Transcript
This is the l"lAffidavit of Kim Rossmo in this case and was made on the r{ltt¿sy s1 October,2012.
Action No. 5112969 Vancouver Registry
IN SUPREME COURT OF BRIT¡SH COLUMBIA
Between:
Amanda Elizabeth Ladas
Plaintiff And:
Apple Inc,
Defendant
"Brought under the Class Proceedings Act, RSBC 1996, c. 50"
AFFIDAVIT
l, Kim Rossmo, Professor and Geographic
THAT:
Profiler, c/o Suite 302-1224 Hamilton
Street, in the City of Vancouver, Province of British Columbia, MAKE OATH AND SAY
1.
Attached hereto and marked as Exhibit "4" to this my affidavit is a true copy of
my expert report herein dated September 2012.
SWORN BEFORE ME at the Citv of
Èet:+.L, '-f{. Kn<
jn
tre
,
grff)éÌ
z$l-
this
day of
October, 2012.
Kim Rossmo
tu
MICHELE MUNIZ
i.XtS vy'commiásionExpires ¿;i':lìs Morch 20,20]3
Notary Public, State of Texas
GenLiA9ST2laff#1 of Kim Rossmo
Introduction
This report was prepared by Dr. Kim Rossmo of Austin, Texas. My areas of expertise
include geographic profiling and investigations. A curriculum vitæ, outlining my qualifications,
employment, and education experience in my areas of expertise, is attached.
Instructions Provided and Nature of Opinion Sought
I was asked to prepare a report setting out possible
uses, misuses and ramifications related to
and arising from the collection and storage of locational and other data on Apple smart devices,
as demonstrated
by Francis Graf in connection with the Plaintiff s claims made in the Action.
This opinion relates to the issue of damages in this proceeding.
Opinion My opinion and findings are laid out in the Consequences section below.
Reasons for Opinion Assumptions
This report assumes that an Apple smart device (e.g., iPhone) running the iOS4x operating
system generates an unencrypted backup file containing location and date/time information for a
one-year period stored
in a consolidated.db database file (re iOS4x Location Based Services
Analysis Report, June 6, 2012, by Francis Graf).
Methodologv
This report discusses possible misuses of the unencrypted backup file on Apple smart
devices running the iOS4x operating system, focusing on the loss of privacy implications for the
user. The likelihood of these misuses is not evaluated. The references used in the preparation
this report are listed at the end.
of
Ädvice and Certification
I certifu that I afirawate of my duty to assist the court and not bo an advocate for any party,
that I have made this report in conformity with that duty; and that I will, or written testimony, give that testimony in conformity with that duty.
if
called on to give oral
I am responsible for the contents of this report.
Respeetfu lly submitted,
D. Kim Rossmo, PhD
Introduction
Our daily routines, travels, and destinations say much about our lives. Many of our personal
activities can be inferred from where we go, when we travel, and how long we stay somewhere. Just as we wish to keep our private conversations, financial status, and credit card purchases private, we also do not want to be electronically "shadowed." Unfortunately,
it
appears that an
Apple smart device, such as the iPhone, which uses the iOS4x operating system, generates an
unencrypted backup
file containing location and date/time information for a one-year period
file (Graf, 2012).
stored in a consolidated.db database
The potential loss of privacy consequences to the user from this backup file depend upon
two factors: (1) the information that can be derived from the file's data (a function of
measurement precision, data comprehensiveness, and anal¡ic potential); and (2) from who can
access that data (exposure).
1.
Information
-
The more information that can be derived from the data in the backup file,
the greater the loss of privacy. The information potential depends on the precision and
comprehensiveness of the data and on its analyic potential.
a.
Precision
-
The more precisely an iPhone or similar Apple smart device determines
the position of a user, and the more precisely the time is recorded for when a user
was at a given position, the greater the loss of privacy.
b.
Comprehensiveness
- The higher the proportion
of a user's movements recorded in
the backup file, the greater the privacy loss. Similarly, the longer the recording
period, the greater the privacy loss.
c.
Analytic Potential
-
Location and time data can be mapped, measured, and analyzed
measures.
in a number of different ways, and can be used to generate derivative
The greater the analytic potential of this information for inferring actions of the
user, the greater his or her privacy loss.
2.
Exposure
-
The potential privacy loss consequences depend on who (i.e., individuals,
companies, and organizations) can access the user's Apple device backup file.
These factors are discussed in more detail below.
Information
Precision
The backup file contains the locations of cellular telephone towers and V/iFi sites that are
near the device when
it is
used, not the exact location of the device itself (Apple Press,
20ll).
This results in a loss of precision as there is almost always some distance between a device and
even the nearest cell tower or WiFi
site. Furthermore, in an urban
area, there are usually a
number of different cell towers and V/iFi sites within range of a device. Depending on how long
a device was operated in a given area, multiple locations
distances
-
in different directions and varying
- will be recorded. The level of geographic
accrüacy therefore depends on the density
of both cell phone towers and V/iFi locations in the surrounding atea, and on the street pattern or highway network during travel.
Each location entry in the backup file includes the date and time the consolidated.db file was updated; consequently, a large number of entries have the same date and
time. The exact date
(it is not
and time a position was recorded by the device is apparently not available from the f,rle
known
if
Apple has a way of accurately determining date/time information). Therefore,
temporal precision is
low. Only a date-time interval
can be determined, equal to the lag between
updates. The more often this happens, the narrower will be this range.
Comprehensiveness
The proportion of a user's movements recorded in the backup file appears to be related to how often the Apple device is used and how it is used. An individual who frequently uses his or her iPhone, especially while traveling,
recorded in the file.
will have a greater proportion of his or her movements
The recording period for the backup file is one year. An individual engages in many
activities and much travel in a year, so consequently the file provides a great deal of personal information about a user (Graf, 2012).
Anal)¡tic Potential Even with the precision limitations discussed above, certain analyic techniques exist that
can improve geographic and temporal accuracy. For a stationary device, measures of geographic
central tendency such as the spatial mean, the spatial median, or the centre of minimum distance.
Figure 1 shows these measures for the WiFi sites from an Apple iPhone user's backup file (the
red circles are WiFi sites, the black triangle is the spatial mean, the white triangle is the spatial
median, and the yellow triangle is the centre of minimum distance). All three measures fall very
close to the user's offrce at 612 View Street, Victoria, British Columbia (marked with a blue
square), where she used her iPhone. Figure 2 shows the same measures for cell tower locations.
In this case, proximity to the user's office is lower, most likely because there were fewer towers and their backcloth (i.e., their distribution
in
space)
is less uniform. However,
geographic
profiling, a more robust technique that is less sensitive to skewed spatial distributions, located
the user's office
within
a block (see Figures 3 and 4).
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2. Measures of cental tendency for cell phone towers.
I
Figure
í:-zView St.
3. Geoprofìle of cell phone towers.
Figure
4. Geoprofile þeak
1%) of cell phone towers.
A moving device has only
a couple
of seconds to make a connection to a cell phone tower or
WiFi site. Consequently, there are fewer entries in a given arcaand it is much easier to discern
the user's movement, direction of travel, and often specific route (see Graf,2072).
Even though only the date and time the consolidated.db database file was updated is
recorded, it appears the locations are recorded in the file in chronological order. For example, all
the entries for an iPhone user's travel along British Columbia Highway 4 Parksville
-
from Port Albemi to
-
show a date-time stamp of 11/18/1
I
6:25 pm (Graf, 2012). However, as can be seen
in Figure 5, moving from west to east the row number of the entry increases (e.g., 4685,4692,
4693, 4700,4709). Consequently, even though individual entries do not have an accurate time
10
stamp, they are listed in chronological order. This is consistent with the offrcial description
of
how the iPhone operates (Apple Press, 20lI).
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Figure 5. iPhone user's travel route on British Columbia Highway 4.
A location entry can therefore be both
placed in chronological order in relationship to the
other location entries in the file, and bracketed by date and time (before or equal to its recorded date-time and after the date-time of the prior update). This means that longer movements (i.e.,
greater than the median distance to nearby
'WiFi
sites, or approximately 100-200 meters in an
urban core) by the user can usually be detected from the consolidated.db update
file.
Finally,
logical inferences regarding starting times and speed allow for funher temporal accuracy. For
example,
if a user traveled from his work site to abar, the arrival time
can be estimated from
knowing when he usually left work, the distance between his work site and the bar, and the speed
limit.
11
It may be possible to use the location data in the consolidated.db update file to make several
inferences:
the location of the user's home - the location the user most commonly visited, at
daily intervals, and where the user typically spends the night
the location of the user's work
-
the location regularly visited during business hours,
on weekdays but not weekends, typically involving no movement for several hours
the locations where the user's relatives, relationships, and friends reside
-
residential
locations regularly visited, particularly during the evenings or on weekends the location(s) of the user's significant relationships
-
residential locations regularly
visited, particularly during the evenings, on weekends, or ovemight
o o o
the locations where the user engages in social activities
-
locations visited outside
of
work hours, particularly during the evenings or on weekends
other locations the user has visited
travel and commuting routes.
The addresses and dates/times in the consolidated.db update file are not isolated data. The
file's total knowledge potential must be
understood
in the context of
other geographic
information and existing spatial databases. Maplnfo (2004), a geographic information system (GIS) company, claims that over
addresses
85%o
of data has a geographic component. In other words, the
in the consolidated.db update file are not isolated points. Rather, they are points that within the context of such other geographic information
as land
can be placed on a map and seen
use, zoning, and nearby businesses, facilities, and residences. Additional knowledge can be gained from any available ancillary information, such as telephone calls, websites visited, and historical travel and behaviour patterns. Furthermore, patterns of movements and places visited
l2
over time reveal more about an individual than isolated locations. Such patterns extend over both space and time. Under certain conditions, it is even possible to make future predictions
behaviour from such data. The lack of precision limits the accuracy of the inferences that can be made from the data in
of
the consolidated.db
file.
However, much can still be determined about a user, especially one
who often uses his Apple smart device, by employing various anal¡ic techniques. The length
the backup
of
file (one year) increases this anal¡ic potential.
emerges from the ambiguity
An important issue
of user movement
caused by
this lack of
a
precision. Situations will arise where erroneous information incorrectly suggests a user visited
problematic location.
If
a user's backup
file falls into the hands of individuals or agencies that
react on the basis of probabilities or suspicion, this could result in such negative consequences as
relationship strain, divorce, loss of employment or advancement opportunity, media allegations,
or unwarranted attention from law enforcement or govenìment intelligence agencies (see the
discussion in the Government subsection below).
Exposure
The nature and extent ofprivacy loss and consequent injury depends on who can gain access
to a uset's backup
access could occur
file. Access
to the file can occur either physically or electronically. Physical
by a spouse, family member, friend, roommate, housekeeper, work colleague,
boss, secretary, subordinate, burglar, or
thief. Electronic
access could occur by
Apple (assuming
Apple can access a user's backup file; see Smith, 2010, 2012) or by a hacker or network
eavesdropper (see Henry, 2012). Once an outsider has obtained a copy, the frle can be passed on
to anyone including advertisers, "trusted third parties," (i.e., Apple's partner companies), private
13
detectives, divorce lawyers, industrial espionage agents, debt collectors, police, goverrìment, and
intelligence agencies.
Some potential misuses are discussed
below. These should be seen as possibilities, not
necessarily probabilities. However, given a large enough group of Apple users, it would not be
surprising
to
see many
of these
misuses eventually
occur. Also, this is by no means
a
comprehensive list.
Personal
Indiscriminate access
to a user's backup file could have significant
consequences for
familial, personal, and romantic relationships.
It
gives an individual the ability to track the
movements of his or her spouse in marital disputes, custody battles, and divorces. Visits to certain locations (e.g., an ex-girlfriend) might cause strains in an engagement. A person could use such information to undermine their siblings in an effort to influence the deliberations of their parents regarding wills or inheritances. Some families
of certain cultures
engage
in
comprehensive background checks
of their
as
children's prospective husbands and wives. Apple smart device backup files could be used
part of such a vetting.
V/ork
Some businesses have required
job applicants to provide
access
to their Facebook accounts
(Maltais, 2012). "It's become standard practice for employers and schools to peruse potential applicants' Facebook profiles. But in some cases, they are going even further: Some have
demanded applicants hand over their passwords so they can view individual's restricted profiles"
14
(Stem, 2012). Similarly, companies could ask prospective employees to provide the backup files
from their Apple smart devices. Software could easily evaluate the data for potential personnel
problems based on such warning signs as:
time away from work
frequent visits to problematic places (e.g., bars, nightclubs, racetracks, casinos, brothels,
vice districts, etc.).
In such situations, the onus would be on the job applicant to justify their behaviour.
A user who sometimes backs up his or her Apple device on an office computer exposes their
backup file
to employers,
supervisors, colleagues, employees, and administrative assistants.
Under certain conditions, loss
of this information to the wrong person could jeopardize
a
promotion, cause loss of employment, or even result in organizational blackmail.
Government
For obvious reasons, individuals engaged in criminal behavior do not want the backup files of their Apple smart devices inspected by police. However, it is not just law breakers that might
have cause for concem. Intelligence and homeland security organizations operate on the basis
of
possibilities and probabilities.
If
government agencies can gain access to a users' backup file
through Apple or an intermediate entity, an individual who was in
-
or even near
- the wrong
place at the wrong time, could find themselves scrutinized by police or by an intelligence service. Mere suspicion has been sufficient in the past for some people to be questioned, placed
on a watch list or do-not-fly registry, or refused entry into a country. Governments have also used private data-mining companies to determine how public and
private records might be analyzed for certain security or military objectives. For example,
a
15
marketing services business called Acxiom (http://www.acxiom.com/) sold demographic data on
a group of airline passengers to a U.S. Department of Defense contractor (U.S. should prosecute
JetBlue, 2003). Acxiom provided data on gender, home owner/renter status, years at residence,
income, number of children, Social Security numbers, occupation, and vehicle information
If Acxiom was an Apple "trusted third party,"l it
backup
could do much with the data in users'
files. Analysis and sale of such information is not restricted to person-based questions;
rather,
it
could just as easily be location or time based. For example, a request by a data
customer might be of the following nature:
1.
Provide the identity of every Apple user within 100 meters of this particular address on this particular date.
2. Filter that dataset by the following
user characteristics (obtainable from individual
identifying information; see Smith,2010,2012): (1) gender; (2) age; (3) ethnicity; and
(4) occupation.
3.
Cross-compare that result with the following govemment databases. . ..
Businesses
There are many ways that private businesses and companies could exploit the data in Apple backup files, not all of them to the user's benefit. Targeted advertising is the most obvious one.
Marketing companies already use an individual's home address and the demographics of his or
her neighbourhood for delivery of direct ("junk")
mail. Knowledge of
a person's movements
and routine travels would open up a new world of invasive advertising possibilities.
This phrase "trusted third party" raises the question, trusted by whom? different answer to this question than would Apple.
I
A
user might well have a very
T6
More problematic is how insurance companies might use such information. Knowledge of a user's movements could provide insight into an applicant's eating and drinking habits through
information on restaurants and bars frequented
-
which ones, how often, and for how long.
Automobile insurance companies may even be able to detect a user's risky driving behaviour
such as speeding.
The information in the Apple smart device backup files of key employees, managers, and executives is valuable industrial espionage and could be exploited to advantage by business
competitors.
Criminals
Criminal access to a user's backup file, either through theft or by hacking, increases his or
her exposure to victimization. More sophisticated criminal groups could use this information to
determine when someone was likely to be present or away from certain locations such as their
home. Such knowledge could then be used to assist in the perpetration of fraud, identify theft, or
burglary. It would be disastrous
if
a user's backup file fell into the hands of a stalker. For
certain individuals, at risk of extortion, kidnapping, or terrorism because of their social, work, or
political position, the implications of a stolen file are even more sinister.
Other
The data in the backup hle of an Apple device owned by a movie celebrity, sports star, politician, or other famous person would prove to be a bonanza of information and innuendo for
media outlets, tabloids, and scandal magazines. Similarly, the data could prove to be a potent
weapon in the hands of a rival candidate in any political election.
l7
Possible Misuses with Advanced Accuracy
The details of the exact data available to Apple (and through Apple, other businesses and
organizations) is not known. If Apple has methods for obtaining accurate location and time data
from the backup file, then the loss of user privacy is tremendous. With such information, it
would be possible to accurately generate what is c.alled a geospatial lifeline, defined as
a
"continuous set of positions oceupied in space over some time period" (Mark, 1998, p. l2). Figure 6 shows a typical example of an individual's one-month geospatial lifeline (Grengs,
W*g, & Kostyniuk,
2008).
18
Legend
Destlnatlon Vlslt Frequency 22-41 12-21 4-11 1-3
Route Visit Frequency
Figure 6. One-month geospatial lifeline.
Much can be derived from a person's lifeline. "Such individual lifelines presage a new era
of movement analysis ... in which scientists from various research fields previously hampered
by sparse and random movement observations can now be hard on the heels of their subjects as they move in space and time" (Laube, Dennis, Forer,
&
'Walker,
2007,
p.
a8\.
Specialized
computer software, such
as GeoTime developed by the
Canadian company Oculus
l9
(nttp:¡¡www.geotime.c ), now exists for analyzing geospatial lifelines. Figure 7 shows
example of a geospatial lifeline displayed in GeoTime.
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Figure 7. Geospatial lifeline displayed in GeoTime.
A geospatial lifeline obviously provides information on a user's position (location and time)
and movement (speed and direction). However, those who work in the field of Moving Object Databases (MOD) have also developed complex models and query languages
for analyzing
geospatial lifelines. For example, some of the existing context operators and standardization techniques include location, interpolated locations, speed, acceleration, movement azimuth,
20
sinuosity, tortuosity, straightness, path entropy, navigational displacement, absolute and
standardized approaching rates, first derivatives, interval standard deviation, time series analysis, path time and distance analyses, equal duration and track wrapping, spatial and temporal distance analyses, bipolar analysis, and spatial variance analysis (Laube et al.,
2007). Much can be
inferred from these measures.
A study on the geographic
pattems of reoffending criminals illustrates the potential uses
of
geospatial lifeline analysis. Parolees who reoffended while on an electronic monitoring (EM)
and global positioning system (GPS) program were identified and their movements for a period
of eight days prior to the new crime then mapped and analyzed (Rossmo, Lu, & Fang, 2011).
The objective was to study their spatial activity pattems prior to, during, and after offending in
order to distinguish routine travel from criminal movement. For example, the normal routine
of
one particular parolee consisted
of a single daily trip to a location northwest of his
home.
However, three days before his re-offence he began to visit a commercial area to the northeast of his home that was characteÅzed by restaurants, bars, motels, and parking lots. His movements
here exhibited hunting behaviour, as shown by the numerous stops and turns in this new part
of
his geospatial lifeline. On his third trip to the commercial area he reoffended and sexually
assaulted a woman in a parking lot (see Figures 8 and 9).
2l
Legend
I I O
ofiense location
Home location GPS recorded poinls Unban areas
V\fater areas
Trajectory 29th Speed (mi/hr)
---- 0.00-500 ----5.01 -15.00 ¡rr¡ 15.01 -45.00 r r. r 45.0l - 95.16
Figure 8. Parolee's movements on offense day.
22
Legend
! I O
offense location
Home location GPS recorded points Unban areas
Trajectory 29th Speed (mi/hr)
---- 000-s.00 ----5.01 -15.00 ..r¡ 1501-4500 rrrr 45.01 -9516
ffMites 0 01 02
04
06
08
ll
:ll
Figure 9. Parolee's movements near crime site on offense day.
An analysis of this parolee's geospatial lifeline showed: (1) departure from nolmal routine;
(2)
repeated visits
to a new aïea; and. (3) high movement density2 (i.e., much travel back
and
forth within a small region, indicative of search behaviour). These spatial aberrations,3
integrated with information on the area's land use and knowledge of the parolee's previous
Spatial activity patterns can also be categorized by such measures as total daily distance traveled, peak and mean hourly velocity, momentum (changes in velocity over time), peak and mean density (travel within areal units), directional changes per hour, movement length between direction changes, and other indicators of search and hunt behavior as opposed to purposeful travel. Also important are type of area (e.g., land use, zoning, crime generators and attractors, etc.) traveled through and prior offender modus operandi factors.
2
A review of another EM program found the most common rule violation involved detours (Gibbs & King, 2003). In contrast, if the routine movements of a pedophile intersects with locations frequented by children schools, playgrounds, parks, daycare centers - he has a higher chance ofrecidivism (Ouimet & Proulx, I 994).
23
3
criminal behaviour, suggested a high probability of reoffending. The study concluded "It may be possible to use electronic monitoring and global positioning system program data to assist in offender risk evaluation during supervision. Differences in the movement patterns of offending
parolees could provide community supervision agents with an early warning tool to facilitate
timely interventions and help prevent new crimes" (Rossmo, Lu,
&, Fang,
20ll,p.4l).
Multiple User and Social Network Analysis
Apple and its "trusted third parties" could have the ability to examine the intersection of
backup file data from multiple users, opening the door to powerful group analyses. Eagle and
Pentland (2006) demonstrated "the ability to use standard Bluetooth-enabled mobile telephones
to measure information access and use in different contexts, rccognize social patterns in daily
user activity, infer relationships, identiSr socially significant locations, and model organizational
rhythms.... The data these devices have returned to us is unprecedented in both magnitude and
depth. The applications we have presented include ethnographic studies of device
usage,
relationship inference, individual behavior modeling, and group behavior analysis" (pp. 255,
267).
It is possible to determine
existence and type
of relationship from the intersection of
geospatial lifelines using such variables as context, proximity, time of day and day of week of
proximity, time length of proximity, and repetition of proximity. Models have been developed
that can accurately discern friends, acquaintances, and workplace colleagues on this basis (Eagle
& Pentland,2006;
see Figure 10).
24
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Figure 10. Friendship and daily proximity networks.
Conclusion
Smart phones provide great functionality and
utility. However, by the very
use of this wide-
ranging functionality, an individual reveals much about their personal preferences, choices, and behaviours. This information must be protected and a user's privacy maintained.
There
is
great value
in this personal data for Apple
and other businesses; however,
be giving
individuals are not paid for
it and they do not realize they might
it away. No one
purchase
would want indiscriminate disclosure of their banking information or credit card
history. Similarly, few who understand the full implications of sharing the information in their
backup file would willingly agree to do so.
The sensitivity to the loss of privacy regarding our personal movements can be seen in the
reactions
to a study on individuals'
movements which only used aggregated (i.e., not location-tracking data from 100,000
individualized, like the data
in an Apple backup file)
cellphones in Europe (Gonzéùez, Hidalgo, & Barabási, 2008):
The use of cellphones to track people, even anonymously, has implications for privacy that make this "a troubling study," said Marc Rotenberg, a founder of the Electronic Privacy
25
Information Center in V/ashington. The study, Mr. Rotenberg said, "raises questions about the protection of privacy in physical spaces, when devices make possible the capture locational data."
of
There are serious ethical issues as well, said Arthur Caplan, director of the Center for
Bioethics at the University of Pennsylvania. While researchers are generally free to observe people in public places without getting permission from them or review from institutional
ethics boards, Mr. Caplan said, "your cellphone is not something
I would consider a public
entity." (Schwartz, 2008) In 1984, the world was introduced to the Apple Macintosh computer by a now famous
Super
Bowl commercial which depicted a female heroine resisting the tyranny of Big Brother. In
George Orwell's dystopian novel,
Big Brother was the omniscient head of a
totalitarian
government. In 2012, we must be careful that Big Business does not take the place of Big
Brother.
26
References
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30
D. Kim Rossmo Curriculum Vitæ
Summary of Professional Background Dr. Kim Rossmo is the University Endowed Chair in Criminology, and the Director of the Center for Geospatial Intelligence and Investigation in the School of Criminal Justice at Texas State University. He has a PhD in criminology from Simon Fraser University, and has researched and published in the areas of environmental criminology, the geography of crime, and criminal investigations. Dr. Rossmo was formerly a management consultant with the Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF), and the Director of Research for the Police Foundation in Washington, DC. Before that, he was the Detective Inspector in charge of the Vancouver Police Department's Geographic Profiling Section, which provided investigative support for the intemational law enforcement community. Dr. Rossmo is a member of the Intemational Association of Chiefs of Police (IACP) Advisory Committee for Police Investigative Operations and is an Adjunct Professor at Simon Fraser University. He sits on the editorial board for Homicide Studies and is a Full Fellow of the Intemational Criminal Investigative Analysis Fellowship (ICIAF). Recently, Dr. Rossmo completed projects studying the geospatial structure of terrorist cells, geographic profiling applications in counter-insurgency, and patterns of illegal border crossings. He has also analyzed the spatial dynamics of animal foraging, including white shark predation and the origins of infectious diseases. He has published books on geographic profiling and criminal investigative failures. In 2000, Dr. Rossmo was awarded the Governor General of Canada Police Exemplary Service Medal. He was appointed to the City of Austin Public Safety Commission in 2009.
Employment Texas State University University Endowed Chair in Criminology, School of Criminal Justice (2007-present) Director, Center for Geospatial Intelligence and Investigation (2004-present) Research Professor, School of Criminal Justice (2003 - present)
Simon Fraser University Associate Scholar, Institute for Canadian Urban Research Studies (2004-present) Adjunct Professor, School of Criminology (1996-present) Sessional Instructor, School of Criminology (1988-1995)
Environmental Criminology Research Inc. Chief Scientist ( 1 996-present)
Bureau of Alcohol, Tobacco, Firearms and Explosives Management Consultant (2003 -2005)
Police Foundation Director of Research (2001-2003)
Vancouver Police Department Detective Inspector, i/c Geographic Profiling Section (1995-2000) Constable, Patrol, Expo 86, Community Liaison, CLEU Intel, Crime Prevention (1980-1995) Communications Operator, Communications Centre ( 1 978-2000)
Education
Degrees
PhD (Criminology, 1996), Simon Fraser University Dissertation: Geographic Profiling: Target Patterns of Serial Murderers M.A. (Criminology, 1988), Simon Fraser University Thesis: Fugitive Migration Patterns B.A. (Sociology, 1978), University of Saskatchewan
Certifi cates and Certifications Advanced Police Studies, General Police Studies; Canadian Police College Police Advanced Certificate of Education, Police Studies Certificate Program; Justice Institute British Columbia POST certified, Califomia TCLEOS certified, Texas
of
Publications
Books Rossmo, D. K. (2000). Geographic profiling. Boca Raton, FL: CRC Press. 'Watanabe, Rossmo, D. K. (2002). Geographic profiling (S. Watanabe, K. M. Suzuki, & T. Shimada, Trans.). Kyoto: Kitaohji Shobo. (Original work published 2000). Rossmo, D. K. (2007). Geographic profiling (M. Lee, Trans.). Beijing: Chinese Public Security University Press. (Original work published 2000). Rossmo, D. K. (2009). Criminal investigative failures. Boca Raton, FL: Taylor & Francis.
Book Chapters Beauregard,E., & Rossmo, D. K. (2007). Profilage géographique et analyse des tactiques de chasse chez les agresseurs sexuels sériels. In M. St-Yves & M. Tanguay (Eds.), Psychologie de I'enquête criminelle: La recherche de la vérité (pp. 577-605). Cowansville, Québec: Les Editions Yvon Blais. Beauregard,E., & Rossmo, D. K. (2008). Geographic profiling and analysis of the hunting process used by serial sex offenders. In M. St-Yves & M. Tanguay (Eds.), The psychology of criminal investigations: The searchfor the truth (çry. 529-55a) (J. Miller, Trans.). Toronto: Carswell. (Original work published 2007). Beauregard, E., Rossmo, D. K., & Proulx, J. (2011). A descriptive model of the hunting process of serial sex offenders: A rational choice approach. In M. Natarajan (Ed.), Crime opportunity theories: Routine activity, rational choice qnd their variants. Surrey, UK: Ashgate. Holmes, R. M., & Rossmo, D. K. (2002). Geography, profiling, and predatory criminals. In R. M. Holmes & S. T. Holmes, Profitüngviolent øimes; An investigative tool (3'd ed.) þp. 20S222). Thousand Oaks, CA: Sage.
Rossmo, D. K. (1995). Multivariate spatial profiles as a tool in crime investigation. In C. R. Block, M. Dabdoub, & S. Fregly (Eds.), Crime analysis through computer mapping (pp. 6597). Washington, DC: Police Executive Research Forum. Rossmo, D. K. (1995). Place, space, and police investigations: Hunting serial violent criminals.
In J. E. Eck & D. L. Weisburd (Eds.), Crime and place: Crime prevention studies, Vol. 4 (pp.2I7-235). Monsey, NY: Criminal Justice Press. Rossmo, D. K. (1995). Strategic crime patteming: Problem-Oriented policing and displacement. In C. R. Block, M. Dabdoub, & S. Fregly (Eds.), Crime analysis through computer mapping þp. 1-1a). Washington, DC: Police Executive Research Forum. Rossmo, D. K. (1996). Targeting victims: Serial killers and the urban environment. In T. O'Reilly-Fleming (Ed.), Serial and mass murder: Theory, research and policy þp. 133153). Toronto: Canadian Scholars' Press. Rossmo, D. K. (1997). Geographic profiling. In J. L. Jackson & D. A. Bekerian (Eds.), Offender profiling: Theory, research and practice (pp. 159-175). Chichester: John Wiley & Sons. Rossmo, D. K. (1997). Geographic prof,rling. In J. L. Jackson & D. A. Bekerian (Eds.), Offender profiling. Theory, research and practice (Japanese translation). Rossmo, D.K. (1997). Place, space, and police investigations: Hunting serial violent criminals. In D. V. Canter &,L. J. Alison (Eds.), Criminal detection and the psychology of uime (pp. 507 -525). Aldershot, Hants : Ashgate Publishing (reprint). Rossmo, D. K. (2003). Maps, technology, and the search for treasure. In M. R. Leipnik & D. P. Albert (Eds.), GIS in law enforcement: Implementation issues and case studies (pp.xii-xiv). London: Taylor & Francis. Rossmo, D. K. (2004). Geographic profiling. In Q. C. Thurman &, J. Zhao (Eds.), Contemporary policing: Controversies, challenges, and solutions (pp.274-284). Los Angeles: Roxbury Publishing (reprint). Rossmo, D. K. (2004). Geographic prof,rling as problem solving for serial crime. In Q. C. Thurman &, J.D. Jamieson (Eds.), Police problem solving þp. 121-131). Cincinnati: Anderson Publishing. Rossmo, D. K. (2004). Geographic profiling update. In J. H. Campbell & D. DeNevi (Eds.), Profilers: Leading investigators take you inside the criminal mind (pp.29I-312). Amherst, NY: Prometheus Books. Rossmo, D. K. (2005). The deadlier of the species. In H. Scott, The female serial murderer: A sociological study of homicide and the " gentler sex" (çry. i-iii). Lewiston, NY: Edwin Mellen Press. Rossmo, D. K. (2005). Geographical profiling. In M. Strano &,R.Bnnzone (Eds.), Psychological uiminal profiling: Manuale operativo (chap. 4). Florence: Societa Editrice
Europea. Rossmo, D. K. (2006). Geographic profiling in cold case investigations. In R. Walton(Ed.), Cold case homicides: Practical investigative techniques (pp. 537-560). Boca Raton, FL: CRC
Press.
Rossmo, D. K. (2008). Geographic profiling. In
M. Strano (Ed.), Manuale di investigazione criminale (pp. a07-al7). Rome: International Crime Analysis Association. Rossmo, D. K. (2008). Place, space, and police investigations: Hunting serial violent criminals. In D. V. Canter & D. Youngs (Eds.), Principles of geographical offender profiling (pp. A9I 63). Aldershot, Hampshire : Ashgate Publishing.
Rossmo, D. K. (2009). Geographic profiling in serial rape investigations. In R. R. Hazelwood & A. W. Burgess (Eds.), Practical aspects of rape investigation: A multidisciplinary approach (4th ed.). Boca Raton, FL: CRC Press. Rossmo, D. K., & Fisher, D. K. (2004). Problem solving prostitution in a problem neighborhood. In Q. C. Thurman & J. D. Jamieson (Eds.), Police problem solving (pp. 87-96). Cincinnati: Anderson Publishing. Rossmo, D. K., Laverty, I., & Moore, B. (2005). Geographic profiling for serial crime investigation. In F. Wang (Ed.), Geographic inþrmation systems and uime analysis (pp. 102-117). Hershey, PA: Idea Group Publishing. Rossmo, D. K., Lu, Y., &.Fang, T. (2011). Spatial-temporal crime paths. In M. A. Andresen & J. B. Kinney (Eds.), Patterns, prevention, and geometry of crime (pp. 16-42). London: Routledge. Rossmo, D. K., & Rombouts, S. (2008). Geographic profiling: An investigative application of environmental criminology. In R. Wortley & L. Mazerolle, Environmental criminology and crime analysis (pp. 136-1a9). Cullompton, Devon: Willan Publishing. Rossmo, D. K., & Velarde, L. (2008). Geographic profiling analysis: Principles, methods, and applications. In S. Chainey & L. Tompson (Eds.), Crime mapping case studies: Practice and research þp. 35-a3). Chichester: John V/iley & Sons. Saville, G. J., & Rossmo, D. K. (1995). 'striking a balance': Lessons from problem-oriented policing in British Columbia. In K. M. Hazlehurst (Ed.), Perceptions ofjustice: Issues in indigenous and community empowerment (pp. 119-I4l). Aldershot, England: Avebury. Refereed Journal Articles Beauregard, E., Proulx, J., Rossmo, D. K., Leclerc, 8., & Allaire, J.-F. (2007). Script analysis of the hunting process of serial sex offenderc. Criminal Justice and Behavior, 34,1069-1084. Beauregard, E., Rebocho, M. F., & Rossmo, D. K, (2010). Target selection patterns in rape. Journal of Investigative Psychology and Offender Profiling, 7, I37 -I52. Beauregard, E., Rossmo, D. K., & Proulx, J. (2007). A descriptive model of the hunting process of serial sex offenders: A rational choice approach. Journal of Family Violence, 22, 449463. Blair, J.P., &, Rossmo, D. K. (2010). Evidence in context: Bayes' Theorem and investigations. Police Quarterly, I 3, 123-I35. Le Comber, S. C., Nicholls, 8., Rossmo, D. K., & Racey, P. A. (2006). Geographic profiling and animal foraging. Journal of The or etical Biolo gy, 2 4 0, 233 -240. Le Comber, S. C., Rossmo, D. K., Hassan, A. N., Fuller, D. O., & Beier, J. C. (2011). Geographic profrling as a novel spatial tool for targeting infectious disease control. International Journal of Health Geographics, 10,35-42. Martin, R. 4., Rossmo, D. K., & Hammerschlag, N. (2009). Hunting pattems and geographic profiling of white shark predation. Journal of Zoolo gt, 2 7 9, 1 1 1 - 1 1 8. Raine, N. E., Rossmo, D. K., & Le Comber, S. C. (2009). Geographic profiling applied to testing models of bumble-bee foraging. Journal of the Royal Society Interface,6,307-319. Rossmo, D. K. (1993). Target patterns of serial murderers: A methodological model. American Journal of Criminal Justice, 17(2),1-2I. Rossmo, D. K. (1994). A primer on criminal geographic targeting. IALEIA Journal,9(l),l-12. Rossmo, D. K. (2005). Geographic heuristics or shortcuts to failure?: Response to Snook et al. Applied Cognitive Psycholog,,, I 9, 651-654.
Rossmo, D. K. (2005). Geographical profiling. Forze civili: Dialogo cultura per la legalita [Official Review of the Italian Police Offrcers Association], 3(1), 13-16. Rossmo, D. K. (2006). Criminal investigative failures: Avoiding the pitfalls. FBI Law Enþrcement Bulletin, 7 5 (9), | -8. Rossmo, D. K. (2006). Criminal investigative failures: Avoiding the pitfalls (Part two). FBI Law Enforcement Bulletin, 7 5 (10), 12-19. Rossmo, D. K. (2011). Evaluating geographic prof,rling. Crime Mapping: A Journal of Research and Practice,3,42-65. Rossmo, D. K. (2011). A reality response to Bridges' "A structured geospatial analytic method and pedagogy for the intelligence community." IALEIA Journal,20(l),91-106. Rossmo, D.K. (2012). Recent developments in geographic prohling. Policing: A Journal of Policy and Practice, 6, 144-1 50. Rossmo, D. K., & Harries, K. D. (2011). The geospatial structure of terrorist cells. Justice Quarterly, 28, 221 -248. Rossmo, D.K., & Routledge, R. (1990). Estimating the size of criminal populations. Journal of Quantitative Criminolo g!, 6, 293 -3 14. Rossmo, D. K., & Saville, G. J. (1991). Policing Challenge 2000: Riding the winds of change. Canadian Journal of Criminology, 33,543-549. Rossmo, D. K., Thurman, Q. C., Jamieson, J. D., & Egan, K. (2008). Geographic pattems and profiling of illegal crossings of the southern U.S. border. Security Journal, 21,29-57 . Schmitz, P., Cooper, 4., de Jong, T., & Rossmo, K. (submitted for publication). Mapping criminal activity space using cellular (mobile) telephone data. Professional Geographer. Stevenson, M. D., Rossmo, D. K., Knell, R. J., & Le Comber, S. C. (2012). Geographic profiling as a novel spatial tool for targeting the control of invasive species. Ecography, 35. Taylor, 8., Brooks, J., Phanidis, J., & Rossmo, D. K. (1991). Services for Vancouver street youth: An integrated delivery model. Journal of Child and Youth Care,6(3),49-61.
Non-Referred Articles Boyd, N., & Rossmo, D. K. (1994, February). David Milgaard, the Supreme Court and Section 690: A wrongful conviction revisited. Canadian Lawyer,pp.28-29,32. Fleming, 2., & Rossmo, D. K. (1996). Optimizing patrol resources: Vancouver's 4lll íeam model. RCMP Gazette, 5 8(6), 2-9. Rossmo, D. K. (1991). After the use of deadly force: What you should be aware of. Blue Line Magazine, 3(4),19-20. Rossmo, D. K. (1999, March). Geographic profiling system helps catch criminals. GeoWorld,p. 41. Rossmo, D. K. (2009, October). Failures in criminal investigation. The Police Chief pp.54-66. Rossmo, D. K. (2010). Criminal investigative failures. RCMP Gazette, T2(l),30-31. Rossmo, D. K., & Davies, A. (2001). Stealth predator pattems. Crime Mapping News, 3(4),6-7. Rossmo, D. K., & Filer, S. (2005). Analysis versus guesswork: The case for professional geographic profiling. Blue Line Magazine, l7(7),24-25. Rossmo, D. K., Filer, S., & Sesely, C. (2005). Geographic profiling debate - round four: The big problem with Bennell, Snook and Taylor's research. Blue Line Magazine, 17(9),28-29. Rossmo, D. K., & Fisher, D. K. (1993). Problem-Oriented policing: A cooperative approach in Mount Pleasant, Vancouver. RCMP Gazette, J5(1), 1-9.
Reports Boyd, N., & Rossmo, D. K. (1992). Milgaardv. The Queen: Finding justice - Problems and process. Burnaby, BC: Criminology Research Centre, Simon Fraser University. Brown, R. O., Rossmo, D. K., Sisak, T., Trahern, R., Jarret, J., & Hanson, J. (2005). Geographic profiling military capabilities. Final report submitted to the Topographic Engineering Center, Department of the Army, Fort Belvoir, VA. Hotel, C, & Rossmo, D. K. (1993). Creating a safer Vancouver: Safer City Task Force questionnaire results. In Safer City Task Force: Final report (pp.3a9476). Vancouver: Author. Rossmo, D. K. (2005). Geographic Profilingfor Military Applications: Report on Sofh,vare Evaluation for the National Technology Alliance (NTA). Rossmo, D. K. (2008). Geographic profilingfor military applications. Final report submitted to the Topographic Engineering Center, Department of the Army, Fort Belvoir, VA. Rossmo, D. K. (2009). Criminal hunting paths: An analysis of the spatial behavior of recidivists. Final report submitted to the Bureau of Justice Assistance, Office of Justice Programs. Rossmo, D. K., Davies, A., & Patrick, M. (2004). Exploring the geo-demographic and distance relationships between stranger rapists and their offinces (Special Interest Series: Paper 16). London: Research, Development and Statistics Directorate, Home Office. Rossmo, D. K., Thurman, Q. C., & Jamieson, J. D. (2005). Geographic patterns and profiling of illegal crossings of the southern U.S. border. Office of Science and Technology, National Institute of Justice (NIJ). Rossmo, D. K., &,Egan, K. (2007). Illegal U.S. land border crossings by American citizens. Bureau of Justice Assistance, Office of Justice Programs.
Book Reviews Rossmo, D. K. (1993). fReview of Violence and public anxiety: A Canadian casel. Culture, I3(2),109-1 10. Rossmo, D. K. (2006). fReview of Policing illegal drug markets: Geographic approaches to crime reduction]. Canadian Journal of Criminologt and Criminal Justice,4S(6),943.
Other Rossmo, D. K. (1993). Strategic crime patteming: Problem-Oriented policing and displacement. In C. R. Block & M. Dabdoub (Eds.), Worl