Kim Rossmo Report - Amanda Ladas Vs. Apple Case

Supporting documents filed with Amanda Ladas's lawsuit against Apple include extensive reports from four leading experts in digital forensics examination, information security, networking and systems administration, geographic profiling and clinical and forensic psychology.
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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). g oo¡oå ¡son St ooõ Cormoranl Sl i.. *t ¡ :': tsando¡a ye õ ã Pan a a a a a soe St a a a oo a I aa . oa e s, .lD to 1.. t.o a ¿ ¡l . ¡ .t. a t. . ' ¡,. vtew S Q¡ oa Vrclona HarÞour . /ì,laler Aerodrome 4 ^i gr ò tZ' I . ro a .t.t a ? h I Figure 1. Measures of geographic central tendency for WiFi sites. q r h.*4, àa Ande¡¡on Pa¡k G*æF46 a : + .f rd¡æ¡ve t, a Rd l¡ a a Commmity Grrden a tllb€ri Sr B-fsr d + )$ så ù € (J u) t'Ð ó ÞanrbraAve Johnh" t I a af Fairf D el(a a Figure 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). I I úì Ð ¡ lrl a 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 ( 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. an -lat Fle kft h ToaÈ lleþ Nqlf,ilEq:;Ileo1+èrl^tr li¡iHe !qi\ sI"DiTiFld |;;;ùß;--lj,",¡t, ?"",4.¿iu+''[ìñil] f.líir ti ì 4 [email protected]'áð j'J0 Ordnance 5urvey GCrown Copyright. All rights reserued GeoÏreô softw¿re uæd courtesy of Oculus lnfo lnc All Geo-ìire rights lesrvêd. 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 *4^* u U a LF . o^ a o o a n O\ \n nh^l ^" I I o AOAa 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 Apple Press Info. (2017, April2T). Apple Q&A on location data. Retieved July 30,2012 from the World V/ide Web: Data.html. Brantingham, P. J., & Brantingham, P. L. (Eds.). (1981). Environmental uiminology. Beverly Hills: Sage. Brantingham, P. J., & Brantingham, P. L. (1984). Patterns in crime. New York: Macmillan. Clarke, R. V,, & Felson, M. (Eds.). (1993). 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London: Routledge. Grat F. (2012). iOS4x location BC. Grengs, J., 'Wang, based services analysis report. Unpublished report. Vancouver, X., & Kostyniuk, L. (2003). Using GPS data to understand driving behavior. Journal of Urban Technolo gy, I 5 (2), 33-53. Henry, A. (2012, September 4). 1 million Apple device IDs leaked, 12 million total stolen. Lifehacker. Retrieved September 5, 2012 from the V/orld Wide V/eb: Jakle, J. 4., Brunn, S., & Roseman, C. C. (1976). Human spatial behavior: A social geography. IL: 'Waveland Press. Prospect Heights, Laube, P., Dennis, T., Forer, P., & Walker, M. (2007). Movement beyond the snapshot - Dynamic analysis of geospatial lifelines. Computers, Environment and Urban Systems,3I, 481-s01 . Maltais, M. (2012, March 20). Asking job applicants for Facebook password, fair or legal? Los Angeles Times. Retrieved JuIy 25,2012 from the World V/ide Web: facebook-20120320. Maplnfo (2004). Technology Provider Opportunities with Maplnþ in Insurance. Retrieved July 25,2012 from the World Wide Web: portunties.pdf. Mark, D. M. (1998). Geospatial lifelines. Integrating spatial and temporal databases, 9847 Vol. L Dagstuhl Seminars. 28 Ouimet, M., & Proulx, J. (1994, November). Spatial and temporal behaviour of pedophiles: Their clinical usefulness as to the relapse prevention model. Paper presented at the meeting of the American Society of Criminology, Miami, FL. Rossmo, D. K. (2000). Geographic profiling. Boca Raton, FL: CRC Press. 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 (çry. 16-42). London: Routledge. Schmitz, P. M. U., Rossmo, D. K., de Jong, T., & Cooper, A. (2007, March). Determining criminal activity space using mobile phone technology. Paper presented at the NIJ Mapping and Public Safety Conference, Pittsburgh, PA. Schwartz, J. (2008). Cellphone tracking study shows we're creatures of habit. The New York Times. Retrieved August 22, 2012 from the World Wide Web: Sistla, A. P., Wolfson, O., Chamberlain, S., & Dao, S. (1997). Modeling and querying moving objects. Proceedings of the l3th International Conference on Data Engineering (ICDEI3) (pp.422-432). Birmingham, UK: University of Birmingham. Smith, E. (2010). iPhone applications & privacy issues: An analysis of application transmission of iPhone Unique Device ldentifiers (UDIDs). Unpublished report. Lewisburg, PA. Smith, E. (2012). iOS4x operating system privacy issues: An analysis of data transmitted from an Apple device to the Apple Corporation. Unpublished report. Lewisburg, PA. Stem, J. (2012, March 26). Demanding Facebook passwords may break law, say senators. IBC Web: World News. Retrieved August 24, 2012 from the World V/ide 29 " t- Taylor, P. J. (1977). Quantitative methods in geograp,hy. Prospect Heights, IL: Waveland Press. UK National Statistics Online. (2003). The UK 2000 Time Use Survey. Retrieved July 25,20O4 from the World Wide V/eb: U.S. should prosecute JetBlue, privacy group says. (September 22,2003). USA Today. Retrieved August 24, 2012 from the World Wide Web: Walmsley, D. J., Longman. & Lewis, G. J. (1984). Human geography: Behavioural approaches. London: Wolfson, O., & Mena, E. (2005). Applications of moving objects databases. In Y. Manolopoulos, A. Papadopoulos, & M. Vassilakopoulos (Eds.), Spatial databases: technologies, techniques and trends (pp. 186-203). Hershey, PA: Idea Group Publishing. 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