Newton Crime Sci (2015) 430 DOI 10.1186s40163-015-0040-7.docx

Newton Crime Sci (2015) 4:30
DOI 10.1186/s40163-015-0040-7
R E S E A R C H
Crime and the NTE: multi-classification
crime (MCC) hot spots in time and space
Andrew Newton*
Abstract
This paper examines crime hot spots near licensed premises in
the night-time economy (NTE) to investigate whether
hot spots of four different classification of crime and disorder
co-occur in time and place, namely violence, disorder,
drugs and criminal damage. It introduces the concept of multi-
classification crime (MCC) hot spots; the presence
of hot spots of more than one crime classification at the same
place. Furthermore, it explores the temporal patterns
of identified MCC hot spots, to determine if they exhibit
distinct spatio-temporal patterns. Getis Ord (GI*) hot spot
analysis was used to identify locations of statistically
significant hot spots of each of the four crime and disorder clas-
sifications. Strong spatial correlations were found between
licensed premises and each of the four crime and disorder
classifications analysed. MCC hot spots were also identified
near licensed premises. Temporal profiling of the MCC hot
spots revealed all four crime types were simultaneously present
in time and place, near licensed premises, on Friday
through Sunday in the early hours of the morning around
premise closing times. At other times, criminal damage and
drugs hot spots were found to occur earlier in the evening, and
disorder and violence at later time periods. Criminal

damage and drug hot spots flared for shorter time periods, 2–3
h, whereas disorder and violence hot spots were
present for several hours. There was a small spatial lag between
Friday and Saturday, with offences occurring approxi-
mately 1 h later on Saturdays. The implications of these
findings for hot spot policing are discussed.
Keywords: Policing, Licensed premises, Alcohol, Multi-
classification crime (MCC) hot spots, Spatio-temporal analysis
© 2015 Newton. This article is distributed under the terms of
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(http://
creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium,
provided
you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and
indicate
if changes were made.
Background
There is a longstanding recognition that the locations
of alcohol consumption and crime co-occur (Gorman,
Speer, Gruenewald, & Labouvie, 2001; Home Office,
2003; Scott and Dedel, 2006; Newton and Hirschfield,
2009a). This often fuels the wider debate over the ‘causal’
versus ‘non-causal’ relationship between alcohol and
crime (Dingwall, 2013; Horvath and Le Boutillier, 2014).
A growing concern is the prevalence of clusters of crime,
termed hot spots, in urban areas with concentrations of
licensed premises, synonymous with the Night-Time
Economy (NTE). For the purposes of this paper licensed
premises are considered those selling alcohol for on and
or off premise consumption; examples include pubs,
bars, nightclubs, hotels, off licenses, supermarkets, con-

venience stores, restaurants, cafes, takeaways, cinemas
and social clubs. Sherman (1995, p 36) defines crime hot
spots as ‘small places in which the occurrence of crime
is so frequent that it is highly predictable, at least over
a 1-year period and this paper examines hot spots over
12–36 months. In addition to the known geographical
clustering of crime near licensed premises, NTE hot spot
areas also exhibit clear temporal patterns, especially on
Friday and Saturday evenings and early mornings, which
correspond with premise closing times (Block and Block,
1995; Newton and Hirschfield 2009b; Popova, Giesbre-
cht, Bekmuradov, & Patra, 2009; Uittenbogaard and Cec-
cato, 2012; Conrow, Aldstadt, & Mendoza, 2015). Thus
there are clear spatial and temporal patterns to NTE
crime hot spots.
There is a sound theoretical basis for the presence of hot
spots in the vicinity of licensed premises. Routine activity
theory (Cohen and Felson, 1979) and crime pattern the-
ory (Brantingham and Brantingham, 1993) contend that
persons, both potential offenders and victims, exhibit sys-
tematic movement patterns governed by their day to day
undertakings, termed routine activities. Certain places
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*Correspondence: [email protected]
The Applied Criminology Centre, The University of
Huddersfield,
Queensgate, Huddersfield HD1 3DH, UK
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of 12Newton Crime Sci (2015) 4:30
are frequented regularly, for example home, place of work
or leisure, termed activity nodes. The routes travelled
between nodes are known as paths. This movement devel-
ops a person’s awareness space, and crime is shown to be
more likely on the edges of these activity nodes (Bow-
ers, 2014). Places at which several offenders and victims
converge form multiple awareness spaces, which increase
the likelihood of crime. Eck, Clarke, and Guerette (2007)
identify a number of ‘risky facilities’ where concentrations
of crime are evident. Indeed, a small minority of facili-
ties contribute the majority of offences at all risky facili-
ties, termed the ‘iron law of troublesome places’ (Wilcox
and Eck, 2011: 476). Examples include shopping centres,
busy road junctions, hospitals, schools, train and bus sta-
tions, and entertainment districts. Places with clusters of
licensed premises represent recreational activity nodes,
where there is a convergence of people in time and space.
This coming together may create unplanned but favoura-
ble crime opportunities, termed crime generators; or draw
in offenders to bars and localities with known opportuni-
ties for offending, termed crime attractors (Brantingham
& Brantingham, 1995). Within NTE areas both of these
eventualities are plausible.
A number of explanations exist for the occurrence of
crime in NTE areas (for good overviews see Finney, 2004;
Graham & Homel, 2008). These include: cultural factors,
relating to societies use and acceptance of alcohol; per-
son factors based on an individual’s responses and beliefs
about alcohol consumption; the psychopharmacologi-
cal properties of alcohol and their influence on an indi-
vidual’s behaviour; and contextual factors, the physical

and social circumstances of where and when alcohol is
consumed. Recently a focus for NTE research has been
on premise density and premise opening hours. Explana-
tions for crime have focussed on: NTE places deemed to
have ‘too many’ licensed premises, those saturated with a
high density of premises (Livingston, 2008; Pridemore &
Grubesic, 2013); and, premises open ‘too long’, with con-
cerns over the length of time premises can remain open
for, based around extensions granted in trading hours
(Chikritzhs & Stockwell, 2002; Holmes et al., 2014). What
is clear is the relationship between crime and alcohol is
multi-faceted. A useful explanation is offered by Elvins
and Hadfield (2003) who suggest a combination of fac-
tors are likely account for crime in NTE areas, including:
places with high densities of licensed premises in urban
areas; the convergence of large number of persons at
these places; crowding of persons within drinking ven-
ues in close proximity in confined spaces, often leading to
‘vertical drinking’; the consumption of alcohol, often in
large quantities; poor management of NTE places; and,
the cumulative build up of ‘environmental stresses’ over
the course of an evening.
Efforts to tackle problems of crime in the NTE have
predominantly but not exclusively focussed on: better
place management (Madensen & Eck, 2008); alcohol edu-
cation and awareness schemes; regulation of licensing,
legislation and enforcement (Hadfield and Newton 2010);
increasing the costs of unit prices of alcohol (Booth et al.,
2008); regulating the number of, and opening times of
premises (Chikritzhs & Stockwell, 2002); and high vis-
ibility police patrols. Whilst the merits of each approach
have and will continue to be debated in the literature (see
Graham & Homel, 2008; Humphreys & Eisner, 2014; Hol-
mes et al., 2014), the focus of this paper is on the use of
police patrols in NTE areas.

A recent movement in policing has been a resurgence
of hot spot policing, ‘targeted on foot patrols’, fuelled by
the willingness of a number of police forces to implement
randomised control trials (RCTs) of hot spot policing
effectiveness (Ratcliffe, Taniguchi, Groff, & Wood, 2011;
Braga, Papachristos, & Hureau, 2012; Groff et al., 2015).
Successes are evident for hot spot policing targeting bur-
glary, repeat calls for service, nuisance bars, drugs, and
violent crime, in particular when focussed on hot spots
defined tightly in both place and time. A caveat identi-
fied in the literature is that the effectiveness of the polic-
ing tactic used often is dependent on the type of hot spot
policed.
The process of hot spot policing involves identifying
hot spot areas, and then subsequently targeting patrols at
these places in a systematic fashion. It is contended here
that this reflects more general current trends in policing,1
of using evidence gleaned from crime analysis or crime
intelligence to inform police response. Many including
the author advocate a problem solving/evidence based
approach to policing and crime reduction. Two of the
most well know examples of this are Problem Orientated
Policing (Goldstein, 1990) and Intelligence Led Policing
(Ratcliffe, 2008). At the simplest level of explanation, the
analyst or police officer is encouraged to: firstly identify a
crime problem through some form of suitable analysis of
crime or other data; then, to further examine the identi-
fied problem to understand the mechanisms driving it
and the context of its setting; the next step is to identify
and implement possible solutions; and the final stage is to
monitor and or evaluate the effectiveness of the measure
implemented.
This paper focusses on the first stage of the pro-

cess, known as ‘scanning’ in the SARA model (Ashby
& Chainey, 2012) or ‘Intelligence’ in the 5Is approach
1 In the UK the College of Policing has recently launched the
What Works
Crime Reduction Centre,
http://whatworks.college.police.uk/Pages/default.
aspx; the US has a long standing Centre for Problem Orientated
Policing
(POP) http://www.popcenter.org/about/?p=whatiscpop; and the
Society of
Evidence Based Policing launched in 2012
http://www.sebp.police.uk/.
http://whatworks.college.police.uk/Pages/default.aspx
http://whatworks.college.police.uk/Pages/default.aspx
http://www.popcenter.org/about/?p=whatiscpop
http://www.sebp.police.uk/
Page 3 of 12Newton Crime Sci (2015) 4:30
(Ekblom, 2011). The process of identifying crime hot
spots for subsequent deployment of hot spot policing
tends to be atemporal. This is a reflection of both soft-
ware availability and analytical skills (Newton and Fel-
son, 2015). Furthermore, sample sizes are larger when
crime is not dissected by time of day, which increases the
robustness of hot spot analysis. Moreover, once a crime
hot spot has been identified, subsequent analysis by time
of day enables identification of when to implement hot
spot policing at detected hot spots. Perhaps an important
component of high crime places overlooked here is that
analysts are encouraged to be crime specific, and thus
tend to examine single crime classifications, for example
violent crime. This is not unexpected, the spatial patterns

of burglary will not closely resemble those of street rob-
bery, nor should they be expected to.
However, areas with concentrations of licensed prem-
ises are known to be highly criminogenic and not just for
violence. Associations have been demonstrated between
licensed premises and a number of crime types, most
notably violence and aggression, but also criminal dam-
age, disorder, and drug use (Scott & Dedel, 2006; Graham
& Homel, 2008; Newton and Hirschfield, 2009b). Indeed
Yang (2010) demonstrated longitudinally that correlations
in time and place exist between violence and disorder.
Furthermore, offenders have been shown to be versatile
in the types of crime they commit (Roach & Pease, 2014),
and indeed police may overestimate the specialised nature
of offending. Thus, if offenders are known to commit sev-
eral types of crime, and several types of crimes have been
shown to be related to NTE places, should analysis of crime
at these places be focussed on single crime classifications?
This discussion has demonstrated that: particular NTE
places experience more than one crime type; offenders
are known to be versatile in the types of crime they com-
mit, and that one of the limitations of spatio-temporal
analysis is that segmenting data in both time and place
can substantially reduce sample size. Combing several
‘related’ crime types into a single analysis is a possible
solution here. Therefore, this research aims to investi-
gate whether multi-classification crime (MCC) hot spots
exist near licensed premises, and if so, do they exhibit
distinctive spatio-temporal patterns. More specifically, it
examines four crime types known to be associated with
licensed premises, namely violence against the person,
criminal damage, drugs, and disorder incidents (anti-
social behaviour), to ascertain how these crimes manifest
in NTE hot spots both in time and place. The following

research questions were formulated for this study.
Research questions:
• Is there spatial correspondence between the locations
of hot spots for different crime and disorder classi-
fications near licensed premises (violence, criminal
damage, disorder and drugs)?
• Do MCC hot spots correspond temporally, that is to
say, when a place is a hot spot for violence, is it also a
hot spot for criminal damage?
• Do MCC hot spots fluctuate over time, for example
does a place experience criminal damage, and then
later in the day or a different day of the week experi-
ence violence against the person?
Methods
Data
This study used crime and disorder data for an
anonymised case study area in England. Its residential
population is approximately 1.5 million persons and
includes a mixture of large towns and several rural vil-
lages, covering a geographical area of approximately
600 km2. Offence data were obtained for the 3 years
period 1st January 2007 to 31st December 2009 for
crimes categorised as violence against the person
(VAP), criminal damage (CD), and drugs; based on
the UK Home Office 2010 counting rules for recorded
crime. Incident data for calls for service for disorder
(non-crimed) were also obtained for the 12 month
period 1st January to 31st December 2007. An addi-
tional dataset used was a licensed premise database
for the case study area, and 6047 premises were iden-

tified as ‘open’ during the considered time period
(2007–2009).
Data processing
The crime and disorder data were cleaned to include
only those containing a known time of offence, and
those with geo-spatial references outside of the case
study area were also excluded. This resulted in a sam-
ple of: 64,440 VAP offences; 83,159 CD offences; 18,270
drugs offences, and 346,022 disorder incidents. A Geo-
graphical Information Science (GIS) software program
was used to calculate the distance from each offence
or incident to the nearest licensed premise, and the
results of this are shown in Table 1. This demonstrates
that for all crime and disorder types the mean distance
to a licensed premise was approximately 130–170 m.
Median distances ranged from 80 to 125 m. Considering
these distances and other studies using buffer analysis
to examine crime near licensed premises (Newton and
Hirschfield, 2009b; Ratcliffe, 2012), a 250 m thresh-
old was selected as an appropriate distance to repre-
sent crime and disorder ‘near’ licensed premises in
this study. As shown in Table 2, for all crime and dis-
order types analysed, 50–65 % of all crime and disorder
offences (varying by crime or disorder classification)
occurred within 250 m of a licensed premise.
The temporal nature of offences
It was previously identified that NTE hot spots exhibit
distinct spatial and temporal patterns, with crime peaks
evident on Friday and Saturday evening, or the early
hours of Saturday and Sunday morning, around premise

closing times. In order to examine this further the time of
all crime and disorder in NTE hot spots (within 250 m)
were re-coded with a value representing both the time of
day and day of week (termed week-hour, ‘WH’ for this
study). There are a total of 168 h in a week, and thus each
crime and disorder incident was assigned a WH2 value
from 6 to 173.
Figure 1 shows the weekly temporal distribution of
each crime and disorder type and reveals distinctive pat-
terns in the WH of VAP, CD, drugs and disorder. For all
crime and disorder types there are clear peaks during the
evening and early hours of the morning on all days. How-
ever, there are some differences in the patterns observed;
the highest peaks for disorder are on Friday evening fol-
lowed by Saturday evening, with lower peaks from Sun-
day though to Thursday; VAP peaks on Saturday evening,
followed by Sunday, Saturday, and Monday, with lower
peaks Tuesday to Thursday; drug offences peak on Satur-
day evenings, followed by Friday and Sunday, with more
2 A value of 6 represents the time period 6.00 a.m. to 6.59 a.m.
on a Sunday
morning; 23 represents 11.00 p.m. to 11.59 p.m. on a Sunday
evening; 24
represents midnight to 0.59 a.m. on a Monday morning; 47
represents 11.00
p.m. to 11.59 p.m. on a Monday evening; 48 is midnight to 0.59
a.m. on a
Tuesday; and so forth. A look up reference for this is provided
in Additional
file 1: Appendix S1.
irregular peaks during the rest of the week; for CD the
highest peaks are Sunday evening, followed by Saturday
and Friday; peaks during the rest of the week are again

lower, but the reduction is less than that of other crime
types. Disorder, CD and drugs also exhibit two separate
peaks during Saturday evenings which are not evident for
VAP. CD tends to have two distinct peaks in the evening
most days of the week, unlike disorder and VAP which
have single evening peaks all days except Saturday. Over-
all, there are clear and distinct temporal patterns evident
for each crime type.
It is possible that using 3 years of data may skew the
results as the temporal patterns of each crime may have
changed over time. In order to test this the WH val-
ues for each time period were compared by year, thus
WH values for 2007 were compared with those of 2008
(2007–2008), and WH values for 2008 compared with
those of 2009 (2008–2009). Mann–Whitney tests were
used to compare the means (non-parametric independ-
ent samples). The results were as follows: for VAP 2007–
2008, z = − 0.253, p = 0.8; for VAP 2008–2009 z = − 0.7,
p = 0.48; for CD 2007–2008 z = − 0.35, p = 0.25; for
CD 2008–2009 z = −0.18, p = 0.6, for drugs 2007–2008
z = −1.5, p = 0.12, and for drugs 2008–2009 z = −0.46,
p = 0.09. This suggests that there were no significant dif-
ferences in WH crime times for VAP, CD or drugs over
any of the comparative time periods, and therefore that
the WH temporal patterns of each of the three crime
types remained stable over the 3 years period. As only
12 months of data were available for disorder, tests for
this were not conducted. However, it is assumed that
these are also likely to have remained stable, based on the
stability of the recorded crime results.
Identifying hot‑spots
A range of methods can be used to identify crime hot
spots including thematic mapping, kernel density estima-
tions, nearest neighbourhood hierarchical clustering, and

the Getis Ord GI* statistic (Eck, Chainey, Cameron, &
Wilson, 2005; Chainey & Ratcliffe, 2005; Levine, 2015).
For this analysis the Getis-Ord GI* method (Getis & Ord,
1992; Ratcliffe, 2010; Chainey, 2014) was used to identify
significant hot spot areas of crime around licensed prem-
ises. The advantage of this method over other hot spot
mapping techniques is that it identifies small grid areas
that are statistically significant, and returns a z3 score that
measures the strength or intensity of the clustering and
its significance. This method also produces tightly
defined hot spot areas appropriate for hot spot policing.
3 The higher the z score the greater the clustering, and a z score
equal to or
above 1.960 is significant at the 95 % confidence level, and
equal to or above
2.576 significant at the 99 % level.
Table 1 Average distances of offences to licensed prem-
ises (metres)
Offence/incident N Distance to nearest licensed
premise (m)
Mean Median SD
Disorder 346,022 167.5 119.5 197.7
Violence against person 64,640 132.4 84.2 173.4
Criminal damage 83,159 163.4 124.6 178.6
Drugs 18,270 149.1 85.4 225.6
Table 2 Percentage of offences and incidents near licensed
premises (within 250 m)

Offence/incident N < 250 m Percentage Total N
Disorder 188,756 54.6 346,022
Violence against person 41,538 64.3 64,640
Criminal damage 44,570 53.6 83,159
Drugs 11,870 65.0 18,270
Using the GIS software a 250 m grid matrix was gener-
ated across the study area resulting in 104,958 grids. A
GIS was used to count the number of crimes in each grid
repeated for VAP, CD drug offences, and disorder inci-
dents. This analysis used all crimes within the case study
area. An alternative approach would be to only select
crimes within 250 m of premises, but this may skew the
hot spot generation. For each of the four classifications of
crime and disorder, GI* hot spots were calculated4 using
ArcGIS spatial statistics toolbox. Figure 2 shows the case
study area, the 250 m grids, and the location of licensed
premises. The results of the hot spot analysis are shown
in Fig. 3a–d, which maps the location of hot spots. Note
in these maps only grids which are clustered with 99 %
confidence or greater (z ≥ 2.576) are displayed, with hot
spots superimposed by the locations of licensed premises
4 The parameters for this were to use a fixed distance band,
with a threshold
(spatial lag) of 355 m (based on 250 m grids).

in the case study area. The images are rotated for
anonymity.
There are distinct spatial hot spots evident in Fig. 3,
which correlate with urban areas containing high densi-
ties of licensed premises. Upon first glance similar hot
spot patterns are apparent for VAP, CD, disorder and
drugs. However a more detailed visual inspection reveals
subtle differences. The extent of the hot spots around
urban centres is greater for VAP and disorder, and more
tightly concentrated for drugs and CD. Towards the bot-
tom of the case study area there are hot spots of VAP, CD
and disorder, but not for drug offences. Towards the right
of the map there is an area with large concentrations
of VAP, drugs, disorder, and CD, but close inspection
reveals the extent of this is much more spread for VAP
than the other three crime types. On these maps only
grid cells that are significant hot spots at 99 % confidence
interval are displayed. There were 2970 such cells, and
these cells are now examined further.
Fig. 1 Weekly-hourly2 crime frequencies (Sunday to Saturday)
four each of four crime types (a–d). CD criminal damage, VAP
violence against person
Results
The first research question was to examine the degree
to which hot spots of different crime classifications co-
exist spatially, in other words occur at the same place.
Analysis of all grids in the study area using Spearman’s
Rank revealed strong statistically significant correlations
for each crime and disorder type (Table 3) with the loca-

tion of licensed premises; the strongest relationship was
between premises and disorder, followed by CD, VAP,
and drugs. All crime and disorder types were correlated
with premises at R > 0.7, p < 0.01 which indicates a high
degree of correlation between the location of licensed
premises, and crime and disorder events in the case study
area.
Further analysis was undertaken using only grids sig-
nificant at the 99 % level (2970) which contained a sig-
nificant hot spot for at least one of the four crime and
disorder classifications examined. 2435 grids contained
a licensed premise, and unsurprisingly all of these grids
were identified as a statistically significant hot spot for
at least one crime type. Further analysis revealed 2485
grids of the 2970 were hot spots for VAP (83 %), 2385
for CD (80 %), 2160 for disorder (72.7 %), and 1307 for
drugs (44 %). Each grid could contain a hot spot for one,
two, three, or all four crime types, and a Conjunctive
Case Analysis (CCA, Miethe, Hart, & Regoeczi, 2008)
was used to examine the 256 (44) possible combinations
here.5 The results of this are presented in Table 4. This
found 1214 grids, 40 % of the significant crime hot spot
grids, were statistically significant hot spots for all four
crime classifications. A further 663 grids (22 %) were
significant hot spots for at least three types of crime.
This shows strong evidence of an overlap in the location
of hot spots for VAP, disorder, CD and drugs near
licensed premises and suggests strong evidence in the
case study area that MCC hot spots are present near
licensed premises.
Profiling the ‘hottest’ hot spots
The research has thus far demonstrated that MCC hot
spots are present spatially, thus hot spots of VAP are also

hot spots of CD for example. The purpose of research
questions two and three are to further examine the MCC
hot spots temporally, to ascertain whether the different
crime types found in the MCC hot spots occur at the
same time, at different times of day, or different days of
the week. Therefore the top twenty hot spot grids were
identified for further profiling. To determine these top
twenty cells, the ‘hottest hot spots’, cells that were statisti-
cally significant hot spots for all four types of crime and
disorder (VAP, CD, drugs and disorder) were identified.
There were 1214 of these cells. Cells with the highest
combined z scores6 were selected to represent the twenty
‘hottest’ hot spots. A profile of each of these cells is pro-
vided in Table 5. At these twenty 250 m grid cells over the
3 years period (12 months for disorder) there were a high
number of crime and disorder incidents ranging from: 78
to 802 for VAP; 252 to 1736 for disorder; 37 to 182 for
CD; and 8 to 265 for drugs. The number of license prem-
ises in each grid ranged from a minimum of 3 to a maxi-
mum of 96. In order to examine the temporal profiles of
these cells, the WH values of each crime type for each
cell was calculated, and the results of this are presented
in Fig. 4. The frequencies of offences by time of day were
divided into five equal quintiles, and these are colour
coded as per the table key. Those in red represent the
20 % of times with the highest levels of crime for each
classification, VAP, CD, disorder and drugs.
Figure 4 shows the temporal profiles of the 20 hot-
test MCC hot spots. There were seven WH time periods
(each WH is 1 h of the week) that had high levels (col-
oured red in Figure) of crime and disorder for all four
crime and disorder categories at the same time and
same place: Thursday 2.00 a.m. to 2.59 a.m.; Friday 1.00
5 An alternative here may be the use of Multiple Classification

Analysis
(MCA), also known as factorial ANOVA. However, as this is
used for linear
data, and spatial crime data often follows a negative binomial
distribution,
this was not considered appropriate here.
6 Calculated as combined z score of each of four crime
classifications from
GI* analysis.
Fig. 2 Case study area with 250 m grids and licensed premises
a.m. to 2.59 a.m.; and Saturday midnight to 02.59 a.m.
There were some further distinctive temporal patterns
identified in the MCC hot spots. Disorder is prevalent
Wednesday through Sunday evenings; on Sunday peaks
were at 7.00 p.m., 9.00 p.m., and from midnight to 2.59
a.m.; on Wednesday from 1.00 a.m. to 2.59 a.m.; on
Thursday from midnight to 3.59 a.m.; on Friday from 6.00
p.m. until 2.59 a.m.; and then on Saturday from 7.00 p.m.
until 3.59 a.m. Thus there is an extended period of disor-
der on Friday and Saturday, which last for several hours.
There are also some disorder peaks on Tuesday afternoon
not found for other crime types. VAP followed similar
patterns to that of disorder. However, the length of the
peaks was shorter, occurring slightly later on Sunday until
3.59 a.m., and generally VAP starts later in the evening
Fig. 3 GI* hot spot maps of crime and licensed premises by
each of four crime types (a–d) (>99 % significant hot spots
shown). CD criminal dam-

age, VAP violence against person
Table 3 Correlations between licensed premises and crime
hot spots (250 m grid based analyses)
Spearman’s Rho correlation
with licensed premises
VAP CD Drugs Disorder
N 10,948 10,948 10,948 10,948
P 0.805 0.913 0.712 0.937
Sig 0.001 0.001 0.001 0.001
than disorder. The corresponding periods of disorder and
violence also seem to occur 1 h later on a Saturday than
they do on a Friday. Drugs followed a more unusual pat-
tern; offences occurred on Thursday to Sunday evenings
correlating with VAP and disorder, and there were some
unique peaks early Friday morning at 9.00 a.m. and 11.00
a.m. Drug offence peaks tended to be for 1 h only with
the exception of Thursday through Sunday. CD tended
to occur at much earlier time periods during the day, for
example: on Sunday between 6.00 p.m. and 8.00 p.m., and
then 10.00 p.m. to midnight; at 5.00 p.m. on a Monday
and Thursday; and 5.00 p.m. and 7.00 p.m. on a Saturday.
Discussion of findings
The top 20 ‘hottest’ hot spots identified (based on 250 m

grid cells) accounted for less than half a percent of all the
Table 4 Hot spot grids (99 % significance) and crime
and disorder types
Crime type VAP CD Disorder Drugs Total
Number of grids 2485 2385 2160 1307 2970
Percentage of grids (%) 83.7 80.3 72.7 44.0 100.0
CCA analysis of grids by hot spot types
VAP CD Disorder Drugs Number
of cells
Presence (1) or absence of a hot
spot (0) of a hot spot
1 1 1 1 1214
1 1 1 0 609
1 0 1 1 25
0 1 1 1 16
1 1 0 1 5
1 0 1 1 5
0 1 1 1 3
Table 5 Top 20 grid profiles (the hottest hot spots)
Z score based on Getis Ord (GI*) hot spot significance

(>2.576 = 99 % significant)
Grid_ID Premises (N) VAP (N) VAP
(z score)
Disorder (N) Disorder
(z score)
CD (N) CD
(z score)
Drugs (N) Drugs
(z score)
Total z All Crime
(N)
54124 63 530 106.86 784 79.88 143 53.27 115 88.87 4920.74
1602
54125 17 146 110.88 800 85.18 58 71.53 42 98.01 7206.67 1056
54126 5 92 53.64 338 54.96 85 60.76 28 51.21 3220.20 553
54417 19 187 92.54 532 64.97 37 36.61 39 78.63 3035.92 809
54418 44 756 126.20 1736 94.52 172 58.94 187 109.04 6647.91
2871
54419 35 468 120.32 876 90.04 182 71.75 129 103.21 7615.46
1685
54420 3 126 55.78 384 53.56 126 57.49 54 52.79 3143.88 704
54712 9 224 103.58 498 83.26 101 55.67 53 92.81 5353.51 887

54713 49 78 95.60 266 74.95 67 66.00 22 85.40 5807.23 439
54714 8 87 50.04 252 41.41 56 52.54 26 49.79 2707.54 427
55006 75 124 56.56 472 50.91 90 43.30 27 62.40 2809.36 718
55007 96 83 54.28 348 50.54 72 51.74 30 53.89 2893.05 538
55301 48 205 58.52 266 56.72 103 60.62 49 49.79 3133.43 635
55595 7 96 54.69 338 50.27 79 57.71 8 46.01 2760.01 527
62448 16 202 78.72 542 57.60 93 36.24 56 73.11 2786.24 910
62449 8 181 88.94 642 68.99 63 43.52 83 87.14 3950.09 981
62450 4 100 73.80 436 62.44 49 41.63 28 81.31 3520.88 622
62742 11 185 80.64 458 61.20 66 38.65 35 81.78 3302.21 756
62743 22 802 90.40 1234 76.17 182 49.05 265 94.39 4796.07
2539
62744 23 166 77.13 1018 64.33 78 41.48 42 82.57 3566.55 1319
Totals 562 4838 12,220 1902 1318 20,578
grids that contained a crime or disorder incident (6165
cells), yet contained over 5 % of all crime and disor-
der incidents analysed across the entire case study area.
Moreover, a 7 h time window (Thursday 2.00 a.m. to 2.59
a.m., Friday 1.00 a.m. to 2.59 a.m., and Saturday midnight

to 02.59 a.m.), which represented 4 % of the 168 WH
intervals over a week), accounted for nearly 15 % of all
crimes at these top 20 hot spots alone. Therefore crime
is highly concentrated at these times in these places. This
7 h time frame is important as at these times MCC hot
spots co-existed both in time and space, for all four crime
classifications examined. The most plausible explanations
for this are the high volumes of persons likely to be pre-
sent at these times and places create multiple opportuni-
ties for crime, supported by crime pattern theory, routine
activity theory, and the non-specialised nature of many
offenders. Indeed conterminously at the same places and
locations there may be suitable targets and lack of capa-
ble guardians in these micro places for drugs, criminal
damage, disorder and violence. At these time periods hot
spot policing may require a range of tactics, due to the
diverse nature of multiple crime types prevalent.
At other times of the day MCC hot spots were also
evident but not for all crime types. On Friday and Satur-
day afternoons disorder was evident from 6.00 p.m. until
the early hours of the morning, whereas violent offences
tended to occur after midnight. This may be reflective of
a number of factors, perhaps disorder is a signal crime of
later violence (similar to the Innes, 2004). Alternatively
later in the evening, the number of persons in the NTE
settings may increase, but to fewer locations; cumula-
tively more alcohol is consumed, and the result that dis-
order may escalate into more serious violence. Criminal
damage offences occur earlier in the evening than vio-
lence. An interesting finding is the apparent spatial lag
between Friday and Saturday; both have similar patterns
but offences are approximately 1 h earlier on Fridays.
This may reflect cultural difference and routines; those
who partake in the NTE on Friday’s may do so straight

from work, whereas those who go out on Saturdays may
have constrained activities on Saturday afternoons, or go
out with different friend groups or their partners, thus
drinking in the NTE may start slightly later on Saturdays.
There are a number of limitations to this study. Police
recorded crime and disorder data is known to be sub-
ject to both underreporting and errors in the accuracy
of geo-coding (Chainey and Ratcliffe, 2005; Newton and
Hirschfield, 2009a). It would be useful to supplement this
data with hospital accident and emergency data (A & E)
or ambulance data. According to Shepherd, Ali, Hughes,
and Levers (1993) six in seven of those attending A & E
for violent injuries are not in recorded crime statistics.
However, health data does not always contain location
specific information on when and where crime occurs,
and this data is not always available to the police. It is
suggested a more robust future analysis incorporating
A & E data is likely to confirm the presence of MCC hot
spots near licensed premised.
There are limitations in the arbitrary 250 m buffer
distance, and the use of the GI* statistic. Analysis using
Time of Day 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
24 1 2 3 4 5
Sunday Disorder 22 30 18 22 26 26 46 48 64 48 50 50 80 92 74
96 74 72 112 110 128 54 60 20
VAP 4 1 8 2 4 6 9 6 15 34 24 22 31 29 43 37 62 97 71 113 90
74 35 10
CD 4 5 2 1 4 1 6 5 6 8 6 19 22 25 15 17 22 15 27 14 14 18 21 5
Drugs 0 2 0 0 2 0 2 5 2 4 4 7 9 9 11 15 8 41 16 17 10 9 1 0
Monday Disorder 4 10 12 30 34 44 76 58 50 72 50 78 76 68 64

66 62 74 60 88 80 48 16 4
VAP 6 3 6 6 5 9 12 11 21 19 27 21 23 19 19 27 36 69 56 58 57
61 21 3
CD 1 4 3 7 1 1 9 6 6 10 12 30 18 18 16 16 17 27 14 23 12 16 8
6
Drugs 0 0 0 0 2 5 2 4 7 10 11 8 7 6 4 9 6 13 19 20 6 8 2 0
Tuesday Disorder 6 6 26 24 32 64 60 94 102 64 104 80 72 84 74
64 52 64 56 68 76 58 20 6
VAP 1 3 2 3 10 12 10 17 21 29 20 18 15 28 22 25 41 37 37 48
50 40 20 1
CD 0 0 3 5 3 5 6 7 9 6 12 27 20 11 9 18 15 13 16 14 12 10 6 5
Drugs 2 0 1 0 4 0 6 1 9 9 6 2 7 3 11 7 16 9 28 12 0 1 6 0
Wednesday Disorder 4 12 20 30 58 52 74 84 90 86 66 54 72 62
82 88 68 74 74 94 100 60 22 8
VAP 2 2 1 7 6 5 9 17 24 33 21 15 21 29 27 29 24 50 35 44 51
39 16 5
CD 1 3 0 4 4 7 6 5 6 12 9 18 23 9 11 17 15 20 14 18 16 7 8 0
Drugs 0 0 0 8 4 5 9 5 5 8 4 2 3 8 13 7 8 3 4 8 5 3 4 0
Thursday Disorder 6 2 14 32 44 50 52 80 72 68 82 66 82 66 62
66 58 80 132 154 138 166 80 22
VAP 4 0 4 7 13 10 14 7 13 22 25 16 18 10 21 23 26 37 73 82 90
83 37 7
CD 1 2 4 2 3 0 3 4 3 3 13 27 8 19 10 10 8 13 18 19 22 24 12 3
Drugs 0 0 1 7 3 6 5 9 7 2 3 7 0 4 8 4 2 11 52 22 18 5 4 3
Friday Disorder 6 6 22 28 40 60 42 54 74 88 90 68 92 122 128
140 140 172 226 228 190 150 84 30
VAP 1 5 1 4 5 15 17 10 18 20 17 19 15 10 20 29 37 48 92 112
101 90 45 6

CD 1 4 4 5 5 1 10 5 11 5 14 22 19 11 17 15 17 23 21 31 23 18
10 6
Drugs 0 0 2 14 6 12 5 8 3 2 2 4 4 7 2 4 10 22 24 42 16 20 10 2
Saturday Disorder 22 14 26 22 48 50 70 66 92 82 96 118 82 106
108 104 146 202 286 338 312 244 142 54
VAP 2 1 4 5 5 8 11 7 20 21 16 28 17 25 32 44 37 65 123 178
173 121 58 17
CD 2 1 2 1 0 4 6 8 6 5 8 30 16 26 10 19 22 15 33 35 28 28 26
14
Drugs 0 0 0 3 0 5 0 4 4 4 5 5 6 12 11 11 13 36 45 76 51 27 11 6
Fig. 4 The ‘Hottest’ hot spot profiles by time of day and crime
type (MCC hot spots): values indicate crime counts
alternative buffers (100 m, 400 m) found no discernible
differences in patterns of crime observed. A possible lim-
itation of the GI* is it identifies too many hot spot areas
significant at 99 %. Future analysis could compare the use
of a corrected Bonferonni approach rather than Gausian
for determining Z-score (Chainey, 2014). This technique
also identifies cells that have low crime counts, as it is
based on neighbourhoods surrounding cells rather than
just inside a cell in its calculation; alternative hot spot
techniques should be used explored and compare MCC
hot spots.
Conclusions
This paper has presented strong evidence for the pres-
ence of MCC hot spots near clusters of premises,

known to be particularly criminogenic places. This is
not surprising, given the literature on crime oppor-
tunity, crime pattern theory, routine activities, risky
facilities, and crime attractors and generators. How-
ever, what this research does begin to question is the
conventional wisdom of hot spot analysis and hot spot
policing being wholly crime specific, using single crime
classifications at highly criminogenic places. Hot spots
of VAP, CD, drugs and disorder were identified at the
same locations in the study area, near to licensed prem-
ises. Moreover, the results show that at particular time
periods (seven hourly periods of a 168 h week) all four
crime and disorder types occurred conterminously in
both time and space. At other times only one or two hot
spots were present, and at some times of the day hot
spots were not found. This has clear implications for hot
spot policing in terms of tactics used and when best to
target resources. Further exploration and explanation
of these patterns is warranted to assist in effective hot
spot policing deployment and tactics at MCC hot spot
locations.
A range of methods could be incorporated to refine
future analysis. In particular more statistical time based
analysis should test: whether MCCs are clustered in time
and space; if the space–time clustering occurs continu-
ously or within defined time periods; or if there is a space
time interaction (Levine, 2015). Suggested tests here are
to use the Knox and Mantel tests to examine the interac-
tions between licensed premises and the MCC hot spots
identified. Furthermore circular statistics could be incor-
porated, for example the use of Rayleigh’s test to exam-
ine significant clustering by time of day, or the Watsons
U test to examine for differences in two temporal data-
sets (Wuschke, Clare, & Garis, 2013) by month, season
or year.

As observed by Townsley (2008) characteristics of
crime hot spots can alter over time, with periods of emer-
gence, persistence, and decline. Therefore any future
analysis that is developed should also consider how
MCC hot spots may emerge and dissipate over time near
licensed premises, and whether they are stable hot spots
or occur more sporadically. Moreover, there are seasonal
variations in crime patterns and discretionary routines
influenced by daylight hours and temperature (Tompson
& Bowers, 2015) and this may influence MCC hot spots
near licensed premises.
At present there are a number of studies using predic-
tive crime mapping or crime forecasting (Chainey, 2014).
Perhaps predicting MCC hot spots should form part of
this research. Indeed, Shekhar, Mohan, Oliver, and Zhou
(2012) attempt to do similar, by testing for the emergence
of crime trends with multiple crime types. MCC hot
spots have been identified near licensed premises, but
perhaps alternatives exist, for example: burglary hot spot
analysis could also consider patterns of theft of, and theft
from vehicle; the locations of street robbery could be
compared with pickpocketing and theft from person; at
drug locations a number of crimes associated with illicit
trade could be examined. In other places known to be
criminogenic, it may be important to identify alternative
configurations of MCC hot spots.
VAP, CD, drugs and disorder have all been shown to
relate to licensed premises, but more detailed informa-
tion on types of premises, density and opening hours
should also be taken into account before prioritising
hot spot policing. Indeed a final question that remains
is the implications of this research for hot spot polic-

ing and resource targeting. It is possible to continue to
police hot spots based on single crime types effectively.
It is not known if focussing on the places and times of
MCC hot spots is likely to be more effective in reduc-
ing crime, as theoretically more offenders are likely to
be present at MCC than single crime hot spots, thus
police may be more likely to deter or apprehend offend-
ers at MCC hot spots. However, tactically it may be
more difficult to police MCC areas, targeting multiple
types of crime may require several concurrent tactics
that may conflict. MCC hot spots have been shown to
contain different crime types over time, criminal dam-
age and disorder earlier in the day and violence at later
times. It is not known if early intervention here would
reduce crime at later times of the day, or if police would
need to remain at these MCC hot spots for longer
time periods. It is suggested an RCT of MCC hots spot
patrols near licensed premises may shed some light on
this question.
Additional file
Additional file 1. Appendix 1: Look up table for ‘WH’ weekly-
hour
values in Fig. 1.
http://dx.doi.org/10.1186/s40163-015-0040-7
Abbreviations
CD: criminal damage; GIS: geographical information science;
MCC: multi-clas-
sification crime; NTE: night-time economy; VAP: violence
against the person;

WH: week hour.
Competing interests
The author declares no competing interests.
Received: 3 July 2015 Accepted: 30 September 2015
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c.40163_2015_Article_40_25877.pdfCrime and the NTE: multi-
classification crime (MCC) hot spots in time and spaceAbstract
BackgroundMethodsDataData processingThe temporal nature
of offencesIdentifying hot-spotsResultsProfiling the ‘hottest’
hot spotsDiscussion of findingsConclusionsCompeting
interestsReferences
73
The Cell As a City
© Kendall Hunt Publishing Company
3
EssEnTiAls
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74 Unit 1: That’s Life
ChECk in
From reading this chapter, you will be able to:
• Explain how differences caused by inherited
organellescould have societal implications.
• Describe how the characteristics that are valued
change from culture to culture and over time.
• Outline the cell theory, list and describe types
of cells, and explain endosymbiosis.

• List and describe the organellesfound in a
cell, and explain their main functions.
• Explain the processes of diffusion, osmosis,
facilitated diffusion, active transport, and bulk
transport.
The Case of the Meddling houseguest:
A Friendship Divided
Theta and Joules liked their friend Sally, but when they entered
college, they learned
that Sally was different. When they were all young, they played
together on the block,
went to each other’s birthday parties, and had some great
sleepovers. “We had a lot of
fun with Sally in sixth grade . . . I wish she could join our
sorority,” said Theta. Aghast at
the thought, Joules replied, “Don’t even say it – you know what
that would mean for us.
We shouldn’t even admit that we know her.”
“Why can I not hang out with people I like? . . . Am I not
allowed to be Sally’s
friend because of some test?” thought Theta. “There is no law
against me being friends
with Sally!” exclaimed Theta, after a long pause. Joules
dismissed Theta smugly, “You
know you can’t do it. It will never happen.” They were
expecting Sally to come into the
dorm any minute. Sally was expecting to hang out with them as
usual. But on this day,
their friendship had to end. On this day, Joules and Theta were
going to pledge their new
sorority . . . and Sally did not have the mark.
It was an advanced society, in 2113 with all of the comforts –
space travel beyond

the solar system, teleporting, and no more diseases that the
ancients had; instead there
were life spans approaching two centuries for the marked
people. Humans had it better
than ever, and teens had the world in their hands. Everyone with
parents that had any
sense had a mark on their children to denote their superior
genetic lineage. People in
the line of descent from genetically modified mitochondria had
an “M” on the inside
of their ears. Their life expectancy was much higher and their
health much better than
those without the mark. Finding out about one’s mitochondrial
DNA was easy, with tests
dating back over 100 years to trace the origin of one’s genes.
Mitochondria are organelles that make energy for a cell; they
are inherited from
mother to children because they have their own genetic material
and divide on their
own. Mitochondria are, in fact, separate structures existing
within our cells. They were
absorbed some 2.5 billion years ago, with their own set of DNA,
making them houseg-
uests in our bodies.
The genes in the mitochondria stay intact from generation to
generation. “This is
why the mark was so important – the health benefits,” thought
Theta. Mitochondrial
DNA with modified genes of a particular line of mitochondria
made people much health-
ier, free of many diseases in the society of this story.
Mitochondria are the meddling
houseguests in the title because defects in them cause a range of
diseases. For example,

Mitochondria
Is the organelle that
makes energy for a
cell.
Organelle (subcel-
lular structure)
Structures that
function within cells in
a discrete manner
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Chapter 3: The Cell As a City 75
mitochondrial defects in the 21st century were responsible for
many ailments, ranging
from heart disease and diabetes to chronic sweating, optic nerve
disorders, and epilepsy.
Joules told Theta, “People without the mark are jealous of us
because they die earlier
and have a worse life with more diseases. You know Sally
would never understand us.
Sally’s genes are still from the 21st century.” But something
still bothered Theta: She
liked Sally. Sally came into the dorm and Joules explained that
they were leaving for the
sorority. Sally knew what that meant and said good-bye. Theta
looked deeply at Sally,
realizing that their past was gone and that they would not see
each other again as friends.
Sally and Theta both had a single tear in their eyes and they
knew they were part of each
other’s youth . . . and that meant something.
But Theta looked back one last time and said thoughtfully to
herself, “She’s not one
of us.”
Culture, Biology, and social stratification
Culture plays an important role in defining what is desirable
and valued in society. Often
decisions on what it means to be “better” are based on cell
biology. Our genetic material

makes each of us unique and guides the workings of our cells.
We all have the same set
of cell structures or organelles, but, as in our story, genetic
variations give each per-
son unique characteristics. While the opening story is science
fiction, its possibilities
are real. Gene technology is improving human health and has
the potential to “design”
human genes and organelles, possibly leading to social issues
like those described in the
conflict faced by Sally, Joules, and Theta.
Biological differences may lead to social changes based on what
a society values
at any one time. For example, research shows that certain
biological features are used
to decide social value of people: symmetry of one’s face, body
fat distribution in both
genders, and musculature in males; smooth skin, good teeth, and
a uniform gait. These
are all biologically determined, based on how our cell structures
work together. Much
as mitochondrial inheritance, described in the story dictates
health and organismal func-
tioning, all cell structures give living systems their
characteristics.
Historically, all cultures have used biology to classify people.
Humans are suscepti-
ble to group messages, such as the one that influenced Theta’s
and Joules’ final decision
to abandon their friendship with Sally. The average American is
exposed to about 3,000
marketing messages per day. This sets up a value system that
requires us to reflect on
how biology and society can affect our thinking.

ChECk Up sECTion
The exclusion of people in our futuristic science
fiction storyreflects a theme in human
society and
history. As a result of cell differences
between Theta and Sally, their friendship ended
– each possessed
a different type of mitochondrion.
Choose a particular situation in which a social
stratification (layering) system is set up in a
society,
in which one group thinks it is better
than another. You may choose a present system
or one of the
past. Is the stratification system reasonable? Is
the system based on cell biology? What
are the system’s
benefits? What are its drawbacks?
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BOdy Art And Skin BiOlOgy in SOciety
Body alterations in the quest for physical
beauty are as old as history. Egyptians
used cosmetics in their First Dynasty (3100–2907 BC).
Hairstyles, corsets, body-
weight goals, and body piercing and tattooing trends
have changed through human
history. Scars have been viewed as masculine and a
mark of courage, and tattoos
were drawn and carved in ancient European,
Egyptian, and Japanese worlds.
Body art was popular in modern western society
among the upper classes in
the early19th century. It lost favor due to stories
of disease spreading because
of unsanitary tattoo practices. Only the lower
classes adopted body art to show
group affiliation. Tattooing has recently gained
popularity; but body art has been
used as a symbol of self-expression and as a

social-stratification mechanism in
many cultures: Indian tattoos mark caste;
Polynesians used marks for showing mar-
ital status; the Nazis marked groups from their
elite SS to concentration camp pris-
oners; and U.S. gangs use it to showgroup
membership. Tattooing has been firmly
established in societies and continues to growin
popularityin the United States.
The canvas for tattoos is skin, which is part of
the integumentary system
and has a variety of functions in humans (Figure
3.1). It
• maintains temperature;
• stores blood and fat; and
• provides a protective layer.
We will discuss this important system in a later
chapter.
In this chapter, we will look at the structure and function of the
eukaryotic cell. We
will see that, while there are marked differences between plant
and animal cells, the
basic processes carried out at the cellular level are remarkably
the same, as are those of
simple, unicellular organisms. We will compare the organelles
(structures) of the cell
to functions of a city to emphasize that all parts are needed.
Each organelle has its own
duties, and the parts work together to make an efficient
machine. We begin by looking at
the development of the microscope, without which our
understanding of cells and how
they function would be incomplete.

Figure 3.1 Tattoos and body art. Dyes penetrate into the
skin cells of a tattoo.
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Exploring the Cell
The Microscope
The human body is composed of over 10 trillion cells, and there
are over 200 different
types of cells in a typical animal body, with an amazing variety
in sizes (see Figure 3.2).
Despite the variety in size, all of these cells and the structures
within them are too small
Figure 3.2 Biological size and cell diversity. When
comparing the relatives’ sizes of

cells, we use multiples of 10 to showdifferences.
The largest human cell, the female
egg, is 100 µm, while the smallest bacterial cell is
1000 times smaller at 100 nm. Most
cells are able to be seen with the light microscope.
The smallest object a human eye can
see is about 1 mm, the size of a human
egg cell (or a grainof sand). From
Introductory
Plant Science, by Cynthia McKenney et al.
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Atoms
Small
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Proteins
Viruses
Nucleus
Mitochondria
Lipids
Ribosomes
Smallest bacteria
Most
bacteria
Most plant and
animal cells
Fish egg

Human height
1 nm
10 nm
100 nm
1 �m
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to be visible to our naked eyes and can only be identified by
using microscopes to
magnify them.

There are several types of microscopes; perhaps the one with
which you are already
familiar is the compound light microscope. The compound light
microscope uses two
lenses: an ocular and an objective lens. Each of these is a
convex lens, meaning that its
center is thicker than its ends. Convex lenses bring light to a
central, converging point to
magnify the specimen. A microscope’s parts are seen in Figure
3.3.
The purpose of a microscope is to magnify subcellular parts.
What is magnifica-
tion? Magnification is the amount by which an image size is
larger than the object’s size.
If a hair cell’s image is 10 times bigger than its original object,
the magnification is 10
times. If it is 100 times bigger, then the magnification is 100
times. The microscope uses
two lenses to magnify the specimen: an ocular (eyepiece), which
generally magnifies
between 10 and 20 times, and a series of objective lenses (each
with higher magnifica-
tions). The total magnification of a specimen is equal to the
ocular (in this example let’s
use10 times) times the magnification of one of the objective
lenses.
Most animal cells are only 10–30 µm in width. It would take
over 20 cells to span the
width of a single millimeter. Recall that a millimeter is only as
wide as the wire used to
make a paper clip. See Table 3.1 for measurements used for
looking at living structures.
How were cells and their smaller components discovered using

the microscope?
Anton van Leeuwenhoek and Marcello Malpighi built
microscopes in the late 1600s. At
this time, those instruments were very rudimentary. They
consisted of a lens or a com-
bination of lenses to magnify smaller objects, including cells.
Both scientists used their
instruments to observe blood, plants, single-celled animals, and
even sperm. Van Leeu-
wenhoek’s microscope is shown in Figure 3.4. At about the
same time that van Leeuwen-
hoek and Malpighi were making their observations, Robert
Hooke (1635–1703) coined
the term cell, as he peered through a primitive microscope of
his own construction.
When he viewed tissues of a cork plant, Hooke saw what
seemed to be small cavities
separated by walls, similar to rooms or “cells” in a monastery
(see Figure 3.4). These
cells are defined as functioning units separated from the
nonliving world.
Although it has progressed in design, materials, and technology,
the compound light
microscope is based on the same principle as in the 17th
century: light bends as it passes
through the specimen to create a magnified image. Some amount
of light always bends
compound light
microscope
Microscope that uses
two sets of lenses
(an ocular and an
objective lens).

Magnification
Is the amount by
which an image size is
larger than the object’s
size.
Figure 3.3 Compound light microscope – its parts
and internal lens system.
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when hitting the edges of the lens, causing scattering in a
random way. The random
scattering of light, called diffraction is bad for getting a clear
focus on the image. Dif-

fraction also limits the resolution of the image. Resolution is
defined as the ability to see
two close objects as separate. (Think about looking at two lines
on a chalkboard that is
very far away; chances are they blur together and look like one
messy line.) In fact, the
human eye has a resolving power of about 100 µm or 1/10th of a
millimeter for close-up
images. In other words, two lines on a paper closer than 1/10th
of a millimeter apart look
blurry to us. The light microscope is limited in the same way by
diffraction because the
diffracted rays create blurry images.
diffraction
The random scattering
of light.
resolution
Is the ability to see
two closeobjects as
separate.
Figure 3.4 Hooke’s microscope from the 1600s
and van Leeuwenhoek with his
microscope. These simple microscopes led to
the first descriptions of cells. Van
Leeuwenhoek’smicroscope consisted of a small
sphere of glassin a holder.
1 centimeter (cm) =
1/100 meter or 0.4 inch
3 cm

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1 mm
Fr
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1 meter = 102 cm = 103 mm = 106 µm =109 nm
Unaided human eye

1 millimeter (mm) =
1/1,000 meter
1 micrometer (µm) =
1/1,000,000 meter
1 nanometer (nm) =
1/1,000,000 meter
100 µm
Hu
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Light microscopes
Electron microscopes
10–100
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table 3.1 Measurements Used for Microscopy. The units of
measurement used in the study of molecules
and cells correspond with methods by which we
are able to detect their presences.
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Higher magnification under the microscope leads to greater
diffraction. This is the
reason a compound light microscope can magnify only up to
1000–1500 times (under
oil immersion), after which there is too much diffraction for a
clear image to be formed.
To overcome the effect of diffraction and achieve clarity at
higher magnifications, oil is
placed on the slide. However, even with oil immersion, only the
large nucleus of a cell
can be seen; other organelles appear as small dots or not at all.
So how did the more complex world of even smaller structures
within cells get dis-
covered? The 1930s saw the development of the electron
microscope that allowed for
magnifications of over 200,000 times greater than that of the

human eye. There are two
types of electron microscopes: transmission electron microscope
(TEM) and scanning
electron microscope (SEM). Transmission electron microscopy
allows a resolving power
of roughly 0.5 nm (see Table 3.1) that visualizes structures as
small as five times the
diameter of a hydrogen atom. Electron microscopes use
electrons instead of light, which
limits diffraction and increases resolution. Magnets instead of
lenses focus electrons to
create the image. The electrons pass through very thin slices of
the specimen and form
an image.
A SEM looks at the surfaces of objects in detail, while a TEM
magnifies structures
within a cell. The SEM has a resolving power slightly less than
the TEM, at 10 nm. (A
depiction of an electron microscope is shown in Figure 3.5.)
Electron microscopy has
led to many scientific developments, uncovering subcellular
structures to help us under-
stand cell biology. Seeing a mitochondrion enables us to better
understand diseases and
perhaps, if our opening story becomes reality, improve societal
health through its use.
Cell Theory
Fairly recent advances in microscopy have allowed scientists to
learn about the structure
and function of even the tiniest components of cells, but the cell
theory, which states key
ideas about cells, developed a long time ago. We have seen that
scientists began study-
ing cells in the early 1700s. About a century later, in 1838, a

German botanist named
Matthias Schleiden (1804–1881) concluded that all plants he
observed were composed
of cells. In the next year, Theodor Schwann (1810–1882)
extended Schleiden’s ideas,
transmission elec-
tron microscope
(teM)
A type of electron
microscope that
magnifies structures
within a cell.
Scanning electron
microscope (SeM)
An electron
microscope that
looks at the surfaces
of objects in detail
by focusing a beam
of electrons on the
surface of the object.
Figure 3.5 a. A researchersits at a modern
electron microscope. b. Apple tree pollen
grains on cells, an
electron micrograph.
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observing that all animals are also made of cells. But how did
these cells come to survive
generation after generation? The celebrated pathologist Rudolf
Virchow (1821–1902)
concluded in 1858 that all cells come from preexisting cells (He
wrote this in Latin:
“Cellula e cellula”). These scientists contributed, together, to
the postulates of the cell
theory. The cell theory is a unifying theory in biology that
places the cell as the center of
life and unifies the many branches of biology under its
umbrella. The cell theory states
that:
1) All living organisms are composed of cells.
2) The chemical reactions that occur within cells are separate
from their
environment.
3) All cells arise from other cells.
4) Cells contain within them hereditary information that is
passed down from par-
ent cell to offspring cell.
The cell theory showed not only that cells are the basic unit of
life, but that there is
continuity from generation to generation. Genetic material is
inherited in what we refer
today as the cell.
Types of Cells
Microscopes allowed researchers to examine differences
between organisms that had

previously been impossible to determine. A current
classification of organisms defines
five kingdoms, with organisms in those kingdoms having
similar types of cells (There
is some debate arguing inclusion of Archaea bacteria as a
separate kingdom, and a six-
system classification scheme is thus also accepted). Cells of
organisms in the five king-
doms each have many internal differences, as summarized in
Table 3.2. Images of some
organisms of each kingdom are given in Figure 3.17 as
examples.
Prokaryotes (bacteria) are composed of cells containing no
membrane-bound
nucleus and no compartments or membranous organelles. They
are much smaller than
eukaryotes, by almost 10 times. Prokaryotic genetic material is
“naked,” without the
protection of a membrane and nucleus. They are composed of
very few cell parts: a
membrane, cytoplasm, and only protein-producing units called
ribosomes. Even without
most structures found in other organisms, prokaryotes contain
genetic material to repro-
duce and direct the functions of the chemical reactions
occurring within its cytoplasm.
group domain cell type cell number cell Wall component energy
Acquisition
Bacteria Bacteria Prokaryotic Unicellular Peptidoglycan Mostly
heterotrophic,
some are autotrophic
Protists Eukarya Eukaryotic Mostly unicellular,

some are simple
multicellular
Cellulose, silica; some have
no cell wall
Autotrophic,
heterotrophic
Plants Eukarya Eukaryotic Multicellular Cellulose Autotrophic
Animals Eukarya Eukaryotic Multicellular No cell wall
Heterotrophic
Fungi Eukarya Eukaryotic Mostly multicellular Chitin
Heterotrophic
From Introductory Plant Science by Cynthia McKenney et al.
Copyright © 2014 by Kendall Hunt Publishing Company.
Reprinted by
permission.
table 3.2 Differences in Cell Structure within the
Five Kingdoms: Plants, Animals and Prokaryotes.
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Prokaryotes have a simple set-up, but all of the needed
equipment to carry out life func-
tions. Bacteria have a rapid rate of cell division and a faster
metabolism than eukaryotes.
Most organisms on Earth, in terms of sheer number, are
prokaryotes.
• As indicated in Chapter 1, prokaryotes include organisms in
the Bacteria and
Archae domains. These organisms will be discussed further in
Chapter 8.
All other organisms (plants, animals, fungi, and protists) are
eukaryotes. Cells of
eukaryotes are complex, containing a membrane-bound nucleus
that houses genetic
material. Eukaryotic cells comprise compartments that form a
variety of smaller internal

structures, or organelles. Eukaryotic cells are the focus of this
chapter, which will give
an overview of the primary organelles and their functions
(Figure 3.6).
Eukaryotes may be examined by dividing into its four groups:
plants, animals, fungi,
and protists. Plants contain cells that are surrounded by a cell
wall, a rigid structure giv-
ing its organisms support. Plant cells contain chloroplasts,
which enable plants to carry
out photosynthesis, using energy from sunlight to make food.
• Plant cell walls contain cellulose, which gives structure to
plants as discussed
in Chapter 2. The process of photosynthesis, producing food for
plants, will be
further discussed in Chapter 4.
Plants also have large vacuoles or storage compartments to hold
water and minerals for a
plant’s functions. While both plants and animals have a cell
membrane, animal cells are
Photosynthesis
The process by which
green plants use
sunlight to synthesize
nutrients from water
and carbon dioxide.
Figure 3.6 a. Differences between prokaryotes and
eukaryotes. Prokaryotes have a
generally simple structure (see top cell in figure
above), while eukaryotes (the lower

cell in figure above) have multiple organellesand
membranes forming complex com-
partmentalization. From Biological Perspectives, 3rd ed
by BSCS. b. Differences between
plants and animals. Plantand animal cells perform
different functions, and their subcel-
lular structures are also different. Plantcells have chloroplasts
to produce sugar and a
cell wall to give added strength. The animal cell
shown has no cell wall or chloroplasts
but possesses centrioles. From Biological
Perspectives, 3rd ed by BSCS.
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less rigid, surrounded only by a cell membrane and lacking a
cell wall for support. Both
plants and animals contain membrane-bound organelles, but
animals also contain a set

of small structures called centrioles, which serve in cell
division. Animal cells are also
quite complex, as we will see. While lacking certain organelles,
such as cell walls and
chloroplasts, they have flexible strategies to perform many
functions.
Fungi have cell walls but no chloroplasts. They are not able to
make their own food
and, instead live off of dead and decomposing matter as well as
other living organisms,
centriole
Minute cylindrical
organellesfound in
animal cells, which
serve in cell division
(not given in bold in
text).
(b) C
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to obtain energy. Mushrooms and yeasts are familiar types of
fungi, which will be dis-
cussed in Chapter 7.
Some species of protists are a bit animal-like in that they are
able to move; other
species are a bit plant-like in that they have chloroplasts.
Protists such as Amoeba in
Figure 3.7 have varied environments. Amoeba live in freshwater
and, in a rare infectious
disease, grow and destroy human brain cells. We will discuss
protists in more detail in
a later chapter.

Figure 3.7 Cells of the five kingdoms.While the
cells of organisms in all of the kingdoms
perform similar
life functions, their individual structures enable differing
functions unique to each kingdom. From
Biological
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The Role of inheritance
The stratification system depicted in our opening story is based

on the inheritance of
cellular components. We know that organelles are structures
that carry out functions
within a cell. In fact, organelles work in concert with one
another, coming together to
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form a complex, dynamic cell. Mitochondria, so important in
the society in our story, are

the powerhouses of the cell, providing energy for a cell’s
functions.
All organelles are built and controlled by inherited genetic
material. Thus, the way
a cell works is based upon its genetics. But some organelles are
inherited separately
from the others. There are three ways to inherit organelles,
including mitochondria: 1)
maternal inheritance, in which organelles are inherited from
mothers; 2) paternal inher-
itance, in which organelles are inherited from fathers; and 3) bi-
parental inheritance,
in which organelles are inherited from both mothers and fathers.
The inheritance type
varies among species and for different organelles. For example,
chloroplasts are pater-
nally inherited in the giant redwood Sequoia but maternally
inherited in the sunflower
Helianthus. Most animal species have maternal transmission of
mitochondria, as seen in
the story, because female eggs hold most of the mitochondria in
their large cytoplasmic
cells. Sperm contributes very little cytoplasm or organelles in
human species, although
there are exceptions among other organisms; for example, green
algae Chlamydomonas
has paternal transmission of mitochondria.
Endosymbiosis
Eukaryotes appeared in Earth’s history about 1.5 billion years
after prokaryotes. In their
2.0 billion years on Earth, eukaryotes have evolved into living
systems that range from
butterflies to beavers, crocodiles to humans. As you progress
through this text and the

course, you will learn about how this amazing diversity
evolved.
Eukaryotes have two types of organelles – those that evolved as
membranes and those
from other, simpler organisms as precursors, called
endosymbionts. Endosymbionts
endosymbionts
Any organism living in
the body or cells of
another organism.
the Oxygen revOlutiOn
Haveyou ever tried to imagine the Earth in its
earlystages? After millions of
years during which the Earth was a mass of
molten gases, those gases began
to cool into layers that became landmasses, while
water vapor formed seas.
There were as yet no animals and only a few
prokaryotic forms living in the
waters. One form of bacteria, known as
cyanobacteria, is believed to have
been among the first organisms to photosynthesize
(convert light energy into
chemical energy to drivecellular activities). Since
a by-product of many forms
of photosynthesis is oxygen, over several million
years, more and more oxygen
was released into the atmosphere – the oxygen
revolution.
The oxygen revolutionoccurred, according to geologists,

about 2.5 billion
years ago. It led to an availability of oxygen
that could be used by cells that
evolved to use it. The advantage of using oxygen
to yieldlarger amounts of
energy led to an increase in the number of
aerobic cells. Aerobic cells, or those
cells that use oxygen as the fuel for obtaining energy,
contain mitochondria to
provide largeamounts of energy production. Over
the course of many millions
of years, single-celled eukaryotes, with more
complex cellular processes than
prokaryotes, evolved, then multicellular eukaryotes,
and eventually organisms
became larger and more complex. It is important to
realize that oxygen is a
key player in the development of organisms sinceit
can be used to produce
fast, plentiful energy.
Oxygen revolution
The biologically
induced appearance
of dioxygen in Earth’s
atmosphere 2.5
billions of years ago.
Aerobic
Occurring in the
presence of oxygen or
require oxygen to live.
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have their own genetic material and are semi-independent
within the cells of eukary-
otes. Endosymbionts include two types of organelles –
mitochondria and chloroplasts.
Mitochondria are the energy producers of animal cells, and
chloroplasts are solar power
transformers of plant cells. Mitochondria divide independently
and have an internal
environment that is different from that in the rest of the cell.

Evidence tells us that endosymbionts were once independent
prokaryotes that were
somehow incorporated into eukaryotic cells. Lynn Margulis, a
well-known evolution-
ary biologist, formulated the endosymbiotic theory, which states
that some organelles
in eukaryotes were descendants of ancient bacteria that were
absorbed by larger cells.
(Symbiosis refers to a mutually beneficial relationship of
organisms living together;
endo means within.) The larger cell gave ancient bacteria
absorbed by larger cells a
“home.” The bacteria received protection and in return gave
their unique set of chemical
reactions to the larger host cell.
Analysis of endosymbionts have particular uses in today’s
society: Crime scene
investigations can test for mitochondrial DNA; ancestry can be
traced using mitochon-
drial DNA; and research on diseases inherited due to faulty
mitochondrial genetic mate-
rial may yield medical treatments. In our story, Joules and
Theta had a different form of
mitochondria than Sally. Joules, Theta, and Sally all inherited
their mother’s mitochon-
dria, but Sally’s inheritance included a predisposition for a
number of diseases.
We have said that mitochondria can be thought of as the power
plants of the cell. We
say this because mitochondria carry out the series of energy-
producing reactions that
convert food energy into ATP (defined in Chapter 2; the form of
energy used to drive

cell functions). This set of reactions is called cell respiration.
Chloroplasts may have originated from cyanobacteria, which
were able to transform
light energy into usable sugar for energy in the process called
photosynthesis – the
making of food (glucose) from sunlight, carbon dioxide, and
water. Thus, precursor
mitochondria provided instant ATP energy for animal host cells,
and ancient chloro-
plasts made stored glucose available for longer-term use in
plants. Modern chloroplasts
use sunlight to rearrange carbon to form food in the form of
sugars, for cell usage. They
are the solar power plants of cells because they trap sunlight
and generate ATP energy.
• These energy-obtaining processes, cell respiration, and
photosynthesis will be
discussed in greater detail in Chapter 4.
Evidence for mitochondria and chloroplasts as endosymbionts is
considerable:
1) Mitochondria and chloroplasts are similar in size and shape
to bacteria, roughly
7 µm in length.
2) Both contain their own genetic materials and divide in the
same way as
prokaryotes, through binary fission (splitting in half).
3) Both contain the same type of 70S ribosomes (described
below; small organ-
elles that make protein) as bacteria, whereas eukaryotes contain
80S ribosomes.

4) Mitochondrial and chloroplast DNA are more related to
bacterial than to eukary-
otic DNA. See the proposed process on endosymbiosis in Figure
3.8.
We will now look at each of the structures of the cell, using the
analogy of a city,
with the organelles being structures critical to the smooth
functioning of the “city.”
Organelles work in concert with each other to carry out life
functions. Chloroplasts
and mitochondria carry out key energy-producing and releasing
functions to drive cell
activities. However, there is an intricacy to cell biology akin to
the workings of a large
and dynamic city.
endosymbiotic
theory
The theory that states
that someorganelles
in eukaryotes were
descendants of ancient
bacteria that were
absorbed by larger
cells.
cell respiration
A series of energy-
producing reactions
that convert food
energy into ATP.

chloroplast
A part of plantthat
contains chlorophyll
and conducts
photosynthesis.
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Figure 3.8 This model shows how mitochondria and

chloroplasts wound up in
eukaryotic cells. Evidence for endosymbiosis.
Chloroplasts and mitochondria are simi-
lar in size and shape to bacteria. The ribosomes in
bacteria and mitochondria and chlo-
roplasts are “70S.” Mostbacteria have a size of
roughly 7–10 µm and 70S ribosomes,
both characteristics are similar to mitochondria and
chloroplasts.
©
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Proteobacterium
engulfed by a
heterotrophic
eukaryote
Loss of bacterial cell wall
and transfer of some genes
to the nucleus-endosymbiont
becomes mitochondrium
Loss of bacterial cell wall and
transfer of some genes to the
nucleus-endosymbiont
becomes chloroplasts
Heterotrophic
eukaryotes: animals,
fungi and some protists
Autotrophic eukaryotes:
plants and algae

Cyanobacterium
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Cell Architecture: The Cell As a City
The organelles of a cell work independently but in concert,
with its components inter-
acting actively, having many thousands of chemical reactions
occurring at any one time.

In an analogous way, the components of a city work
independently but cooperatively to
ensure its smooth functioning. The structures in the cell are
shown in the cartoon image
depicting the cell as a city in Figure 3.9.
A cell membrane or plasma membrane surrounds each cell,
which is a wrapping that
allows some materials across it. While all cells contain a plasma
membrane, some cells
have structures surrounding the membrane for protection and
support. For example,
plant cells have cell walls surrounding their membranes, and
fungi have chitin barriers,
which are protective polysaccharides.
Plasma membranes are important to living systems because they
control the mate-
rials entering and leaving them. Plasma membranes are
selectively permeable. Selective
permeability means that the membrane allows some materials to
pass through cells but
not others. Within the cell is the cytoplasm, a semisolid liquid
that holds organelles
suspended within it. Cytoplasm is roughly 60–80% water by
volume, and chemical reac-
tions occur within its medium. The other 20–40% of cytoplasm
is composed of pro-
teins and dissolved ions. Cytoplasm is all of the cell material
found between the plasma
Plasma (cell)
membrane
A biological membrane
that separates the

cell’s interior from the
outside environment.
Selectively
permeable
A condition in which
the membraneallows
somematerials to pass
through cells but not
others.
cytoplasm
A semisolid liquid
that holds organelles
suspendedwithin it.
Figure 3.9 A cell is like a city. Its parts work
together to perform a cell’s function.
A
d
a
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f
ro
m
A

n
a
to
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y
I a
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P
h
ys
io
lo
g
y
Le
c
tu
re
M
a
n
u
a

l b
y
Jo
h
n
E
ric
ks
o
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a
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d
C
.
M
ic
h
a
e
l F
re
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c

t
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A. Nuclear envelope City Hall
Nuclear pores
Nucleoplasm
Outer and inner
membranes
F. Plasma membrane
the Border Patrol
B. Endoplasmic reticulum The Subway
C. Golgi body
Processing Plant
D. Centrioles MoversE. Mitochondrion Power Plant
Proteins
Phospholipid
bilayer

Channel (allows ions
and water to move in
and out of cell)
Inner and outer
membranes
Cristae
Microtubules
Maturing
face Secretory
vesicles
Forming
face
Rough E.R.
Smooth E.R.
Fixed
ribosomes
Cisternae
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membrane and the nucleus. A nucleus, or control organelle,
serves as the city hall of the
cell, usually centered within cytoplasm. It contains the genetic
material that produces
and controls the cell’s parts.
A cell’s architecture is very complex, with continual activity
within many com-
partments of each cell. Compartmentalization of a cell is
accomplished by a series of
membranes throughout its cytoplasm. These membranes allow
for a surface to per-
form chemical reactions such as those carried out by enzymes.
The layers of mem-
branes also separate different chambers of the cell so that
conditions may be different
within each chamber. An acidic pH in one compartment, for
example, may be suitable

for cell functioning in one section, while a basic pH might be
needed in another section.
Throughout the cell, ropes (such as collagen and elastin) and
membranes maintain a
cell’s organization. Figure 3.9 gives you an idea of the complex
architecture of the cell.
plasma Membrane: The
“Flexible” Border patrol
A border surrounds all cells: in eukaryotic animal cells, the
border is the plasma or cell
membrane. This dynamic covering is very complex, with parts
that continually move to
allow certain materials into the cell and keep other substances
out. The plasma mem-
brane is composed of a phospholipid bilayer in and around
which are membrane pro-
teins (see Figure 3.10).
• Recall from Chapter 2 that phospholipids are molecules with a
phosphate
(hydrophilic) head and two tails made of carbon and hydrogen
atoms.
The phospholipids are arranged as a bilayer, with the heads
facing to the outside and
tails to the interior of the cell. The membrane proteins are part
of the plasma membrane
border, moving in between cholesterol molecules and
phospholipids. Although it may
seem that they are arranged randomly, in fact, they form a
pattern. The membrane is
often referred to as a fluid mosaic (see Figure 3.11) because it
is made of different pieces
that form a pattern and seem to float and move in the watery
environment. Note the

phospholipid bilayer in Figure 3.11.
There are two types of membrane proteins suspended within the
phospholipid
bilayer: integral proteins, which span the entire lipid bilayer;
and peripheral proteins,
which station either inside or outside of the membrane. Integral
proteins are also known
as transmembrane proteins. These membrane proteins serve to
anchor cells to each
nucleus
The central and the
most important part
of a cell and contains
the genetic material.
compartmental-
ization
The formation of
cellular compartments.
Fluid mosaic model
A model that
describes the
structure of cell
membranes.
integral protein
(transmembrane or
carrier protein)
A type of membrane

protein permanently
embedded within the
biological membrane
(not given in bold in
text).
Peripheral protein
Is a protein that
adheres only
temporarily to the
biological membrane
with which it is
associated.
Figure 3.10 Every living cell is surrounded by a
cell membrane. From Biological
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other, move materials, much like a ferry, across the membrane,
receive chemicals such
as hormones, and transport ions through pores within them.
These proteins orient them-
selves according to the bonds they make with surrounding
chemicals.
Integral proteins in the fluid mosaic model serve four functions:
1) As receptors, allowing chemicals to bind to cell surfaces.
Insulin, a hormone-
regulating blood sugar, binds to cells to increase the absorption
of sugar from
blood into cells. Insulin’s special shape matches to the shape on
integral pro-
teins. The docking of the two elicits chemical reactions within
the cell to main-
tain sugar balance;
2) As recognition proteins, giving the immune system a code
that informs it that
a cell is its own and not a foreign substance. Cells with the
wrong recognition
proteins are rejected, as occurs in cases of organ transplants
whose codes do not
match up with those of the recipient;

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