Evidence on the impact of the Prudential Center on crime in downtown Newark

Abstract

Objectives
Evaluate the effects that Prudential Center events had on crime in downtown Newark from 2007 to 2015 in terms of incident counts and spatial characteristics.

Methods
We evaluate the effects of events held at the Prudential Center on crime counts via negative binomial regression. Through the Fasano-Franceschini test, we assess whether crimes that occurred during events spatially differ compared to the incidents in no-event hours. Finally, we employ logistic regression to assess the correlation between crime locations and activity at the center.

Results
Five event types (out of nine) are statistically associated with increases in crime. Spatially, differences in the distribution of incidents when the facility is active partially emerge. Two out of six location types (streets and parking lots) correlate with activity at the center.

Conclusions
The complex array of crime-related effects that the center has on downtown Newark suggests tailored policies discriminating between event and location types for enhancing public safety.

Full text

Vol.:(0123456789)
Journal of Experimental Criminology (2024) 20:1225–1251
https://doi.org/10.1007/s11292-023-09576-8
1 3
Evidence ontheimpact ofthePrudential Center oncrime
indowntown Newark
GianMariaCampedelli1· EricL.Piza2 · AlexR.Piquero3· JustinKurland4
Accepted: 22 May 2023 / Published online: 15 June 2023
© The Author(s) 2023
Abstract
Objectives Evaluate the effects that Prudential Center events had on crime in
downtown Newark from 2007 to 2015 in terms of incident counts and spatial
characteristics.
Methods We evaluate the effects of events held at the Prudential Center on crime
counts via negative binomial regression. Through the Fasano-Franceschini test, we
assess whether crimes that occurred during events spatially differ compared to the
incidents in no-event hours. Finally, we employ logistic regression to assess the cor-
relation between crime locations and activity at the center.
Results Five event types (out of nine) are statistically associated with increases
in crime. Spatially, differences in the distribution of incidents when the facility is
active partially emerge. Two out of six location types (streets and parking lots) cor-
relate with activity at the center.
Conclusions The complex array of crime-related effects that the center has on
downtown Newark suggests tailored policies discriminating between event and loca-
tion types for enhancing public safety.
Keywords Crime· Super facilities· Entertainment venues· Prudential Center·
Sport
* Eric L. Piza
e.piza@northeastern.edu
1 Department ofSociology andSocial Research, University ofTrento, Trento, Italy
2 School ofCriminology andCriminal Justice, Northeastern University, 401 Churchill Hall, 360
Huntington Ave, Boston, MA, USA
3 Department ofSociology andCriminology, University ofMiami, Miami, FL, USA
4 NewYork, NY, USA
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Introduction
Environmental criminology focuses on the role that the immediate environment
plays in the performance of crime (Wortley & Townsley, 2017). This body of
research has consistently found crime patterns to be influenced by a range of
facility types, inclusive of schools (Murray & Swatt, 2013), bars (Ratcliffe, 2012),
check cashing stores (Bernasco & Block, 2011), bus stops (Loukaitou-Sideris,
1999), and railway stations (Irvin-Erickson & La Vigne, 2015). The typical facil-
ity types that are analyzed can be considered crime generators, places to which
people are attracted for reasons unrelated to criminal motivation but nonetheless
may offer increased crime opportunities (Brantingham & Brantingham, 1995).
Recent scholarship has focused on the effect of macro crime generators, very
large facilities that can accommodate many more people than the smaller facili-
ties that have typically been considered crime generators previously. Examples
of macro crime generators include amusement parks (Han etal., 2021), casinos
(Johnson & Ratcliffe, 2017), and sports arenas and stadiums (Kurland etal., 2014;
Kurland, 2019). Macro crime generators typically have a time-specific effect that
coincides with their opening/closing times and/or peak hours of operation (New-
ton, 2018). The time-specific nature of macro crime generators helps address a
common challenge associated with the cross-sectional nature of environmen-
tal criminology research. The criminogenic influence of a particular facility, or
group of facilities, is assumed (analytically at least) to be constant. Consequently,
causal inference is not entirely possible because there is no way to establish that
cause (i.e., crime generator operations) precedes effect (i.e., increased crime lev-
els). Macro crime generators, conversely, provide the necessary conditions to
circumvent this challenge as their episodic usage allows researchers to contrast
times when they are used, and large numbers of people gravitate to them, with
those days and times when no event is scheduled.
Sports arenas/stadiums are perhaps the most well-researched macro crime gen-
erator (Humphreys, 2019). Research has demonstrated that arenas are associated
with heightened levels of crime (Breetzke & Cohn, 2013; Kurland, 2014; Kur-
land etal., 2018; Marie, 2016; Menaker & Chaney, 2014; Montolio & Planells-
Struse, 2019), as well as additional negative externalities such as traffic conges-
tion, air pollution, and related negative health outcomes (Cardazzi etal., 2020;
Humphreys & Pyun, 2018; Humphreys & Ruseski, 2019), during their hours of
operation. Some studies have further demonstrated that different event types (e.g.,
sporting events and concerts) exhibit heterogeneous effects on observed crime lev-
els (Breetzke & Cohn, 2013; Yu etal., 2016; Kurland, 2019). Less understood is
whether spatial crime patterns in the surrounding area of arenas/stadiums differ
across event types. Given that recent scholarship has found the effect of crime
generators and attractors can differ across time of day (Haberman & Ratcliffe,
2015), seasons (Szkola etal., 2021), and neighborhood context (R. W. Jones &
Pridemore, 2019), we find it plausible that spatial crime patterns around sports
arenas/stadiums may shift according to the type of event taking place.
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Evidence ontheimpact ofthePrudential Center oncrime in…
The current study seeks to advance the extant literature on macro crime gen-
erators’ effects on crime by studying the impact that events held at the Prudential
Center had on the surrounding area of downtown Newark, New Jersey over the
period 2007–2015. We first employ negative binomial regression to assess the effect
of nine distinct event types on crime counts, at the hourly level in the area under
analysis. Second, we leverage the Fasano-Franceschini test, a statistical measure that
emerged originally in astrophysics, to assess whether events acrosscrime categories
are spatiallydistributed differently when distinguishing between event and no-event
time units at the center. Third, logistic regression models investigate whether there
exists a relationship between the type of location in which a crime incident occurred
and the presence or absence of events at the Prudential Center, providing insights
on potential qualitative differences in the geographic distribution of crime incidents
across the event and no-event time units.
Results suggest that five out of nine event types are associated with statisti-
cally significant crime increases within the Newark downtown area, ranging from
a minimum of 33% increase to a maximum of 61.9% increase. In terms of spatial
distributions of crimes, incidents are distributed differently when they are not dis-
aggregated by category. However, when considering crime typologies separately,
out of six crime categories, only thefts and auto thefts exhibit distinct spatial loca-
tions. Finally, concerning location types, out of six typologies, only street locations
and parking lots are statistically related to the presence of events at the Prudential
Center. As for the former, the likelihood of a crime on the streets is about 30% lower
when no events are held at the facility. Regarding the latter, the odds that crimes are
committed in parking lots are 73% higher when no events are in place at the center.
Background literature
The current work fits into the rich empirical area on crime and place, which dem-
onstrates how crime clusters in specific areas within an urban context. In particular,
we frame our work in the context of the literature on crime generators and attractors.
Crime generators are defined as those places characterized by a high flow of people
leading to the spatial concentration of crime incidents (Brantingham & Branting-
ham, 1995). Crime attractors are those places that attract offenders due to the pres-
ence of significant crime opportunities.
Over time, crime generators may become crime attractors—places well known
to offenders as providers of suitable crime targets—by establishing reputations for
the crime opportunities they provide (Clarke & Eck, 2005), in line with the concept
of multiplicative interaction effects proposed by Cohen and Felson (1979). Studies
concerned with the relationship between crime and particular locations have been
largely situated in three theoretical traditions: (a) crime pattern theory (Brantingham
& Brantingham, 1995), which explains how individuals interact with the built envi-
ronment and highlights the relevance of the particular characteristics of each place
in determining crime risks; (b) routine activity theory (Cohen & Felson, 1979),
which states that the spatial and temporal convergence of suitable targets and likely
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G.M.Campedelli et al.
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offenders in the absence of capable guardians offers optimal conditions for crime;
and (3) the principle of least effort (Zipf, 1949), which argues that offenders tend to
prefer shorter trips compared to longer ones, hence implying that the probability of
crime commission will be higher in the area surrounding a given facility.
Within the boundaries of these theoretical frameworks, Groff and Lockwood
(2014) practically categorize research on facilities and crime into three distinct
areas. The first one is concerned with the mechanisms through which social struc-
ture and land use influence the opportunity for crime occurrence across areal units
such as city blocks. The second addresses the same question but focuses on smaller
units of analysis, such as street segments, analyzing this link across multiple urban
contexts. The third area instead examines the relationship between crime and facili-
ties in the area surrounding said facilities. This work aligns with this third area of
scholarly inquiry.
Previous works have extensively examined the crime-generating as well as crime-
attractor natures of many different locations and facilities across cities around the
world (Bernasco & Block, 2011; Tillyer etal., 2021; Wuschke & Kinney, 2018).
More recently, a growing body of evidence also demonstrated the positive rela-
tionship between sporting events that take place at arenas and other types of mass
gathering facilities and crime. Despite contrasting settings and methods of inquiry,
findings are remarkably consistent in that they repeatedly highlight statistically sig-
nificant increases in the incidence (or expected counts) of crime and disorder in
those locations and during those times that sporting and other entertainment events
occur. Significantly, greater levels of crime have been observed in multiple cities
across the USA (Decker etal., 2007; Yu etal., 2016; Kurland & Piza, 2018; Kur-
land, 2019; Menaker etal., 2019; Pyun, 2019; Ristea etal., 2020; Block & Kaplan,
2022) and the UK (Kurland etal., 2010, 2014; Kurland, 2014; Marie, 2016; Kur-
land etal., 2018). Significant crime increases associated with arenas/stadiums have
further been observed in Barcelona, Spain (Montolio & Planells-Struse, 2016,
2019); Montevideo, Uruguay (Munyo & Rossi, 2013); and Tshwane, South Africa
(Breetzke & Cohn, 2013).1
A subset of this literature has analyzed how an arena’s effect on crime differs
across event types. Breetzke and Cohn (2013) found that overall crime increased in
the 1/2-mi, 1-mi, and 2-mi buffers around South Africa’s Loftus Versfeld Stadium
on rugby and soccer match days in which the home team won. Conversely, crime
significantly increased only within the 1-mi buffer on match days in which the home
team lost. Yu etal. (2016) incorporated hourly data on robbery incidents to test the
criminogenic effect of arena events in Memphis. Findings indicate that NBA Griz-
zlies and Memphis University Tigers basketball games were associated with signifi-
cant robbery increases over the hours immediately prior to, during, and immediately
1 A noteworthy exception to this literature is the recent study by Piquero and co-authors (2021), which
found that the 2018 Formula 1 Grand Prix in Austin, TX did not lead to any significant crime increases
during the weekend of the race. However, the small temporal window, examined in this particular study,
is more temporally constrained than those investigated in the other above-mentioned studies, as is the
case that the race track itself is 12 mi away from the city center and is surrounded by farmland with vir-
tually no development. This may go some way toward explaining the absence of a significant effect.
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Evidence ontheimpact ofthePrudential Center oncrime in…
following the events. Interestingly, robbery did not increase during the hours asso-
ciated with Grizzlies and Tigers away games. This finding suggests arena activ-
ity, rather than the general behavior of fans in Memphis viewing sporting events
remotely, as a key driver of the observed robbery increases. Particularly relevant
to the current study, Kurland (2019) incorporated a similar hourly approach in his
analysis of Newark’s Prudential Center and found that New Jersey Devils ice hockey
games, concerts, and Disney-themed events were all associated with increases in
robbery. The largest effects were observed for Disney-themed events. Conversely,
events such as circuses, NBA/WNBA basketball games, boxing matches, and mixed
martial arts matches were not associated with robbery increases.
In an effort to further capture the disamenities produced by sporting events, a new
line of scientific inquiry has begun to quantify the nature and extent of congestion
externalities including additional vehicular traffic, CO2 emissions in cities, the local
air quality index (AQI), and even the increased levels of airborne particulate matter
generated during the sports facility construction projects in cities that have led to an
increase in maternal prenatal visits and lower infant birth weights (Humphreys and
Ruseski, 2019; Humphreys & Pyun, 2018; Locke, 2019). The negative public health
consequences of sporting and entertainment events have also been empirically docu-
mented in the recent literature (Cardazzi etal., 2020; Stoecker et al., 2016). The
evidence base that has been assembled, particularly over the previous decade, across
various disciplines on the effects linked both to the construction of such sports and
entertainment venues, and their persistent role as generators of negative externalities
in the form of traffic, pollution, public health outcomes, and crime and disorder in
the communities they are meant to serve is extensive. In what follows, a brief sum-
mary on the case of the Prudential Center is provided to set the stage for the analy-
sis and the associated policy recommendations that stem from a combination of the
empirical base outlined above and the results that follow.
The case ofthePrudential Center
The Prudential Center is a nearly 20,000-seat arena in downtown Newark, NJ, acces-
sible by public transport (rail, light rail, and bus). The Prudential Center opened for
business on October 25th, 2007, with ten shows by New Jersey–native John Bon
Jovi attended by close to 140,000 individuals and generating over $16 million in
ticket revenues. The opening of the Prudential Center was the result of many years
of political negotiating and planning by the city of Newark officials (Farber, 2007).
The idea to build an arena in New Jersey’s largest city was first advanced by Sharpe
James, Newark mayor from 1985 to 2006, who like many other mayors across the
USA envisioned the development of an arena as an economic generator and job crea-
tor for the city. The Prudential Center was built at a cost of approximately $375M of
which the city subsidized $220M (Kaske, 2007). While spearheaded by the James
administration, the Prudential Center would not open until the new Mayor (and now
US Senator) Cory Booker took office.
While celebrated as a positive development by city leaders, the opening of an arena
in New Jersey’s largest (and highest crime) city, and second most socioeconomically
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G.M.Campedelli et al.
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deprived city in the nation, initially worried some observers. Many wondered whether
public safety concerns would prevent middle-class and more affluent suburban resi-
dents from frequenting the Prudential Center (R. G. Jones, 2007). Such concern was
articulated by Berry Melrose, a hockey analyst for the ESPN television network. In
describing the newly opened Prudential Center during a webcast, Melrose stated “It
looks great on the inside but don’t go outside, especially if you got a wallet or anything
else because the area around the building is awful” (Mays, 2007).
Despite the worry, public officials ensured that Prudential Center patrons would be
safe during their time in the city. Periodically, statistics released by the Newark Police
Department supported claims that downtown Newark was a safe environment for visi-
tors. For example, over the 4-day period in 2011, the NCAA East Regional Basketball
Tournament was held at the Prudential Center, and police data suggested that crime
was “virtually non-existent” in the downtown area (Queally, 2011). However, high-
profile crime events that did occur around the Prudential Center called into question
the true safety afforded to event attendees. For example, six people walking to a park-
ing lot were assaulted by a group of at least a dozen teenagers after a Britney Spears
concert (Adarlo, 2009), and five people were beaten and robbed about two blocks from
the arena following a Red Hot Chili Peppers concert (Queally, 2012). While high-pro-
file acts of crime were not reported following any hockey games, the most frequently
occurring events at the Prudential Center, the implications of crime in the city of New-
ark when events take place were not lost on the local media. Indeed, reports of serious
crime occurring in Newark often described these events as occurring “within close
proximity” or “nearby” the Prudential Center, irrespective of their connection to arena
events (Queally, 2011, 2012).
With the above-mentioned in mind, Kurland and Piza (2018) sought to build on
the growing research base related to the nature of crime and disorder patterns in and
around large-scale sports and entertainment venues but did so with a focus on New
Jersey Devils hockey games that took place at the Prudential Center. The research
provided the foundation for further inquiry in that it established that the crime pat-
terns that were generated across days that the arena was used for hockey and a set
of comparison days were significantly different for numerous crime categories at
various spatial resolutions. More specifically, aggravated assaults, auto theft, bur-
glary, robbery, theft, and theft from the vehicle all had a significantly higher count
of crime across the city of Newark, across the downtown area of Newark, and at
the street segment-level across numerous areas of the city that were not proximal
to where the arena was located. These underlying differences in the count for these
crime categories were systematic and importantly only represented a relatively small
proportion of the number of large-scale crowd-related events that take place at the
Prudential Center. Thus, the underlying difference in the overall count of crime
when events take place remained, at least in part, unknown. In a follow-up study that
sought to extend the initial work, Kurland (2019) conducted an econometric analysis
of 11 different categories of sporting and entertainment events that took place at the
Prudential Center to explore the influence, if any, that each event category had on
hourly robbery counts across the entire city of Newark. Results from the study sug-
gested the hourly count of robberies increased significantly during Devils hockey,
concerts, and Disney-themed events at 25%, 21%, and 32%, respectively.
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Evidence ontheimpact ofthePrudential Center oncrime in…
Current focus
Professional sports stadiums and arenas bring with them a promise of jobs, new
sources of revenue for nearby businesses, and a place where fans can attend a wide
range of events. Extant research has provided some indication that one adverse by-
product of these facilities, their events, and the crowds that may attend is an increase
in certain types of crime during “game” or “event” days. Herein, we seek to build
on this previous work by providing a more complete picture of the impact of the
Prudential Center on crime patterns through the analysis of all the crimes and events
that have taken place at the venue from opening day through 2015. The analytical
focus will concentrate on the Newark Downtown District as shown in Fig.1. The
downtown district measures about 0.54 mi2 in size and houses the Prudential Center
as well as several commercial parking lots that cater to arena event attendees. New-
ark Penn Station, the primary public transportation hub in the city, sits about three
blocks east of the arena. The downtown district further offers a large concentration
of restaurants and drinking establishments for arena patrons to attend prior to or fol-
lowing the events.
The effects of events held at the center will be investigated in terms of crude
impact on crime counts as well as on the spatial characteristics of crimes that
occurred during event times, to offer not only a quantitative assessment of the
Fig. 1 Map of the area under analysis for evaluating the impact of events held at the Prudential Center in
Newark, NJ. Dashed red line indicates the boundaries of the area. The Prudential Center is highlighted
with the dotted red area
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G.M.Campedelli et al.
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marginal effect of such events on crime, but also an analysis of crime in qualitative
terms, responding to the three following questions:
– Expanding on previous research (Kurland and Piza, 2018; Kurland, 2019) that
focused on city-wide crime trends, do events held at the Prudential Center have
an impact on the hourly number of crimes that occurred in the surrounding New-
ark Downtown area?
– Are the effects on crime trends of events held at the center homogeneous across
event types?
– Does crime change in its spatial distribution and location characteristics, when
the Prudential Center is active?
Methodology
Data
To measure the effect of the various sporting and entertainment events that take
place at the Prudential Center, a dataset of the times for all events that took place
at the venue from its opening in 2007 through 2015 was identified using the offi-
cial website of the Prudential Center (www. pruce nter. com) as well as the Internet
Archive Wayback Machine (https:// archi ve. org/ web/) and was cross-checked for
accuracy using an event dataset for this same period provided by Newark PD. In
addition, an hourly crime count dataset for all five general crime categories2 for the
city of Newark over this same 12-year period was constructed. These data provide
a large number of hours that no events take place. Consequently, sufficient data is
available for testing the effect of different event categories on the underlying crime
patterns that may, or may not be, associated with each type of event. A more com-
prehensive explanation of each of the respective datasets that were utilized in the
model is described in the subsections that follow.
Crime data
The Newark Police Department provided a dataset of all crime events that took
place for a 9-year period between 2007 and 2015. The data was cleaned and aggre-
gated into hourly counts. All five crime categories were used for the study in order
2 These are aggravated assault (which comprises “aggravated assault—hit,” “aggravated assault—no
hit,” “aggravated assault—knife,” “aggravated assault—other weapon,” “aggravated assault—physical
force,” “aggravated assault—PO,” “aggravated assault—pointing,” “aggravated assault—EMS,” “aggra-
vated assault—no hit,” “aggravated assault—no hit robbery—carjacking—strong arm”), auto theft, bur-
glary, robbery (including “robbery—carjacking—gun,” “robbery—gun,” “robbery—knife,” “robbery—
no hit,” “robbery—carjacking—strong arm,” “robbery—bank,” “robbery—other weapon”), theft, and
other (a marginal category including various crimes with low numerosity. There are “murder—shoot-
ing,” “murder—stabbing,” “murder,” “possession of weapon,” “found property,” “criminal mischief,”
“unfounded,” “shots fired,” “rape,” “weapon recovered”).
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Evidence ontheimpact ofthePrudential Center oncrime in…
to estimate the total crime-generating effect of the different types of events that
occur at the Prudential Center. The opportunity theories that frame this research
suggest that various crime types will increase as a direct consequence of a greater
number of potential targets. That is, fans attending sporting and entertainment
events, their belongings, the vehicles they may have taken to get to the venue,
and the number of motivated offenders who may take advantage of serendipitous
opportunities that are furnished in this environment provide suitable conditions for
forms of acquisitive crime. Furthermore, because of the great number of additional
interactions, and likely provocations, that occur between fans and other stakehold-
ers on event/game days, additional expressive crime is also likely to occur.
Prudential Center schedule
The schedule for all sporting and entertainment events that includes the date and
event/game starting time that took place at the Prudential Center between 2007 and
2015 was assembled. To best model the ecological change that takes place when the
facility is used, a dummy coding scheme was implemented. The approach enables
us to capture spectators’ tendencies to arrive and assemble around and inside the
Prudential Center at different rates, but also, their dispersal all at once shortly after
events/games are over. More specifically, the 2h leading up to the event/game, the
hour that the event/game takes place, and the 2h after the event/game concluded
were coded as 1 and 0 otherwise.3 This was done for all hours in the dataset that
included the 24h of every day from 2007–2015. The following event dummy vari-
ables were created: New Jersey Nets (NBA), New Jersey Devils (NHL), Seton Hall
Pirates (NCAA men’s basketball), New York Liberty (WNBA), boxing (including
mixed martial arts), other sports, concerts, circus, Cirque du Soleil, Disney, and
other entertainment.4
Controls
Empirical research has repeatedly found relationships between particular tem-
poral, lighting, and meteorological conditions and crime patterns. These factors
known to influence crime patterns have been included in the model to control
for their potential influence. More specifically, the hour of the day (Felson &
Poulsen, 2003), the day of the week (Cohn, 1993), the month of the year (Ran-
son, 2014), and even the year (Cohen & Felson, 1979) were assembled to take
stock of the different seasonalities that have been found to be associated with
3 We recognize that some events might last longer than 1h, but we precisely chose to consider a 5-h
window which represents a reasonable time window for capturing arrival, the event itself, and the spec-
tators’ dispersal. For instance, one could think of the distribution of these three moments as 1.5 h or
arrival, 2h of event, and 1.5h of post-game activity or, alternatively, 1.5h of arrival, 1.5h of event, and
2h of post-game activity. Different event types will have different mobility patterns, and modeling this
over a total of 5h allows to meaningfully map the heterogeneity in such patterns.
4 This represents a residual category that includes all events that could not be meaningfully associated
with the other event categories, including skateboarding competitions, musicals, or variety shows.
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G.M.Campedelli et al.
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crime patterns. The absence of light is believed to hinder surveillance that in
turn affects guardianship, a protective factor, against some crime types (Rotton &
Kelly, 1985; Van Koppen & Jansen, 1999; L. Tompson & Bowers, 2013) and to
control for a possible relationship between darkness and criminal activity—hours
of darkness were coded as 1, and 0 otherwise.5 Meteorological conditions such as
ambient temperature have also been found to influence crime patterns with lower
temperatures increasing the use of particular clothing items such as winter hats
and balaclavas that increase anonymity but do not increase suspicion during this
period (Cohn, 1990; Field, 1992; L. A. Tompson & Bowers, 2015). Hourly tem-
perature data (°F) for Newark from 2007 to 2015 was obtained from the National
Climatic Center and used to construct the temperature control variable. Further-
more, it is worth noting that while socio-economic characteristics are generally
relevant in inferential studies on crime and place, in the current study, we chose
not to include them because the entire study area (i.e., Downtown Newark) only
intersects two census tracts and represent one contiguous, socially homogeneous
area. Hence, no variation in socio-economic characteristics could be meaning-
fully leveraged.
Analysis
The analysis herein can be broken into three distinct phases. The initial stage makes
use of a commonly used econometric approach for modeling crime count data
(Osgood, 2000) and at sporting and entertainment venues at the city-level more
specifically (Kurland, 2019; Yu etal., 2016). The approach enables the amount of
additional crime that can be confidently attributed to each of the respective crime/
event categories while controlling for other factors that might influence crime pat-
terns to be calculated.
The second stage utilizes the Fasano-Franceschini test (Fasano & Franceschini,
1987) to evaluate whether the spatial distribution of crimes differs when com-
paring crimes that occurred during event stages at the Prudential Center versus
crimes that occurred when no events were in place at the center. Finally, the third
analytical stage concerns the assessment of the impact that event times had on the
different locations in which crimes occurred. Such investigation leverages a set of
logistic regression models using location types as binary dependent variables: the
rationale is to disentangle the possible correlation between event times and spe-
cific locations in crime generation.
5 To compute the binary measure of darkness, we have retrieved sunlight data from the “suncalc” pack-
age in R. The package allows to capture day-to-day variation in daylight, capturing differences across
weeks, months, and seasons. We have specifically coded two measures of darkness and tested our models
with both. The main one takes as reference dawn and dusk, with all crimes occurring between these two
having darkness = 0. The alternative measure uses sunrise and sunset as references, with all incidents
occurring between these two having darkness = 0.
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Evidence ontheimpact ofthePrudential Center oncrime in…
GLM withnegative binomial distribution
Given the count nature of the dependent variable (hourly crime counts), the asso-
ciated distribution most closely resembling a Poisson distribution, and a variance
that exceeded the mean indicating over-dispersion, we estimate a negative binomial
regression model, a specific type of generalized linear model. The approach is more
appropriate than a regular Poisson for hypothesis testing given its flexibility in rela-
tion to over-dispersion (Long & Freese, 2006). We have empirically motivated our
decision by comparing residual plots across a negative binomial model and a Pois-
son model and computing a likelihood ratio test (p-value < 0.0001, indicating that
the negative binomial model is more appropriate for the data under consideration).
The results of the residual plots are visualized in the Supplementary Materials (Fig-
ure A 1 and Figure A 2).
The model utilized for investigating the relationship between crimes and sporting
and entertainment events is calculated as per Eq.(1):
With the dependent variable, Y (
#HourlyCr ime)
is the count of crime for each
hour across the 9-year data period.
Fasano‑Franceschini test
To assess whether crimes that occurred during events hosted at the Prudential
Center differ in their spatial distribution compared to those that happened in other
time frames, we leverage the Fasano-Franceschini (FF) test (Fasano & Franceschini,
1987). The FF test assesses the null hypothesis that independent and identically dis-
tributed random samples of points are drawn from the same distribution. In other
words, it tests whether two random samples are statistically indistinguishable based
on their distribution in a k-dimensional space. The FF test expands on the popu-
lar univariate Kolmogorov–Smirnov (KS) test, a non-parametric test developed to
investigate whether two samples come from the same underlying distribution (Kol-
mogorov, 1933a, 1933b; Smirnov, 1944, 1948). Specifically, in one dimension, the
KS statistic maps the maximum absolute difference between the cumulative density
functions of data and model (when one sample is involved) or between two datasets,
when two samples are analyzed. In the case of the KS test, however, distributions
#HourlyCr ime = 𝛽0+𝛽1Net s + 𝛽2Devils + 𝛽3Pirates + +𝛽4Liberty
+
𝛽
5Boxing +
𝛽
6OSports +
𝛽
7Concerts +
𝛽
8Circus
+𝛽9CirqueduSoleil + 𝛽10 Disney + 𝛽10OEnt er t ainment
(1)
+
k
∑
j+
1
𝛽jContr ol +
𝜀
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1236
G.M.Campedelli et al.
1 3
are unidimensional. In our context, conversely, the spatial distribution of crimes is
characterized by multidimensionality and is particularly defined in a 2-dimensional
space. The FF test precisely provides a way to carry out this task.
The dimensionality problem of the KS test was first examined by Peacock (1983)
and then by Fasano and Franceschini in a later paper. Peacock addressed it by defin-
ing a 2D test statistic as the largest difference between the empirical and theoreti-
cal cumulative distributions, once all possible ordering combinations are taken into
account. As explained by Puritz etal. (2022), the test calculates the total probability
in each of the four quadrants around all possible tuples in the data. To exemplify,
given n points in a 2D space, the empirical cumulative distribution is calculated in
the 4n2 quadrants of the plane defined by all pairs (Xi, Yi). Given that there are n2 (Xi,
Yi) pairs, and each can define four quadrants in the 2D space, by considering them
all, the 2-dimensional statistic is the maximal difference of the integrated probabili-
ties between samples.
The FF variation only considers quadrants centered on each observed (Xi, Yi),
instead of focusing on all possible n2 points, for a total of 4n quadrants. After
the algorithm loops through every point in one sample to define the origins of all
quadrants, the fraction of points in each sample is calculated per quadrant, and the
quadrant with the maximal difference is assigned to the maximum for the specified
origin. By iterating the overall points and related quadrants, the test statistic DFF1
is given by the maximal difference of the integrated probabilities between sam-
ples in all quadrants for all origins from the first sample of data points. The proce-
dure is repeated using the other sample to obtain DFF2: the two are then averaged
(
D
FF=
D
FF1
+D
FF2
2
) for the purpose of hypothesis testing. Via the Monte Carlo simula-
tion, the authors processed an associated look-up table of critical values of
DFF
tak-
ing into account the sample size and coefficient of correlation r. For the two-sample
case, the approximate fit to the look-up table is
with r defined as the usual correlation coefficient (trivially, when r = 1, the points
would form a single line, and thus, the 1-D KS test could be applied on the marginal
distributions). Given the equation above, when the test statistic is found to be statis-
tically significant, distributions are found to be statistically different.
To the best of our knowledge, the test has yet to be applied in the criminologi-
cal context; hence, we also seek to demonstrate its potential for future studies con-
cerned with the spatial analysis of crime and deviance. Assessing and understand-
ing the distribution of different crime incidents or other crime-relevant events are
of paramount importance for the criminological literature. Are violent and non-
violent offenses distributed differently within a given city? Are homicides against
minorities distributed differently from homicides against whites in a given county?
Does crime emerge across different locations during the weekends? These are a few
of the many questions that can be addressed through the use of the FF test. While
(3)
ℙ
(dFF >DFF)=Φ





DFF

n1n2
n1+n2
1+1−r20.25 −0.75∕n1n2
n1+n2





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1237
1 3
Evidence ontheimpact ofthePrudential Center oncrime in…
other approaches exist to compare the distribution of points in spatial contexts rep-
resented in two-dimensional spaces (e.g., kernel density estimation), these often
involve hyperparameter optimization and are not easily interpretable. By leveraging
the FF test, instead, we can easily and flexibly tackle such problems. In fact, the
test, especially through its implementation through the Fasano-Franceschini.test in
R, is extremely computationally efficient. Furthermore, data can be of any dimen-
sion (although in criminology, scholars are mostly interested in the 2D case) and
of any type (i.e., continuous, discrete, and mixed). These two characteristics pre-
sent criminologists with important opportunities which we exploit in this work to
investigate the spatial effects that the Prudential Center has on crime in Downtown
Newark.
Regression models forlocation analysis
Finally, the last analytical step aims at gathering additional knowledge of the spa-
tial characteristics of crime in the Prudential Center area in Newark in the event
vs. no-event timeframes. This third analytical step adds a further layer of results
to the statistical analysis of spatial differences in crime incidents across the event
and no-event time units. Specifically, location analysis seeks to provide addi-
tional qualitative findings highlighting what type of spatial patterns emerge when
the Prudential Center is active. Understanding whether some types of locations
attract more or less crime during sporting and entertainment events is relevant
for both theory and practice. Empirical findings would contribute to the literature
on the complex effects of stadiums and arenas on crime as well as inform crime
control and prevention policies and interventions to allocate special resources to
specific areas, buildings, and premises. For this purpose, we performed statistical
analyses via logistic regression to evaluate the relationship between the type of
time unit (IV) and different types of location (DV). Since location types are our
dependent variable, characterized by eight (six, in practice) different categories,
one natural candidate was multinomial logistic regression.6 However, with mul-
tinomial logistic regression, one level has to be selected as a baseline, such that
effect sizes have to be interpreted in reference to such baseline. Given that no the-
oretically meaningful baseline could be established, and given the relatively low
level of interest in comparative accounts across locations, we relied on a different
strategy. Particularly, we designed our models as a set of “one vs. rest” logistic
6 The location types are (1) street, (2) non-residential building, (3) parking lot, (4) commercial, (5)
dwelling, (6) hotel/motel, (7) other, and (8) unknown. These categories are coded by combining similar
location types in original labeling of the dataset. Street comprises “sidewalk,” “driveway,” and “street.”
Non-residential building comprises “church,” “building under construction,” “school,” “stadium,” “city
hall,” and “public building.” Commercial comprises “store,” “bank,” “salon,” “restaurant,” “clothing
store,” “tavern,” “bakery,” “business,” “department store,” “gym,” “office,” “store,” and “commercial.”
Dwelling includes “public housing,” “apartment,” “residence,” “house,” and “dwelling.” Hotel/motel
comprises “hotel/motel,” “hotel,” “hotel room,” “hotel & motel.” Other refers to “service station,” “lot/
yard,” “garage,” “park,” “transportation facility,” “building under construction,” “dock or wharf,” “play-
ground,” “building,” “bus,” “construction site,” “gas station,” “lot,” and “YWMCA.”.
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1238
G.M.Campedelli et al.
1 3
regression, encoding binary target variables for all location types and therefore
fitting as many separate models as the number of location types. The rationale of
such models is thus to independently evaluate whether the presence or absence of
an event hosted at the Prudential Center has a relationship with the occurrence of
a crime in a particular location category.
Results
Summary statistics
Summary statistics for the main variables are provided in Table 1. Out of the
74,926 hourly time units considered in the 2007–2015 period, 94.11% (N = 70,515)
recorded no crimes in the Prudential Center area of Newark. The range of known
crimes per hour goes from a minimum of 1 (N = 4173, 5.56%) to a maximum of
4 (N = 1, 0.00001%) crimes. On average, the area under analysis registered 0.062
crimes per hour, with a total of 4655 known offenses. When disaggregating per
event type, Liberty and Nets events have the largest mean number of crimes, 0.132
and 0.112 respectively, while the highest number of hours devoted to specific
events regards Devils (N = 1,870) and concerts (N = 765). The event type account-
ing for the lowest average number of crimes is Cirque du Soleil (mean = 0.049),
which is also the event type with the lowest sum of hours dedicated at the Pruden-
tial Center (N = 101).
Table 1 Summary statistics—number of hours and crimes in the main dataset, divided per event type
The sum of event and no-event hours exceed the total sum of hours (74,926) because there were cases in
which multiple events where simultaneously staged at the Prudential Center
Event Total N of hours Avg. crimes Standard
deviation
All hours 74,926 0.062 0.255
No events 69,744 0.061 0.252
All events 5182 0.079 0.252
Boxing 201 0.069 0.255
Circus 294 0.081 0.298
Cirque du Soleil 101 0.049 0.219
Concert 765 0.083 0.291
Devils 1870 0.073 0.277
Disney 497 0.066 0.249
Liberty 280 0.132 0.345
Nets 410 0.112 0.345
Other entertainment 313 0.073 0.273
Other sports 472 0.066 0.280
Seton Hall men’s basketball 675 0.065 0.266
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1239
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Evidence ontheimpact ofthePrudential Center oncrime in…
Negative binomial regression model
Estimation results are given inTable2.7 The dataset is structured at the hourly level,
with the number of crimes that occurred in a given hour as the dependent variable.
Coefficients are reported in the form of incident rate ratio (IRR), mapping the rela-
tive rate of change in the number of crimes for each event type, and are to be inter-
preted as the percent change in the number of crimes when a certain event is held
at the Prudential Center compared to the baseline scenario with no events hosted at
the facility. The results reveal five statistically significant event effects. The effects
associated with circus, concerts, Devils, Liberty, and Nets are all positive, ranging
between 1.330 (Devils) and 1.618 (Nets). At the same time, the effect is not statis-
tically significant for boxing, Cirque du Soleil, Disney, other entertainment, other
sports, and Seton Hall men’s basketball events. Concerning statistically significant
results, they are all above one, with the smallest—and yet positive—IRR magnitude
related to Devils. The IRR equal to 1.330 (95% CI [1.111; 1.592], p-value = 0.002)
suggests a 33% increase in the number of crimes when Devils’ games are held at the
Prudential Center. Given the baseline of 0.061 crimes occurring on average in the
area under analysis when no events are hosted at the Prudential Center, this increase
amounts to an additional 0.02 crimes in Downtown Newark. Magnitude-wise, New
York Liberty games have the second-smallest positive effect (IRR = 1.453, 95%
CI [01.037; 2.035], p-value = 0.020). When such events are held at the Prudential
Center, the model estimates a 45.3% increase in the number of crimes, amounting
7 In an effort to economize space, we have not shown the numerous control variables; however, these are
all included in the Supplementary Materials (TableA1).
Table 2 Results of the negative binomial models for the impact of the different events scheduled at the
Prudential Center
While not presented here for the sake of space, all controls are included in the regression mode (see Sup-
plementary Materials
* p < 0.05, * * p < 0.01, * * * p < 0.001
Event IRR Robust SE p-value 95% CI
Boxing 1.167 0.320 0.550 [0.682; 1.997]
Circus 1.569* 0.332 0.037 [1.035; 2.379]
Cirque du Soleil 0.768 0.348 0.550 [0.316; 1.869]
Concert 1.482** 0.199 0.003 [1.139; 1.928]
Devils 1.330** 0.122 0.002 [1.111; 1.592]
Disney 1.248 0.227 0.195 [0.874; 1.782]
Liberty 1.453* 0.250 0.020 [1.037;2.035]
Nets 1.618** 0.251 0.002 [1.193;2.194]
Other entertainment 1.207 0.259 0.366 [0.793; 1.839]
Other sports 1.223 0.226 0.314 [0.851; 1.757]
Seton Hall men’s basketball 1.222 0.193 0.215 [0.897; 1.665]
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1240
G.M.Campedelli et al.
1 3
to additional 0.027 crimes. Concerts have slightly higher effects, corresponding to
a 48.2% increase in the number of crimes, translated to an additional 0.029 inci-
dents (IRR = 1.482, 95% CI [1.139;1.928], p-value = 0.003). Circus events lead to a
56.9% increase in crimes amounting to 0.035 crime occurrences (IRR = 1.569, 95%
CI [1.035;2.379], p-value = 0.037). Finally, the event type with the highest effect
in terms of crime is New Jersey Nets games, signaling a 61.8% increase in crimes
which can be converted to an additional 0.04 crime events (IRR = 1.618, (95% CI
[1.193;2.194], p-value = 0.002). All the presented outcomes hold when robustness
tests are performed (as reported in the Supplementary Materials, see Table A 1). We
have specifically performed three additional models: a negative binomial with an
alternative measure of darkness and two Poisson models, one using the main meas-
ure of darkness and the other using the alternative one (as explained in the “Meth-
odology” section). These results suggest that the crime-generating effect of different
event/game types are not all equal in their crime generation and, in some cases, there
are types of games/events that are less prone to producing this particular negative
externality. Indeed, this finding is reflective of similar research that has found that
contrasting patterns emerge for the same sports and entertainment facility when dif-
ferent event types occur within them (e.g., for example, see Kurland etal., 2014).
Assessing differences inspatial distributions
Figure 2 reports the distribution of crimes in the area surrounding the Pruden-
tial Center in Newark. Figure2A simply visualizes the distribution of crimes as
points, divided between crimes that occurred during event times held at the center
and crimes that happened in other hourly time units. Mirroring the same division,
Fig.2B shows a more aggregated measure through hexagonal bins, with 40 different
levels. What emerges from the figure is that, besides differences in the overall quan-
tity of crimes between the two categorizations, the geographical distribution of the
two seems to slightly differ. In both cases, the areas in the north-west of the center
appear to be the ones with the most crime prevalence. Yet incidents in event time
units seem to cluster more in specific micro-areas, especially southeast of the center.
When the distributions of crime divided by category are statistically compared,
the overall tendencies signaled in Fig.2 become even more evident. The FF test,
which has been described in the dedicated subsection, precisely aims at evaluating
whether we can detect differences in the 2-dimensional distributions, as crimes are
represented as points in the geographical space under analysis. In other words, the
test evaluates whether two random samples come from the same underlying distri-
bution. The outcomes of the test, for crimes overall, and the different crime catego-
ries are provided in Table3.
Firstly, the FF reveals statistical differences in the geographical distributions of
the 4295 crimes that occurred in no-event hourly time slots and the 412 crimes that
occurred during the events at the Prudential Center (D-stat = 2.219, p-value = 0.009),
in line with the graphical evidence shown in Fig.2. When the specific crime catego-
ries are taken into account, however, only thefts and auto thefts—the two most prev-
alent crime categories in the data—seem to differ in terms of spatial distributions
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1241
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Evidence ontheimpact ofthePrudential Center oncrime in…
between the two categories (D-stat = 1.734, p-value = 0.019 and D-stat = 1.857,
p-value = 0.029, respectively). For all the other categories, no statistically signifi-
cant differences are found. These findings, overall, indicate that there is only a par-
tial displacement effect of crime due to the events held at the Prudential Center.
Event No Event
−74.185−74.180−74.175−74.170−74.165−74.160 −74.185 −74.180 −74.175−74.170 −74.165 −74.160
40.730
40.735
40.740
40.745
Longitude
Latitude
A
Event No Event
−74.185−74.180−74.175−74.170−74.165−74.160 −74.185 −74.180 −74.175−74.170 −74.165 −74.160
40.730
40.735
40.740
40.745
Longitude
Latitude
50 100150 200
count
B
Fig. 2 Spatial distribution of crimes in the Prudential Center area. A The distribution of crimes as simple
points on the map. B More aggregate localized measures using hexagonal bins (n = 40)
Table 3 The Fasano-
Franceschini test results for the
spatial distribution of all crime
categories
* p < 0.05, * * p < 0.01, * * * p < 0.001
Crime category N crimes
(no events)
N crimes
(events)
D-stat p-value
All crimes 4295 412 2.219** 0.009
Aggravated assault 217 24 1.074 0.396
Auto theft 647 61 1.857* 0.029
Burglary 306 10 1.055 0.426
Other 107 11 1.104 0.257
Robbery 620 69 1.388 0.138
Theft 2398 237 1.734* 0.019
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1242
G.M.Campedelli et al.
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Interestingly, in fact, these statistical differences in crime incidents are not universal
across crime types. This partial displacement may be explained by the shocks intro-
duced by spectators in the structure of crime opportunities and generators around
Newark Downtown. The inflow and outflow of people from the area, along with the
modified patterns of formal and informal guardianship, provide offenders with new
opportunities and risks which require partial adaptation to the event circumstances.
Location analysis
Figure3 indicates the percentage distribution of crime by location type, grouped by
the hourly time slots in which they occurred. Percentages sum to 1 when they are
added for each category (event vs. no-event hourly time slots). This first graphical
evidence seems to indicate no major differences in the distribution of location types
for crimes that occurred during events and on other days and hours. Crimes commit-
ted in the streets represent the vast majority in both cases, followed by crimes perpe-
trated on commercial premises.8
Also, in this case, the visual evidence is generally in line with the statistical one
produced by the set of independent logistic regression models reported in Table4.
All models control for darkness and temperature and consider single offenses as
the unit of analysis. Unknown locations (which are mostly related to crimes that
occurred in 2007) and hotel/motel are excluded. The rationale for the former is that
the unknown category does not provide any meaningful indication regarding the
incident. The motivation for the latter, instead, is that out of 45 incidents in hotel/
motel locations, only 1 occurred during events. Out of the six location types repre-
sented in the six different models, four are found to be statistically unrelated to the
presence or absence of events at the Prudential Center, reinforcing the evidence that
points in the direction of the absence of quantitative and qualitative spatial effects
of the facility on crime, and two are found to be significantly correlated with this
Fig. 3 Percentage distribution of
crime by location type, grouped
by crimes occurred during
events at the Prudential Center
vs. crimes occurred in other
hourly time slots
COMMERCIAL
DWELLING
HOTEL/MOTEL
NON RES. BUILDING
OTHER
PARKING LOT
STREET
UNKNOWN
0.0 0.20.4 0.6
% of Location Type out of Event/Non Event Crimes
Location Type
Legend
Event
No Ev
ent
8 It is important to note that the commercial category includes alcohol-selling premises such as restau-
rants and taverns.
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Evidence ontheimpact ofthePrudential Center oncrime in…
measure. The lack of events at the Prudential Center is positively correlated with
offenses committed in parking lots (OR = 1.731, St. error: 0.561), although the coef-
ficient is only statistically significant at the 90% threshold. The “no event” coeffi-
cient indicates that the odds of crimes committed in parking lots are 73% higher
when no events are held at the center. On the other hand, the probability of crimes
committed in the streets decreases in the absence of events at the Prudential Center
(OR = 0.707, St. error: 0.084). The likelihood of a crime on the streets is almost 30%
lower in no-event hourly units.
Discussion andconclusions
The literature on the relationship between stadiums, super facilities, and crime, as
well as the tangential scholarship studying the link between sports and entertain-
ment events and crime, is growing and becoming heterogeneous in terms of tempo-
ral, geographical, and methodological characteristics (Kurland etal., 2010; Breetzke
& Cohn, 2013; Kurland et al., 2014; Marie, 2016; Yu etal., 2016; Montolio &
Planells-Struse, 2016; Kurland & Piza, 2018; Kurland etal., 2018; Menaker etal.,
2019; Montolio & Planells-Struse, 2019; Block & Kaplan, 2022). Despite this heter-
ogeneity in this line of inquiry, most studies over the years highlighted that activity
at such facilities, as well as sports and entertainment events, are generally associated
with increases in crime prevalence and frequency. Several theoretical lenses explain
this convergence of findings. Among these, the change in the opportunity structure
of offending due to the influx of people and services brought in a given environ-
ment during events (and in the surroundings of hosting facilities) and the presence
Table 4 One vs. rest logistic regression models—DVs are location types (standard errors between paren-
theses)
+ p < 0.1, *p < 0.05, **p < 0.01, ***p < 0.001
Commercial Dwelling Parking lot Non-res building Street Other
Intercept 0.456*** 0.049*** 0.034*** 0.045*** 1.101 0.038***
(0.088) (0.016) (0.014) (0.022) (0.173) (0.014)
No event 1.214 1.438 1.731 + 1.706 0.707** 0.703
(0.181) (0.371) (0.561) (0.685) (0.084) (0.180)
Darkness 0.582*** 1.149 1.100 0.570** 1.402*** 1.064
(0.051) (0.162) (0.181) (0.117) (0.098) (0.188)
Temp 0.989*** 0.997 0.994 0.989* 1.011*** 1.005
(0.002) (0.004) (0.004) (0.005) (0.002) (0.005)
N4392 4392 4392 4392 4392 4392
AIC 4286.4 1900.6 1493.1 1233.2 5737.0 1385.7
BIC 4312.0 1926.2 1518.7 1258.8 5762.5 1411.3
Log. lik − 2139.222 − 946.301 − 742.556 − 612.605 − 2864.495 − 688.862
F19.397 1.108 1.515 4.730 20.353 1.000
RMSE 0.39 0.23 0.20 0.18 0.48 0.19
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1244
G.M.Campedelli et al.
1 3
of crime attractors and precipitators that often raise the risk of victimization (Brant-
ingham & Brantingham, 1982, 1995; Farrell, 2015). In this study, we have sought to
advance the extant literature on this topic by focusing on a specific urban context,
namely, the Downtown Newark, New Jersey district, where an important subsidized
super facility is located: the Prudential Center, by building upon previous research
with respect to this arena.
Our work, which considers the 2007–2015 period, expands the previous scholar-
ship in several ways. First, instead of considering the entire city, we have focused on
the area surrounding the Prudential Center, namely, the downtown district of New-
ark. This decision limited the potential for noise in our data and subsequent results,
avoiding implausible assumptions about the fact that crimes occurring far away from
the facility are related to the events taking place at the center. Second, we have con-
sidered all event types and all available crime categories instead of only focusing
on one particular sport or team or one particular crime type. Third, not only have
we investigated the effect that heterogeneous event types have on crime counts—
similarly to what was done at the city-level in Kurland (2019)—we have also ana-
lyzed the quantitative and qualitative spatial characteristics of crime incidents that
occurred during event times versus those that occurred when the Prudential Center
was not active. In doing so, we have showcased the relevance of the Fasano-France-
schini test, a measure of statistical similarity for 2D distributions, a novel approach
within the field of criminology. Fourth, unlike previous studies and besides point-
wise comparisons, we have explored whether crimes during event time units
occurred at qualitatively different locations. Fifth, to the best of our knowledge, this
is the first criminological work exploring the use of the Fasano-Franceschini test to
compare the distribution of geographical events. We have demonstrated how it could
be meaningfully deployed in the analysis of crime incidents and how it could be
used as an alternative to other techniques, such as kernel density estimation, when
interested in understanding whether crime patterns in a given area vary over time or
due to exogenous shocks.
As a collective, these methodological choices allowed us to gather a more com-
prehensive and less noisy understanding of the crime dynamics occurring as a con-
sequence of the influence of the Prudential Center in the urban area of Newark that
is mainly affected by the facility.
The results of the three analytical components of the work (i.e., the influence of
events on crime counts, the similarity in spatial distributions of crime that occurred
during event times versus all the others, and the study of the relationship between
crime occurring at particular locations and the absence/presence of events) are het-
erogeneous. Regarding the effect that different events have on crime counts, we
observed that five event types (out of eleven) are statistically associated with sig-
nificant increases in crime incidents. These are New Jersey Devils hockey games
(associated with a 33% increase in crime), NY Liberty basketball games (+ 45.3%),
concerts (+ 48.2%), circus exhibitions (+ 56.9%), and, finally, New Jersey Nets bas-
ketball games (+ 61.8%). Conversely, boxing (including MMA matches), Cirque du
Soleil exhibitions, other entertainment events, other sports, Seton Hall Men basket-
ball games, and Disney-related events do not exhibit any statistical relationship with
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1245
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Evidence ontheimpact ofthePrudential Center oncrime in…
crime counts and thus do not have an impact on the number of crimes that occurred
in Downtown Newark over the period under analysis.
Importantly, the results of the study stimulate possible conjectures on why
different events impact crime in different ways. Albeit speculative, two main
hypotheses (that can be intended as complementary) can be set forth. The first
concerns the fact that different events attract different types of spectators with
different characteristics and lifestyles, thus modifying the underlying risk for
crime commission or victimization. The second, instead, refers to the possibly
different security and crime control strategies deployed for different event types.
For instance, it might be possible that for events attended mostly by families,
less security personnel and police are allocated, thus reducing guardianship and
increasing crime opportunities.
In terms of spatial distributions, we analyzed the point-wise occurrence locations
for six crime categories, plus all crimes aggregated together. We proposed the use
of the Fasano-Franceschini test, a statistical test that originally emerged in the field
of astronomy for expanding the Kolmogorov–Smirnov test and assessing whether
two random samples are drawn from the same distribution in a k-dimensional space.
In our case, we focused on the distribution of crime incidents in a two-dimensional
space, mapped by latitude and longitude, to investigate whether the distribution of
crime incidents differs in Downtown Newark between event and no-event time units.
The outcomes of the Fasano-Franceschini test indicated a complex picture. The spa-
tial distribution for overall crimes statistically differs comparing event versus no-
event time units, and the same pattern emerges comparing the spatial distribution
of auto thefts and thefts (the two most prevalent crime categories in the data). Con-
versely, no statistical differences are appreciated in relation to aggravated assaults,
burglaries, other crimes, and robberies.
Finally, investigating crime at the incident level to assess the relationship between
six location types and the moment when a crime occurred (again, discriminating
event versus no event), statistical outcomes suggest that for four of these location
types, no statistical correlation was detected. The only two exceptions were streets
and parking lots. Concerning the former, the odds that crimes are committed in the
streets are 29.3% lower when no events are in place at the Prudential Center. The
odds that crimes are committed in parking lots are instead 73.1% higher when the
facility is not active (although the coefficient is only significant at the 90% level).
The results of the location type analysis align with those found after comparing
spatial distributions, showing that the Prudential Center only partially qualitatively
modifies the characteristics of crimes in the area for most crimes and most location
types. Concerning the two significant results, some interpretations can be advanced.
The increase in the likelihood of crime incidents on the streets when the facility is
active might be explained by the higher number of potential targets visiting the area.
This aspect can be further enriched by the confluence of adversarial groups of peo-
ple, especially in sporting events with harsh rivalries between teams. With regards
to parking lots, the fact that crime incidents in parking lots are more likely when the
facility is not active can be also read in terms of variation in guardianship: while
such lots are under continuous guardianship during events, due to the continuous
flow of people parking and leaving, in no-event timeframes, such guardianship is
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1246
G.M.Campedelli et al.
1 3
significantly diminished, creating more opportunity for crime commission (both at
the property and instrumental levels).
A few limitations warrant highlighting. First, focusing on the downtown portion of
Newark has theoretical as well as logical justification, given that it would be challeng-
ing to assume that changes in crime occurring miles away from the Prudential Center
are precisely influenced by activity at the facility. Yet we acknowledge that we chose
clear, fixed boundaries for circumscribing the area under analysis, and there may be
crime events occurring just a few feet over those boundaries still being influenced by
the center, or alternatively, there may be premises—such as restaurants or taverns—
outside the boundaries having an impact on crime patterns in the area. The results
should thus be interpreted keeping this aspect in mind, namely, that the fixed nature
of the boundaries might escape residual crime dynamics. Second, no disaggregation
per crime type is provided in the analysis of crime trends via the negative binomial
models. We acknowledge that, in principle, disaggregating crime data would have
provided helpful information on specific dynamics, bearing practical and not only
empirical relevance. However, several crime categories are particularly sparse, and
this would have provoked significant power issues, thus offering an incomplete and
unstable set of results. Third, although we manually tried to extrapolate attendance
data from the Prudential Center website using the Wayback Machine, we could not
gather systematic information on the number of people attending each event. In fact,
we obtained a non-representative sample of attendance information that accounted for
around 65–70% of the events, while around a third were missing, hence invalidating
any possibility for analytical scrutiny. The hypothesis that attendance might in prin-
ciple play a role in crime variation when events in place align with previous work by
Marie (2016) and Mares and Blackburn (2019), which showed that, expectedly, the
higher the number of spectators attending sporting events, the higher the (positive)
impact on crime. Although we empirically demonstrated that the type of event has a
differential impact on crime, it may be that the number of people attending an event
could also impact crime rates, opening up avenues of future inquiry. In fact, previous
works by Marie and Mares and Blackburn only focus on unique types of events (i.e.,
soccer and baseball games), thereby leaving unanswered questions surrounding the
interaction between event typology and attendance. Better understanding the dynamics
governing the interplay between quality and quantity of spectators should be a priority
in future research on the link between sports, entertainment, and crime.
In general, the heterogeneity of results stimulates theoretical as well as policy
reflections. Theoretically, this study highlights how analyzing crime dynamics with-
out proper disaggregation poses the effect of shadowing critical micro-processes
peculiar to specific crime categories, locations, or surrounding environmental condi-
tions. While the literature on sports, entertainment, and facilities is abundant, it is
essential to consider the nuances and specificities that may emerge in different cit-
ies and for different event types, as this case study clearly suggests. The Prudential
Center certainly affects crime incidence in the surrounding Newark area. However, it
would be simplistic to say that the negative externalities of activity held at the facil-
ity are universal: only some events are more prone to be associated with an increase
in crime, and only some venues are more prone to be the target of crime when events
are in place. This speaks to the importance of theoretical reasoning that considers
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1247
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Evidence ontheimpact ofthePrudential Center oncrime in…
the complexity of crime in all its facets. Additionally, when compared with the study
by Kurland (2019), our results diverge in terms of the event-crime link. This is due
to our choice of focusing on the area surrounding the Prudential Center, suggesting
that scrutinizing the entire urban area might lead to spurious results.
Relatedly, the heterogeneity that emerged from this study also bears relevance to
policy matters. That different event types have different effects across crime types calls
for tailored programs and policies structured to optimally allocate resources considering
the risk of crime increases relative to each event typology. Some types of events will
require more resources to optimize crime reduction efforts. At the same time, patrolling
and monitoring by law enforcement during event times should take into account the fact
that, while most crimes occur in the exact spatial locations, robberies seem to differ, and
streets and parking lots exhibit different probabilities of being victimized in event versus
no-event time units. Law enforcement should thus respond to the changing dynamics
emerging in robberies when events are in place, as well as prioritize monitoring streets
which appear to be the riskiest locations for crime when the facility is active.
Future scholarship in this area of inquiry should investigate whether the heteroge-
neity and complexity of dynamics revealed by the present study of Downtown New-
ark also apply to other urban contexts across the USA, as well as abroad, in line with
other recent calls in this direction (Block & Kaplan, 2022). Applying our analytical
framework—which focuses not only on crime counts and incidence but also on spa-
tial characteristics of crime—across cities will provide essential knowledge on the
variety of effects that facilities and stadiums have on crime at different levels. This
type of comprehensive focus will thus equip scholars with a more solid understand-
ing of the facility-crime relationship as well as policy- and decision-makers with an
array of usable evidence-based indications of the possible negative consequences of
such buildings in an attempt to design urban development plans that consider secu-
rity as an important asset to protect in impacted urban contexts.
Supplementary Information The online version contains supplementary material available at https:// doi.
org/ 10. 1007/ s11292- 023- 09576-8.
Funding Open access funding provided by Northeastern University Library
Data availability All data and scripts used in the current work are publicly stored at the following link:
https:// doi. org/ 10. 5281/ zenodo. 74295 66
Declarations
Disclosure The analyses and conclusions presented here are those of the authors and should not be attrib-
uted to the Bureau of Justice Statistics or the U.S. Department of Justice.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is
not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission
directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen
ses/ by/4. 0/.
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G.M.Campedelli et al.
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and institutional affiliations.
Gian Maria Campedelli is a postdoctoral research fellow in Computational Criminology and Sociology at
theUniversity of Trento, in Italy. His research focuses on the study of homicide, organized crime and terrorism
throughthe development and application of computational and statistical methods. He is the author of the book
“MachineLearning for Criminology and Crime Research: At the Crossroads”, published with Routledge in 2022.
Eric L. Piza is Professor of Criminology & Criminal Justice. Director of Crime Analysis Initiatives, and
Co-Director ofthe Crime Prevention Lab at Northeastern University. His research is centered on the spa-
tial analysis of crimepatterns, evidence-based policing, crime control technology, and the integration of
academic research and policepractice.
Alex R. Piquero is Professor in the Department of Sociology & Criminology and Arts & Sciences Dis-
tinguishedScholar at the University of Miami (on public service leave while he is the Director of Bureau
of Justice Statistics).His research interests include criminal careers, criminological theory, public policy,
and quantitative methods. He isFellow of both the American Society of Criminology and the Academy of
Criminal Justice Sciences. In 2019, hereceived the ACJS Bruce Smith Sr. Award for outstanding contri-
butions to criminal justice and in 2020 he receivedthe ASC Division of Developmental and Life Course
Criminology Distinguished Achievement Award.
Justin Kurland currently works as a Principal Data Scientist in financial services, and prior to his current
role was aSenior Lecturer in the Department of Computer Science at the University of Waikato (New
Zealand). There hetaught several foundational graduate level courses on data analysis, methods, and secu-
rity, in addition to servingas a principal investigator on numerous large-scale security related grants.
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