Expansive
fictional depictions of prolific atypical murderers sustain our collective fascination
during their near absence from police blotters (DeSpirito, 2016), permeating
all aspects of society while appearing in cultural conversations centered on artists
(Caldwell, 2016; Ellwood, 2015; Wolcott, 2016), comedians (Everett, 2012;
Kimble, 2015) and politicians (Tiffany, 2016). We have become a nation of #Fannibals
(Hall, 2013), satiated by the deeds of what are now known as multiple-event
murderers (MeMs) (Aamodt & Yaksic, 2015; Yaksic, 2015). Although Warren,
Dietz, and Hazelwood (2013) surmise that these atypical murderers do not alter
behaviors after learning from public sources, some believe them to become more
adept at crime through this exposure. The utilization of multiple murderers as
educational devices to entice students reinforces ingrained myths and damages
efforts to dismantle them. Why We Love
Serial Killers, a recent effort to co-mingle entertainment media with
academic insight, resonated as a vapid attempt at garnering fame and
profitability (Yaksic, 2014a). This chapter examines deficits exposed during systematic
analysis of past work in homicide research (Yaksic, 2015). Detection of the modern
day MeM requires forward propulsion stimulated by “anti-disciplinary” thinking and
adapting concepts from disparate arenas into the criminological perspective.
Detecting
the Devil in the Details
A
recent Boston Magazine article titled Profiler
2.0 (Halber, 2016) redefined how criminal justice practitioners are
expected to operate in a world saturated with data. We have remained entrenched
in arguments about definitions and exclusion criteria in the domain of atypical
homicide research (Yaksic, 2015) while efforts to create typologies of
different killers are called a popular tactic (Shanafelt & Pino, 2013) and unhelpful
in solving crimes (Mark Safarik, personal communication, 20 May 2016). Trepidation
in advancing beyond descriptive statistics may be a strategic maneuver, one permitting
us to avoid operationalizing data. Studying all criminal offenders with
mainstream theory and methods (DeLisi, 2015) is met with apprehension as
scrutiny of probabilistic genotyping and predictive algorithms – advancements
recently applied to the criminal justice system – (Angwin, Larson, Mattu &
Kirchner, 2016; Davey, 2016; Shaer, 2016) continues. Many relationships have
been frayed in the struggle to build databases cataloging atypical homicide
offenders. Comparably, a Pulitzer Prize winning journalist found himself at
odds with colleagues after claiming credit for amassing data on the subject of
fatal shootings by police even though similar databases had been built (Swaine,
2016).
Today,
much of the criminal justice field has accelerated their effective use of data,
evidenced by the National Institute of Justice employment posting for a
‘groundbreaking crime statistics analysis initiative’ (McGough, 2016), the
existence of forecasting tools such as PredPol and HunchLab (Chammah &
Hansen, 2016) and the Law Enforcement Advancing Data and Science Program
(National Institute of Justice, 2016). Still, access to data with appropriate
sample size and statistical power is the primary impediment to generating
models meant to answer empirical questions about atypical homicide. Data can
help determine if commonplace offenders invested in a criminal career behave
like serial killers while interspersing murder into a range of offenses. Employing
the Radford Serial Killer Database (SKDB) – consisting of 4,274 records across 170
variables (Boyne, 2014) – may aid in resolving queries about the
differences between atypical murderers and ordinary criminals experimenting
with duality (DeLisi, 2015).
Foraging
for the Furtive Few
Some
are confronting the false promises of “Big Data” even though exploiting this
approach evokes a sense of boundlessness. Yaksic (2015) has proposed creating a
dashboard that would apply data mining techniques to the task of identifying
discernable patterns in the SKDB data but there may be danger inherent in surrendering
ourselves over to a world run by algorithms (Davey, 2016). The aspirations of
those interested in weaponizing data to combat the scourge of missing persons[1]
(Joshua Zeman, personal communication, 14 May 2016) and unsolved homicides (Michelle
Katz, personal communication, 18 May 2016; Sacha Thorpe, personal communication,
19 May 2016) currently outweigh the capabilities of databases because basic details
within them, such as the manner in which offenders are apprehended, are often
missing. This limitation fosters a reliance on findings from studies that
either disregard subgroups within the population of atypical murderers
(Kurkjian, 2016) or focus on the broad category of sexual murderers (Beauregard
& Martineau, 2016). In response, atypical homicide researchers created an
incubator designed to address serial offending (Office of the University
Registrar, 2016) and formed a collaborative enterprise with the Homicide
Investigation Tracking System (HITS) of the Washington State Attorney General's
Office (Tamara Matheny, personal communication, 22 July 2016) and members of
the Federal Bureau of Investigation’s (FBI) Behavioral Analysis Unit (BAU) 5 aiming
to use research, strategy, and instruction to close the missing data gap (Sarah
Craun, personal communication, 2 March 2016).
The
culmination of interventions – police investigations, actions taken by the
victim, or the involvement of witnesses (White, Lester, Gentile &
Rosenbleeth, 2011) – result in the apprehension of MeMs but they sometimes reveal
themselves via confessions or miscalculations. Murders can be linked by happenstance
as in the “Black Widow” killings where one detective overheard another talking
about a similar case (Associated Press, 2008). The transcript of serial
murderer James Childers’ statements (United States Department of Justice, 2009) details a decision
making process confirming that small choices lead to unanticipated paths
(Shanafelt & Pino, 2013). Most serial homicides contain incidents occurring
independent of the offender’s ingenuity: escaping capture by way of external
situational factors beyond their control – known in common parlance as a ‘lucky
break’. These episodes amount to unwarranted political involvement (Yaksic,
2014b), lapses in investigative strategies due to erroneous thinking (Kramer, 2016) or corruption
that prevents official inquiry (Schram, Algar, & Tacopino, 2015).
Multiple-event
Murderers as Human Beings: Exploiting Resilience and Collapse
Most
of the work done in breaking serial killer stereotypes (Kuhn & Coston,
2005; Walters, Drislane, Patrick, & Hickey, 2015) stops short of
considering atypical homicide offenders as individuals operating within a set
of ‘normal aberrations’, shared cultural and psychological processes or in
terms of what motivate our actions as people (Shanafelt & Pino, 2013;
Yaksic, 2013). The portrayal of atypical murderers as monsters imbues offenders
with fantastical powers, convincing them that they are advanced predators with
exceptional abilities and expertise (Wiest, 2016). Adapting the conceptual
framework provided by the ‘9 Principles’ (Copeland, 2012) summarizes
how atypical murderers remain adaptable in changing terrain. By allowing
failure to encourage adjustments in routines, not planning for everything in
the beginning, taking risks instead of focusing on safety, fully embracing
unexpected changes and events, being imaginative, practicing over theorizing,
choosing disobedience over compliance, disallowing authority to limit potential
and keeping vision and thinking open and flexible, MeMs can remain resilient
and successful. It can be argued that a failure to abide by these principles
has contributed to a decline in instances of serial homicide (DeSpirito, 2016;
Yaksic, 2015).
Resiliency
is ignored during debates about what makes MeMs successful. Because network
science can provide a framework for capturing regularities in atypical homicide
(Marcos Oliveira, personal communication, 23 June 2016), we can use this
concept to conceive the tipping point between offender resiliency and collapse.
Modifying the ideas posited by complex network researchers Gao, Barzel, &
Barabási (2016) can help us understand how enterprising killers calibrate their
actions to maintain base level functionality when environmental changes occur. Thinking
about atypical homicide offenders as multi-dimensional systems consisting of a
large number of components interacting through a complex network and adjusting
to disturbances in order to remain functional is certainly a paradigm shift.
Doing so could allow the prediction of the atypical murderer’s collapse under
the weight of errors and internal failures.
The
Utility of Criminal Investigative Analysis (CIA) in the 21st Century
What
is being done today to uncover undetected, offending MeMs? Discussions about
the methods utilized to discover MeMs are often superseded by endeavors to reveal
the genetic outlier or negative experience thought to help aid in discovering
other such murderers early on in their maturation (Parshley, 2016). The
development of processes meant to assist in apprehending these offenders is a
popular focal point due to their perceived dangerousness. CIA is often favored as
an investigative suite of tools over other conventional techniques – such as
linkage analysis programs like Crime Linkage International NetworK (2016) or
the use of Bayesian modeling (de Zoete, Sjerps, Lagnado, & Fenton, 2015) – due
to the lore surrounding the FBI’s success rate with the program. Shanafelt and
Pino (2013) astutely call use of CIA inadvisable while Branson (2013) outright
accuses the FBI of creating a matrix devoid of any social or cultural context while
they revel in the “magic of the methodology”. Some are inspired by Agent
Cooper’s mystical Tibetan Method from the television program Twin Peaks (Blassmann, 1999) and imprudently
wield profiling as an armament in a battle of good versus evil (Mains, 2015).
The
divide between perceptions and the practical use of CIA was evidenced by Yaksic
(The Practicality of CIA in the 21st Century, Survey, 14 March, 2005) in a
survey purposed at ascertaining CIA’s further usefulness. While each respondent
understood that the perspectives surrounding the definition of CIA vary widely
(Scherer & Jarvis, 2014) and knew it to be a
useful contrivance, researchers felt that CIA has not adapted to the modern age
and is based on an outdated dataset while the inverse was believed by the
profilers. These rifts contribute to problems where faulty views and
disparaging tones lead to research gaps and limit our scientific understanding
of atypical offenders. Northeastern University’s Atypical Homicide Research
Group (NUAHRG)[2]
was formed to accommodate all viewpoints and reduce the strain of dichotomous
relationships among law enforcement professionals and researchers. DeLisi
(2015) categorizes these opinions as errors of social construction – a failure
to acknowledge that serial murder is an area deserving of study by serious
scholars – and stagnancy – a lack of innovation in scholarly thought – and
recommends reclaiming this area of inquiry from true crime authors.
The
advent of the serial murder entertainment industry has ushered criminal
profiling into a sacred space as the cornerstone of forensic science courses. Academic
institutions capitalize on university students convinced that becoming a
“criminal profiler” is an immediately attainable goal. Although the objective
of these programs is to instill a sense of wonderment regardless of how
implausible the course, students should be actively discouraged from pursing ‘profiling’
as a career choice due to the abysmal state of grant funding dedicated towards
this area of study (Angela Williamson, personal communication, 15 April 2016).
The work of Muller (2000) might inspire students to redirect their energy
towards other impactful fields of inquiry. Because individuals explain atypical
homicide in the terms related to their profession (Shanafelt & Pino, 2013) they
can tangentially connect their work to the pursuit of atypical homicide
offenders. Environmental scientist Nigel Raine used geographic profiling to
look at patterns of bee foraging and perfect the technique (Raine, Rossmo &
Le Comber, 2009), electrical engineers Simkin and Roychowdhury (2014) suggest that the likelihood
of successive killings is higher soon after a murder than after a long period
has passed, physicist Neil Johnson developed a statistical model aimed at
identifying behavioral patterns among online supporters of ISIS and used this
information to predict the onset of violent events (Johnson, Zheng, Vorobyeva,
Gabriel, Qi, Velasquez, Manrique, Johnson, Restrepo, Song, &
Wuchty, 2016), Allely (2016) asks if
we
can predict who will become a mass shooter and the hacker Jester uses IBM's
Watson AI to create a monitoring tool, called iAWACS, to analyze a user's
"trustworthiness, propensity toward violence [and] openness" (Fung,
2016). The following vignettes
comprise efforts by citizen sleuths, journalists, engineers and information
technology professionals to create MeM detection tools and use novel approaches
to understand how they interact with the world around them.
Utilizing
New Tools to Locate MeMs in the Information Age
These
ingenious efforts avoid preoccupation with apprehending MeMs so that attention
can be redirected to detecting their presence using pattern recognition on characteristics
of the victim and offender. The world of data science is fertile ground for folks
outside of criminology to make an impact, especially as technical advances
outpace practitioner’s understanding. Several projects underway heed Leetaru’s
(2016) warning that the big data revolution is being driven by computer science
with the majority of sentiment mining tools coming from that area rather than
the disciplinary fields whose questions they are attempting to answer. Each platform
detailed herein asks ‘where are atypical murderers hiding?’ as their originators
were confounded by the findings of Beauregard and Martineau (2016) stating that
strategies used by organized sexual murderers may increase the likelihood of
detection by police. These architects considered a former FBI BAU 2 Unit Chief’s
view that serial murder research has not “identified any radical new ideas over
the past few years”[3]
to be unsatisfactory and deemed current methodologies to be insufficient for
prospectively locating MeMs. Each has committed to gathering data, designing
algorithms and combining the efforts of those in the world of data science and
engineering with criminal justice practitioners, an approach referred to as translational
criminology or research with the potential for real-world implementation (Laub,
2012). Their guiding research question required supporting one of two schools
of thought about the prevalence of multiple murderers where either hundreds of
serial killers are actively trolling on an unending search for victims or their
presence among the ranks of MeMs is steadily declining (Yaksic, 2015). These platforms
were designed under the assumption that, aside from those cataloged in the SKDB
as captured each year, a great number of active atypical murderers are unaccounted
for in the United States.
The
Murder Accountability Project (MAP) is an outgrowth of a 2010 national
reporting project conducted by Thomas Hargrove at the E.W. Scripps Co. that
aimed to investigate the declining homicide clearance rates in the United
States (Crawley, 2015). MAP seeks to improve public
awareness of the problem of unsolved homicides and identified bodies. Accurately
accounting for unsolved homicides and making FBI murder data more widely and
easily available to police, journalists and the general public is of importance
as more than 216,000 homicides have not been solved since 1980.
MAP seeks to improve the quality of reporting by police by using Freedom of
Information laws to obtain information from state and local governments about
unsolved homicides that many major police departments decline to supply to the FBI’s
voluntary Uniform Crime Report (UCR) and Supplementary Homicide Report (SHR).
During
the national reporting project, Hargrove created
the Serial Killer Detector, a computer algorithm that flags potential serial
killings within the FBI’s SHR (Editor & Publisher, 2010). This tool is now accessible
on MAP’s interactive website (Hargrove, 2015) where the user can consider if a serial killer may be active within a
specific community. The
Scripps study identified an alarming number of unsolved killings of women in
161 clusters nationwide involving 1,247 deaths of women of similar age who were
killed by similar means. Serial murderers are known to operate in a tight
geographic area, selecting, killing and disposing of their victims within this
zone. The
SHR data available on the "Search Cases" tab of the MAP
website can be helpful to homicide investigators testing their theories about
homicide suspects who may have killed across multiple jurisdictions or
within the same jurisdiction over time.
Hargrove
used multivariate analysis on data reported to the FBI to calculate that at
least one multiple-victim killer had operated in Indiana (Hargrove, 2014). Hargrove
provided the names of fifteen potential serial homicide victims to the chief of
police in a letter four years before serial killer Darren Vann was arrested. So
far, one killing on the list is being investigated as part of Vann’s series. Next,
a trend was spotted on the MAP website among the FBI’s case-level homicide data by selecting the “Search
Cases” tab, “Atlanta” under metro areas, “Female” under victim’s sex and
“strangulation” under weapon – indicating
that strangulation deaths of African American women occurred in Atlanta until the
pattern abated in 2007. Hargrove turned to the NUAHRG to
introduce the existence of this series to a member of the BAU. An analyst at the
FBI’s Violent Criminal Apprehension Program (ViCAP) is reviewing the findings[4].
Rebecca
Kramer and other citizen sleuths suspect that it may be advantageous to
manually collect homicide event data. They understand that reliance on
government information obtained from the noncompulsory FBI UCR/SHR may have
limited the scope of MAP’s objectives. Kramer set out to investigate the
ostensible murder of scores of college aged men in or near bodies of water in
the hopes of identifying a pattern in the data. Kramer has persisted in
collecting information on 400 instances of this phenomenon despite general
opinion that these stories cannot be substantiated. The working theory, that
men are being drugged at bars or lured to bodies of water whereby they are
drowned and left at the homicide scene by at least one male or female serial
murderer, can be investigated using advanced mapping technologies and
sophisticated analytical approaches in Risk Terrain Modeling (RTM). Because the
risk of crime is both place-based and situational – since victims often
encounter offenders because of the activities they pursue and the environments
they occupy – it is important to take note of the clustering of environmental
risk factors and their spatial influences (Caplan & Kennedy, 2016).
RTM
is a diagnostic technique that accounts for the different factors contributing
to the spatial and temporal dynamics of illegal behavior, identifies the risks
inherent in features of a landscape and models how they co-locate to create
unique behavior settings for crime. Here, to assess
the possibility that these drowning deaths are homicides, the co-location of
bars and water was explored as contributing to conditions suitable for the
victimization of unwitting men. The interaction effect can be used to compute a probability of
criminal behavior occurring in the future. Because the data
points within these programs are human beings moving in structured environments,
it is critical to consider spatial factors of the topography as influential and
enabling to criminal behavior. Swarm Intelligence – the idea that humans
perform actions based on cues taken from each other (Forman, 2016) – should be
surveyed in conjunction with RTM. Swarms are thought to occur as one event but
they can be a series of people visiting a location over time, each making
decisions based on physical cues left by those who have been there before. Marrying
RTM and Swarm Intelligence makes logical sense as criminals operating
independently can target the same places, evident in South Los Angeles where
the bodies of 55 victims of five serial killers were located between 1984 and
2007 (Los Angeles Times, 2010).
Citizen
sleuths like Kramer can be a productive member of the investigative team (Halber,
2013; Peters, 2013) but are often overlooked as a resource. Resultantly, Kramer
turned to the NUAHRG to conduct outreach to secure cooperation from law
enforcement and refine the project’s inclusion criteria. Modifications to the
data collection procedures would require re-reviewing thousands of online
records but, as a benefit to Kramer’s ongoing efforts, data scientist Peter
Brendt is creating a method to collect and collate records from millions of
news websites simultaneously. Brendt (2016) writes that previous efforts to
connect human investigators with computers meant to aid in solving crimes have
failed due to the idiosyncrasies inherent within both systems. The FBI’s ViCAP,
the most widely available resource for those investigating potential linkages
across crimes, is an application predicated on the idea that entry of all
homicides or sexual assaults is not necessary for the program to be viable (United States Department of
Justice, 2016). FBI analysts request that “cases of a serial nature” are
prioritized for entry into ViCAP, providing autonomy to local law enforcement
officers to select cases for upload to the platform. The onus to determine,
prima facie, the likelihood that a case appears to be a part of series is a
major limiting factor as it falls to those with little or no experience working
a series of murders. ViCAP represents a true intersection of Brendt’s (2016)
point that humans are hampered by how they react to a lack of resources, political
affiliations, jurisdictional conflicts and distrust of others while computers
are limited by data models not designed for complex comparisons, lack of
granularity for attribute comparisons and variable weights of attributes of
crimes that change by area, cultural predominance and victimology.
Brendt
is developing a “knowledge-based” system with the LAMP software bundle of the Linux
operating system,
Apache HTTP Server,
MySQL relational
database management system (RDBMS), and PHP
programming language
designed to search for intersections in unidentified bodies, unsolved homicides
and missing persons datasets. This ‘Weather Map’ may identify linkages using website
screen scraping, data corroboration, deep learning and automatization. Brendt
is motivated by the belief that these data constitute “hallmarks of a serial killer”
and that their victims lay somewhere within it. The ambitious scope of this homicide
offender forecasting tool would require working with incomplete and overlapping
data of differing granularity, thousands of man-hours, top shelf hardware and
algorithms “on the edge of technology”.
Brendt
stresses that for this system to work, it would need to be as adaptable as atypical
murderers have been and change along with their evolution. Current convention
surrounding definitions must be ignored because, as Brendt states, “a serial
killer can become a spree killer during his endgame”. The amount of data needed
for the project would increase perpetually along with the demand for its
quality because datasets become more limited, volatile and uncontrollable as
they swell in size. The software system is being built modularly due to the
necessary combination of techniques with parts dedicated to data entry,
analysis and automatic data reading. While a full productive system is slated
for future release, twenty potential serial homicide series have been detected by
the ‘Weather Map’ across the United States to date.
Detecting
MeMs using the ‘Weather Map’ will locate only those offenders that are well
into their careers and kill frequently. Highly mobile serial murderers (HMSM) and
burgeoning offenders are beyond the bounds of search parameters as they spread
their victims outside of the time and geographical horizons. Further
development of the recognition patterns must be completed to make the rulesets
more logical in order to surpass the spatial and chronological restrictions. Because
Donoho (2015) posits that coping with large scale cluster computing is
hampering our ability to make appropriate judgements and holds us back from
data analysis strategies that we would otherwise eagerly pursue, Joseph Johaneman,
a student dual majoring in data analytics and applied mathematics, plans to
build on Brendt’s work by constructing a small computing cluster from single-board
computers (SBC) with the aim of locating homicide victims of HMSM (i.e. long
haul truckers). Patterns in the SKDB will be used to train the cluster and
design a mechanism to track HMSMs by creating a probabilistic function on the
central hub of their movement based on multiple drop sites, hotzones and victim
locations. The Manhattan Distance formula and buffer zones used in Kim Rossmo’s
geographic profiling tool (Rossmo, 1995) are replaced by Mahalanobis Distance,
the space between two probability distribution sets. Anchor points are used and
based in the space and time of a trucker’s routines along established routes.
Incorporating a temporal element, the constant range of the semi-trailer truck’s speed and assigning weights to
favored body dump locations to ascertain their selection probability will
enhance current strategies. SBCs allow for good performance per watt, the
ability to conduct deep learning activities essential to the project and for
police departments and newspapers to build their own systems due to their low
cost (Baun, 2016). Johaneman will address whether the isolation and anonymity
of driving invites the violence-prone (Strand, 2016) and provide
what he learns to the FBI’s ongoing Highway Serial Killings Initiative (HSKI),
a program implemented to attribute more than 500 murder victims from along
highways to some 200 potential suspects.
Oklahoma
State Bureau of Investigation Intelligence Analyst Terri Turner, the
inspiration for HSKI, arduously identified a series of seven homicides in late
2003 which victimized truck stop prostitutes and encompassed multiple states
and jurisdictions. She coordinated case information between all the agencies
involved and monitored law enforcement and open source information for new
incidents. Turner partnered with ViCAP and the BAU to facilitate the exchange
of case information and provide investigators with “best practices” in working
these cases. Johaneman hopes to use his work to identify future killer truckers
like Bruce Mendenhall or Adam Leroy Lane before they are able to
wantonly victimize others.
The
Importance of Debunking Ingrained Myths
The
search for atypical murderers among the incarcerated may seem counterintuitive but
many unidentified victims result from an offender’s self-imposed silence. Angela
Williamson plans to obtain the DNA of HMSMs and enter those genetic details
into the Combined DNA Index System (CODIS) (Angela
Williamson, personal communication, 15 April 2016) to retrospectively match
offenders to victim’s remains (White et al., 2011), a method used to no avail during
the East Area Rapist-Original Nightstalker (EAR-ONS) investigation. Many of the
legacy axioms included in the EAR-ONS criminal profile (D’Ambrosia, 1998), such
as those surrounding race and gender, must be controverted to approach this
project without cognitive bias. The stereotype of the Caucasian male serial
killer allowed groups of offenders to continue victimizing others as African
American serial murderers have historically been discounted by criminologists who
thought them incapable of enduring through a career of serial murder. Yaksic
(2006) normalized the notion that African American MeMs are as abundant as
their Caucasian counterparts while Farrell, Keppel, and Titterington (2011)
demonstrated the differences in characteristics between male offenders and their
underestimated female equivalents.
White
et al. (2011) and Hickey (2014) call for the study of offenders that kill less
than three victims since they are excluded from databases. Offenders
that spread their victims outside of the capabilities of the ‘Weather
Map’s’ time horizon could theoretically continue to evade detection (Brendt, 2016). SKDB data indicate that the majority of two-victim
offenders kill for financial gain while those killing three or more victims normally
do so for enjoyment. The severity of the offender's crimes concomitantly increases
as their victim count rises. The odds of two-victim offenders committing a
third murder escalates if they are white, use multiple methods, kill for
enjoyment and commit the first two homicides close together in time.
Limitations
of Programs on the Edge of Tomorrow
Each
of these initiatives is informed by basic rulesets established from cataloged knowledge
about how MeMs select and kill their victims. The assumption that atypical murderers
abide by patterns, namely those where victims are clustered within a geographic
location, is at the foundation of each program. Consideration should be given
to findings that indicate differences between the planning behaviors of
American and South African serial murderers (Sorochinski, Salfati, & Labuschagne,
2015) and that intervals between successive killings may be predictable (Lange,
1999). Relying solely on the presence of tightly grouped victim caches would
make the task of MAP unsound but the algorithm also accounts for offenders
using the same kill method over their series, a fairly consistent measure (Hargrove, Witzig, Icove, Harry, Arntfield,
Yaksic, Lang, & Wolf, 2016).
Kramer may be overestimating the prevalence of the serial murderer in modern
society but some researchers subscribe to the belief that virtually anyone can
emerge as a perpetrator of atrocities given the "right" combination
of factors (Shanafelt & Pino, 2015). This logic dictates that large swaths
of recovered bodies could be indicative of an unsolved serial homicide series.
Yaksic (2015) argues that the phenomenon is declining, partially due to the attention
that theories about MeMs receive before more logical possibilities are
considered. Feedback from the NUAHRG highlights our preoccupation “with serial
killers, which in reality are very rare”[5]. Because
extraneous factors may prevent HMSMs from frequenting the same locations, Johaneman
may encounter issues identifying the truck stops that drivers prefer.
Researchers may be ‘censoring’ the data on offenders that claim two victims by
failing to consider their burgeoning status and the further victims their
discovery precluded them from amassing, disallowing their chance to evolve into
a “traditional” serial murderer. Williamson may be unable to compel offenders
to supply their DNA profiles. Brendt’s ‘Weather Map’ platform incorporates the
ambiguous concept of escalation over a killer’s career and stipulates that
offenders do not revert to previous routines in killing method. Educating the ‘Weather
Map’ to detect patterns based in this logic will needlessly restrict the
program by not accounting for the variability inherent in the decision making
process of human beings. Premature capture always interrupts a MeM’s plans and negates
any opportunity to learn if escalation is a well-founded concept. The ‘Weather
Map’ program would overlook offenders altering their techniques dependent on
their victim type.
The
Blood Red Flag of Domestic Violence
The
subset of multiple-method murderers contains a growing population of offenders
that first practice violence on their spouse, brutalizers that choke their
wives during domestic disputes before targeting members of the public. Rehearsals
for these campaigns include instances of intimate partner violence and
culminate in cruelty towards others (Shifman & Tillet, 2015).
The precursory link between private brutality and public savagery is a crucial
part of the MeM detection labyrinth, an unacknowledged corollary due to
investment in thinking of these offenders as loners without families. Returning
home to continue their vicious attacks is unsurprising because the phenomenon
of atypical murder has been succinctly rooted in male dominance for millennia.
Serial rapists, devoid of enjoyment, frequently eliminate the only witness to
their crime as part of a scheme to avoid detection. Non-sexual serial killers
have gravitated to melding their features with characteristics of spree
offenders since aspects of the modern age have made it difficult to behave like
their past counterparts (Yaksic, 2015). Deceit can be readily identified in the
‘Age of Authenticity’ and is infrequently tolerated which forces transparency upon
atypical murderers, jeopardizing their ability to exercise the dual facets of
their personality that contribute to success. MeMs adapted to a more open world
by becoming proficient at sharing their contempt with others through outward
displays of anger and revenge.
Broadening
the scope of offenders comprising the MeM definition to include these unsparing
men requires giving motive additional consideration. Anger is not considered a
key component of the serial sexual homicide offender's motivation, according to
Myers, Husted, Safarik, and O'Toole (2006), but adding ever increasing spree
murderers (Kirby, Graham, Green, 2014) to the cohort of atypical murderers, calls
for reexamining 'toxic masculinity' (Hamblin, 2016) in this context. The recent
actions of Eulalio Tordil, (Alexander
& Bui, 2016) Mainak Sarkar (Hamilton, Watanabe, & Winton, 2016), Cedric Ford (Sanchez,
Berlinger, & Flores, 2016) and Edward Acquisto (Mettler, 2016) encapsulate the social
issues and sentiments
ongoing in American culture: male entitlement, hypermasculinity, stringent
gender roles, unabashed sexual expectations, objectification, and unchecked
rage – all coupled with flagrant use of firearms. These offenders reinforce
antiquated viewpoints on manliness, an abstraction requiring a mixture of anger
(Hayes, 2016), brute force and a domineering attitude towards others. Society
at large has taken notice of intimate partner violence of late because of the
tendency for offenders to pose a threat to others after harming or killing
their significant other.
What
we knew about atypical homicide in the ‘traditional’ sense has given way to hard-won
realizations over the past decade: more impatient and incompetent offenders are
acting out, often erratically, while leaving footprints on social media or
electronic devices (Felton, 2016; Meyjes, 2016). Criminologists must resolve
their misgivings on these changes because algorithms learn by consuming
information upon which the system builds a model of the world. If a system is
trained on faulty data, it will have a harder time generating the appropriate
answer (Crawford, 2016). These learning systems must be aware of circumstances resulting
in outcomes such as offenders killing freely due to apathetic enablers (Crimesider
Staff, 2016) and law enforcement missteps (Holman, 2016), premature captures
due to unforeseen handicaps (Alexander & Bui, 2016) or an inability to
fulfill plans because potential victims alter their routine (Hamilton et al.,
2016) so that accurate information is fed into the platform.
“One-off”
intimate partner and familial murderers sometimes exhibit what investigators
call hallmarks of serial murder activity (Truesdell,
2016) as in the killings perpetrated by Gregory Scott Hale (Radar Staff,
2014), John Robert Charlton (Carter, 2016), Blake Leibel (Hamilton, 2016), Hasib
Bin Golamrabbi (Mejia & Rocha, 2016) and the killer of Karen Perez (Harris,
2016) which involved cannibalism, dismemberment, exsanguination, scrawlings, strangulation
during rape or outward statements of idolization of other serial killers. These
cases could prove problematic for the ‘Weather Map’ as they may be scraped and
cataloged along with other serial killers as the program will assume these
victims are part of a series of murders. The ‘Weather Map’ may also capture
offenders like Vernon Primus due to his link as a person-of-interest in open
homicide cases raised speculation of his ‘serial killer’ status (Murphy, 2016).
Welcoming
Contributions from the Uninitiated
As
the nation's crime monitoring system struggles to move into the 21st century
(Rosenfeld, 2016) there are still many areas in which contributions can be made
by those adjacent to the realm of criminology. Boyd (2016) stresses shunning
the conventions of an academic infrastructure which compels the pursuit of
acceptable lines of inquiry to obtain kudos, emboldening researchers to be
aware of the context they finds themselves and to pay attention to the
incentives and pressures that influence their decisions while developing
impactful questions that matter regardless of funding battles or the fads and
obsessions of their disciplines. Leetaru (2016) calls for understanding the
nuances of data but warns that answering a question of interest may take more
than plugging a dataset into an algorithm. Because deep-learning software
recognizes patterns in digital representations of data (Hof, 2013) it can be
used to overcome noted drawbacks and unite those with institutional knowledge
about MeMs with those that understand how they actually behave. This
reconciliation would place researchers on the forefront of an indispensable
defense science for decades to come.
The
variable demands of academic life have transformed higher learning into an
unattractive pursuit for those looking for a career suffuse with field work. Defending
new science to peers can create an atmosphere of animosity and study findings may
take years to be adopted. The stress experienced by young academics was noted
as one of seven challenges facing the sciences (Belluz, Plumer, & Resnick,
2016). The restrictive path of scholarship can be overcome when the study of
atypical homicide is approached as a neophyte or outsider. Boyd (2016)
characterizes academic disciplines as myopic and judgmental of those operating
outside the acceptable boundaries and encourages those from diverse
experiential, cultural and political backgrounds to contribute. This is an area
ripe for disruption as important research findings can be made readily, such as
the discovery that most partners in
serial killer teams fit into one of three roles that aid the Principal
Offender: the Enlisted Accomplice, the Witting Facilitator and the Idle Witness
(Yaksic & Hargrove, 2016). Hester Brink (personal communication, 5 July
2016) is collecting the input of outsiders for use in
the investigation of cold cases while other research avenues focus on the
interplay between a genetic predisposition to violence and environmental
factors (Parshley, 2016) and the roles depression (Levenson, Ransom &
Crimaldi, 2016; United
States Department of Justice, 2009), emotional regulation (McGinty,
Kennedy-Hendricks, Choksy, & Barry, 2016) and mental illness (Eyster, 2012;
Gelinas & Hadjistavropoulos, 2016) play in the lives of atypical murderers.
Pioneering work is underway to deduce the likelihood that the decrease in
serial homicide is correlated with an observed increase in spree and mass
homicides. The projects outlined in this chapter have built on the work of Cai
(2015) and demonstrate the need for engagement in practices that will enable the
field to operationalize data for the social good.
Acknowledgments
This chapter could not
have been completed without the contributions of the following individuals:
Mike Aamodt, Dalal Alrajeh, Peter Brendt, Joel Caplan, Thomas Hargrove, Joseph
Johaneman, Rebecca Kramer, Ravi Shroff and Angela Williamson. This chapter is dedicated
to the memory of Michelle McNamara.
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