax assessment, land use, and zoning information. This study considers
only residential properties in its analysis. To this end, the residential parcels
were extracted by querying the parcels dataset on zoning for all residential
zoning codes as described in the City of Milwaukee Master Property File
Documentation (Table 295-107-2, p. 24). The  to access this document is
provided in Appendix A.
   |  ./-
   () -
Calculation of Property Crime Rates
The presence or absence of property crime and/or a licensed dog for residen-
tial parcels was evaluated by spatial intersection of the residential parcels data
with the geocoded point data for property crimes and dog licenses. Property
crime rates were then calculated for the total population of residential parcels;
residential parcels with licensed dogs; and residential parcels without licensed
dogs. Property crime rates were calculated as:
Property Crime Rate = × 100%
Additionally, the data were stratied by residential zoning type, and the afore-
mentioned property crime rates were calculated for the following zoning
types: residential properties zoned as single- and two-family only, and residen-
tial parcels zoned for single-family only. After obtaining property crime rates
as described above, the diference in the property crime rates of parcels with
licensed dogs and those without licensed dogs was calculated.
Validation and Signicance Tests
To evaluate the signicance of diferences between property crime rates for
parcels with and without licensed dogs, this study makes use of random per-
mutation testing, from a class of nonparametric statistical techniques called
resampling methods (Good, 2013). Resampling tests adhere to the following
general procedure: calculate a test statistic for the original data (in this case,
the test statistic is the diference in property crime rate between parcels with
licensed dogs and those without). Then, making the assumption that the dif-
ference in property crime rates are no diferent between properties with li-
censed dogs or without (essentially assuming that our population is just one
random sample), we randomly resample from the dataset without replace-
ment (randomly reassigning the dog location label among the data), and then
recalculate the test statistic. We then repeat this resampling and recalculation
of the test statistic n times, to create a sampling distribution.
We then calculate the percentage of records in the randomly assigned
distribution (i.e., the test statistic under the null hypothesis of no diference
between crime rates in dog parcels and non-dog parcels) that exceed the cal-
culated test statistic from the original data (Good, 2013). The rate at which the
observed value of the test statistic occurs or is exceeded in the sampling dis-
tribution approximates the p-value for that test statistic in a traditional one-
tailed t-test (Nichols & Holmes, 2002). For example, if the observed value for
# of parcels with property crime
# of total parcels
 ./- |   
   () -
the rate diference occurs or is exceeded 5% of the time in the sampling distri-
bution, the approximate p-value would be 0.05.
The source data for this analysis contain nearly 140,000 records and in ef-
fect approximate a census of the study area. In an efort to address concerns
around the use of very large datasets (Harford, 2014), this study employs the
resampling tests described above on randomly selected subsets of the total
population of parcel data. To create the subsets for both of the classes of resi-
dential parcels examined in this study (single- and two-family, and just sin-
gle-family), random samples containing 1,000 records with licensed dogs and
1,000 without licensed dogs were drawn with each permutation and submit-
ted to the random resampling test previously described; the sample size was
determined to be appropriate based upon both the size of the total popula-
tion of parcel data and the geographic extent of the data. This study conducts
resampling tests using 1,000 permutations on the subsamples described above
for each of the two classes of residential parcels—single- and two-family, and
single-family.
Results
Simply as a point of reference, our initial query identied the total number
of parcels zoned for residential use in the City of Milwaukee, and the total
number of residential parcels that incurred property crime during calendar
year 2011. This query resulted in the identication of 139,399 residential par-
cels, 11,097 of which had property crimes. Using these results, we calculated
a simple overall residential property crime rate—regardless of whether a dog
was known to be present or not—of 7.96% ((11,097 / 139,399) × 100% = 7.96%).
Each parcel zoned for multi-family use can contain three or more units. The
characteristics of multi-family buildings are problematic in our analysis be-
cause large, multi-unit buildings can thwart dogs’ ability to surveil the property
around the units they are in; a dog on the 4th oor of a building is unlikely to
play any role in preventing a property crime on a diferent oor, or perhaps even
on the other side of the same oor. For this reason, we excluded multi-unit res-
identially zoned parcels from further analysis. We retained parcels zoned for
two units because nearly all parcels zoned for two units will contain a single,
stand-alone structure in the form of a side-by-side or top-and-bottom duplex.
We then conducted a query of the total number of residential parcels zoned
for single- or two-family uses, a query for this same subset that had a licensed
dog associated with them, a query of this subset that had property crimes, and
   |  ./-
   () -
a query of the total number of single- and two-family residential parcels that
had both a licensed dog and a property crime associated with them.
Inluence of Dogs on Single- and Two-Family Parcels
We queried for total number of single- and two-family residential parcels
with dog licenses associated with them and the number of that same subset
that experienced property crimes. This resulted in the identication of 6,359
single- and two-family parcels with licensed dogs, of which 295 experienced
property crimes, producing a property crime rate of 4.64% ((295/6,359) × 100%
= 4.64%). This set of queries identied 124,295 single- and two-family residen-
tial parcels without licensed dogs, and 7,888 single- and two-family parcels
without licensed dogs that incurred property crime during 2011, resulting in
an overall property crime rate of 6.35% for single- and two-family residential
parcels without licensed dogs ((7,888/124,295) × 100% = 6.35%).
The rate of property crime for single- and two-family residences with li-
censed dogs was 1.71 percentage points lower (6.35 – 4.64 = 1.71) than the rate of
property crime for single- and two-family residences without licensed dogs. In
order to explore whether there might be a diferent relationship rate in a more
explicitly one-to-one comparison, we ran another set of queries for just single-
family residential parcels; these results are reported below.
We queried for total number of single-family residential parcels with dog
licenses associated with them and the number of that same subset that experi-
enced property crimes. This resulted in the identication of 3,481 single-family
parcels with licensed dogs, of which 105 incurred property crimes, producing a
property crime rate of 3.02% ((105/3,481) × 100% = 3.02%).
The queries on single-family parcels identied 55,192 single-family resi-
dential parcels without licensed dogs and 2,438 single-family parcels with-
out licensed dogs incurring property crime, resulting in a property crime
rate of 4.42% for single-family residential addresses without licensed dogs
((2,438/55,192) × 100% = 4.42%). The rate of property crime for single-family
residences with licensed dogs was 1.40 percentage points lower (4.42 – 3.02 =
1.40) than the rate of property crime for single-family residences without li-
censed dogs.
The results of the property crime rate analysis and the random resampling
tests are presented in Table 1. Section A in Table 1 presents results for all parcels
zoned for residential use (as a point of reference—resampling tests were not
run), section B in Table 1 presents results for parcels zoned for single- and two-
family use (including resampling test results), and section C in Table 1 presents
results for all parcels zoned for single-family only (including resampling test
results).
  ./- |   
   () -
Within each table section, the “Crime Rate” corresponds to the rates observed
for the entire population of parcels in each group (all parcels, single- and two-
family parcels, and only single-family parcels), and “Resample Test” corre-
sponds to the average rates obtained from 1,000 equal-sized, random samples
from the original parcel types generated during the resampling test using the
algorithm described above. The “% Exceeding Rate Diference” describes
the percentage of the sampling distribution obtained from the random resa-
mple test that exceeds the observed property crime rate diference for parcels
without licensed dogs versus parcels with licensed dogs.
For one- and two-family residential parcels, the observed rate diference is
exceeded in the resample test 3.7% of the time. For one-family residential par-
cels, the observed rate diference is exceeded 4.7% of the time. In both cases
  Summarized results
A) All Residential Parcels # Identied Crime Rate Resample Test
Total Parcels , .% /
Total Parcels w/ Crime ,
B) Single- & Two-Family # Identied Crime Rate Resample Test
Total Parcels w/ Dogs , .% .%
Total Parcels w/ Dogs & Crime 
Total Parcels w/o Dogs , .% .%
Total Parcels w/o Dogs & w/ Crime ,
Rate Diference: w/o Dogs vs. w/ Dogs .% +.%
% Exceeding Rate Diference .%
C) Single-Family # Identied Crime Rate Resample Test
Total Parcels w/ Dogs , .% .%
Total Parcels w/ Dogs & Crime 
Total Parcels w/o Dogs , .% .%
Total Parcels w/o Dogs & w/ Crime ,
Rate Diference: w/o Dogs vs. w/ Dogs .% .%
% Exceeding Rate Diference .%
w = with; w/o = without.
   |  ./-
   () -
we can reject the null hypothesis with 95% condence and conclude that there
is an association between the presence of licensed dogs and reduced property
crime rates in single- and two-family residential properties in Milwaukee, ,
for the 2011 data.
Discussion
The results showed a signicant reduction in property crime rates at address-
es where licensed dogs are registered. Households with licensed dogs in 2011
on parcels zoned for both single- and two-family structures enjoyed property
crime rates 1.71 percentage points lower than single- and two-family house-
holds without licensed dogs. Households with licensed dogs in 2011 on parcels
zoned solely for single-family structures enjoyed property crime rates 1.40 per-
centage points lower than single-family households without licensed dogs.
With the benet of this study’s strong evidence that dogs appear to reduce
the incidence of property crimes—at least on parcels associated with licensed
dogs and zoned for single- or two-family residences—additional, more granu-
lar evaluation is warranted. For instance, analyzing crime rates based on dog
breeds may be elucidative of breed-specic tendencies towards crime deter-
rence. Additionally, the application of bufers around parcels with licensed
dogs might allow us to identify reduced rates of crime for their neighbors
versus addresses not neighboring licensed dogs. And, separate evaluations by
crime type may also provide insights into whether dogs appear to have an ef-
fect on various crime types, and if so, to what extent.
A more ambitious, but even more revealing study might entail collaboration
with the Milwaukee Police Department and the Milwaukee Area Domestic
Animal Control Commission () to design and implement a crime
report data collection strategy that would capture—for example—the pres-
ence of dogs at property crime scenes without regard to their licensure sta-
tus; whether the perpetrator was a resident of the property crime addresses;
and whether other crime prevention tactics had been employed, such as signs
warning of alarm systems or the presence of dogs.
We would be remiss if additional possible benets of the human-canine
relationship were not mentioned before concluding. This paper accepts as a
current reality that some dogs, in some places and times, are not seen to have
value beyond their “capacity to serve some human purpose” (Benton, 2007,
p. 5). It is hoped that the analysis conducted herein is also seen—beyond the
primary search for empirical evidence that dogs contribute to reduced prop-
erty crime rates in private residences—as empirical evidence that contributes
  ./- |   
   () -
to improved understanding of how both human (e.g., reduced crime victimiza-
tion) and canine (e.g., more dogs adopted from, and treated better while in,
shelters) well-being might be improved. In short, we subscribe to what Arluke
and Sanders (1996) identify as the perspective that companion dogs are highly
social creatures who possess distinct personalities and with whom rewarding
relationships may be developed. Thus, we hope to contribute to answering the
question: “how do we engender a system of regulation and human interven-
tion that will provide the best outcome for human and non-human?” (White,
2007, p. 42)
For instance, knowing that the presence of a dog appears to reduce the inci-
dence of property crimes may encourage households to retain canine compan-
ions who might otherwise be abandoned, or to improve the level of care they
provide to their canine companions. Such outcomes would serve to counter-
balance, at least partially, that many humans forget the “harsh story behind the
making of a pet” (Tuan, 2007, p. 148), resulting in six to eight million dogs nd-
ing themselves homeless—and nearly half being euthanized (.. Humane
Society, 2014). This situation represents a moral tragedy in terms of the “physi-
cal, emotional, [and] psychological” (Beirne & South, 2013, p. xiv) harm inict-
ed on dogs and the humans employed or volunteering in non-human animal
services and rescue organizations; it is also a needless nancial burden upon
the municipalities charged with regulating non-human animal care and con-
tainment (Beirne & South, 2013).
Secondly, better policies might be expected to come from the knowledge
and experience derived from better understanding the benets of the canine-
human relationship. Such policies, for example, might include laws prohibiting
the keeping of dogs outdoors—or at least, minimally—when the temperature
drops below a certain level. Or, perhaps they would include increased munici-
pal funding of—and development and operation of more healthful programs
in—non-human animal shelter operations, more and better dog parks, and
more participation in dog obedience courses; these policy changes could lead
to increases in the current 20% rate of household non-human companion ani-
mal adoption (.. Humane Society, 2014).
Limitations
Although these results suggest a relationship between the presence of dogs
and reduced property crime rates, there are indeed limitations to this study
that should be considered. The rst class of limitations involves the source
data utilized in this analysis. First, rates of dog licensing are thought to be
   |  ./-
   () -
low in cities. We obtained an estimate of 35 to 40% dog licensure compliance
for Milwaukee County from the Milwaukee Area Domestic Animal Control
Commission (the dog-licensing agent in Milwaukee County, Wisconsin) in
2015. We rst attempted to conduct this study with Louisville, , data, but
were denied access to the addresses of dog license holders by the city govern-
ment based on privacy concerns. This may be a common concern; the 2011 data
for Milwaukee, , utilized in this study appears to be an anomaly, as neither
pre-2011 nor post-2011 data are known to be publicly available.
Such rates of licensure compliance probably mask the strength—or weak-
ness—of the relationship between dogs and property crime occurrence re-
vealed by this study. Unfortunately, there is not much that can be done about
these unavailable data in terms of obtaining them for 2011. Identifying single-
and two-family residences with resident unlicensed dogs would require con-
tacting—and receiving answers from—the residents of every address of this
subset of parcels in Milwaukee; at best this would be a daunting task. One of
the goals of our study’s methodology was to circumvent this limitation, at least
partially, by evaluating those addresses where dogs are understood to reside
(by being licensed), and identifying whether property crime rates at those
addresses (with licensed dogs) were diferent than those for similar property
types that did not have licensed (known) dogs associated with them.
A random sample survey of Milwaukee residences might be useful for iden-
tifying rates of unreported crimes and/or unlicensed dogs—but part of our
goal was to use readily available data, or data that could become readily avail-
able if the proper institutions choose to collect and/or release them in the fu-
ture; this allows for easier replication of this study in other cities. We are also
hopeful that our results will encourage more creative ways to improve dog li-
censure rates; perhaps cities will be encouraged to license dogs free of charge,
which we imagine would improve licensure rates tremendously.
Another well-known limitation is that police crime data can be of question-
able reliability and validity (Perkins et al., 1993). Some of the crime designa-
tions utilized by City of Milwaukee police have a nebulous nature. Despite a
conversation with a public inquiry ocer with the City of Milwaukee Police
Department, we were unable to discern some crime categories as being (to
whatever degree) property crimes. For example, it is feasible that none of the
crimes categorized as “all other larceny” were property crimes, but these data
have been included in our analysis because it is also feasible that some of these
were property crimes. Despite the possible veracity concerns of publicly avail-
able police data, the data are commonly used for analysis. It might be possible
to reduce such concerns as part of a pre-planned replication of this study, so
that crime data can be corroborated as they are reported during a calendar year.
  ./- |   
   () -
Also,  analysis, by itself, does not provide much explanatory information.
However, it excels at analyzing spatial data and efects. Our results show an in-
verse relationship between dog location and crime location for parcels zoned
for single- and two-family residences in Milwaukee, Wisconsin. This  study
is an important step in better understanding whether the presence of dogs
contributes to a reduction in property crime rates; it also highlights the need
for more comprehensive and readily available data concerning the presence of
dogs in urban environments.
Conclusion
We found an inverse relationship between the Milwaukee, , locations where
dogs were licensed in single- and two-family residences and rates of property
crimes at those addresses. In short, parcels zoned for single- or two-family
structures experienced property crimes during 2011 at rates between 1.40 and
1.71 percentage points lower than the property crime rates for these same par-
cel sets without licensed dogs. These results suggest further studies using spa-
tial regression and other more ne-grained analyses are warranted to identify
other forces—if any—at work in reducing rates of property crime.
Acknowledgements
The authors are grateful to Dr. David Simpson, Dr. Aaron Rollins, and Dr. John
Gilderbloom at the University of Louisville for their enthusiastic encourage-
ment that we pursue this research. Thanks are also due to Dr. Forrest Stevens,
also at the University of Louisville, for some minor statistical assistance, and
Dr. Bob Schneider, at the University of Wisconsin–Milwaukee, for data-sourc-
ing assistance. In addition, both anonymous reviewers, as well as the editor, of-
fered insightful commentary on earlier versions of the manuscript that greatly
improved the end product. Finally, we are grateful to Rob Bodart for inspiring
this study.
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Appendix A—s to Milwaukee Property File Documentation
and  Milwaukee 
City of Milwaukee Master Property File Documentation:
Residential zoning codes identied in table 295-107-2 on page 24.
http://itmdapps.milwaukee.gov/gis/mprop/Documentation/mprop.pdf
Map Milwaukee  Services:
Dog license addresses and property crime addresses geocoded to parcels using the
MAITHENDIME_GEOCODE service provided by City of Milwaukee. See page 12 in
the document at the link.
http://city.milwaukee.gov/ImageLibrary/Public/GIS/MapMilwaukee_GISServices.pdf
COMPASS:
Federally funded joint partnership between Milwaukee Police Department and
Milwaukee Information Technology Division.  Mapping application of crime data
for Milwaukee. The rst  provides information about . The second 
is the disclaimer page leading to the query application for downloading crime data.
http://city.milwaukee.gov/AboutCOMPASS.htm#.VZQd1E3bK71
http://itmdapps.milwaukee.gov/compass/disclaimerQuery.html
City of Milwaukee Police Department—Oce of Public Relations:
Assisted with determining which crime categories are, or could be, considered prop-
erty crimes. See Appendix C for details.
http://city.milwaukee.gov/Directory/police/Information--Services.htm#Oceof
PublicRelations
  ./- |   
   () -
Appendix B—Data Files and Sources
Data Source
 City Shape File University of Wisconsin–Milwaukee
American Geographic Society Library
 Neighborhoods Shape File University of Wisconsin–Milwaukee
American Geographic Society Library
 Parcels Shape File University of Wisconsin–Milwaukee
American Geographic Society Library
City Property Data Files  University of Wisconsin–Milwaukee
American Geographic Society Library
 Milwaukee County Dog License
Data
University of Wisconsin–Milwaukee
American Geographic Society Library
 City of Milwaukee Crime Data City of Milwaukee Police Department
Milwaukee County Building Shape File Milwaukee County Land Oce
Milwaukee County /Line Shape
Files
.. Census Bureau
Appendix C—Crime Designations (All to Clearly or Likely
“Property Crimes”)
Crime Designation
NOT a
Property
Crime
Not typically
a Property
Crime;
usually NOT
a Property
Crime
Typically
not Robbery
or Burglary
and NOT a
Property
Crime
Could be any
type of crime
INCLUDING
Property Crime
Property
Crime
Aggravated assault X
All other larceny X
All other ofenses X
Arson X
Bribery X
Burglary/breaking
and entering X
Counterfeiting/
forgery X
   |  ./-
   () -
Crime Designation
NOT a
Property
Crime
Not typically
a Property
Crime;
usually NOT
a Property
Crime
Typically
not Robbery
or Burglary
and NOT a
Property
Crime
Could be any
type of crime
INCLUDING
Property Crime
Property
Crime
Destruction/
damage/vandalism
of property X
Disorderly conduct X
Forcible fondling X
Forcible rape X
Forcible sodomy X
Homicide X
Impersonation X
Incest X
Intimidation X
Kidnapping X
Liquor law
violations X
Motor vehicle theft X
Murder/non-
negligent
manslaughter X
Pocket picking X
Purse snatching X
Robbery X
Sexual assault with
an object X
Shoplifting X
Simple assault X
Statutory rape X
Stolen property
ofenses X
Theft from building X
Theft from coin-
opperated machines X
  ./- |   
   () -
(cont.)
Crime Designation
NOT a
Property
Crime
Not typically
a Property
Crime;
usually NOT
a Property
Crime
Typically
not Robbery
or Burglary
and NOT a
Property
Crime
Could be any
type of crime
INCLUDING
Property Crime
Property
Crime
Theft from motor
vehicle X
Theft of motor
vehicle parts/
accessories X
Trespassing X
: The crime types in the bold, shaded cells were those included in the study. Additionally,
all crimes labeled as “ALL OTHER LARCENY” were included due to their indeterminate
nature.
... The historical record shows the use of guard dogs in Roman times (Clutton-Brock, 1999;Coren, 1994) and during the early modern era in Europe (Ekirch, 2005). In the contemporary world, dogs are used to protect livestock from carnivorous predators (Andelt & Hopper, 2000;Green et al., 1984), and farms, businesses, and homes from theft (Adams & Johnson, 1995;Bunei et al., 2014;Grooms & Biddle, 2018). It is not simply an impression that dogs ward off predators and intruders; the data support this idea. ...
... It is not simply an impression that dogs ward off predators and intruders; the data support this idea. For example, urban homes with registered dogs in them are less likely to experience property crime (Grooms & Biddle, 2018). Today, many people rely on dogs to alert them to possible intruders, especially at nighttime when most or all of the household is asleep and in a state of heightened vulnerability. ...
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