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Climate Change and Levels of Violence in Socially Disadvantaged Neighborhood Groups

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Climate Change and Levels of Violence in Socially Disadvantaged Neighborhood Groups

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The current study examines the link between climate change and neighborhood levels of violence using 20 years of monthly climatic and crime data from St. Louis, MO, USA. St. Louis census tracts are aggregated in neighborhood groups of similar levels of social disadvantage, after which each group is subjected to time series analysis. Findings suggest that neighborhoods with higher levels of social disadvantage are very likely to experience higher levels of violence as a result of anomalously warm temperatures. The 20 % of most disadvantaged neighborhoods in St. Louis, MO, USA are predicted to experience over half of the climate change-related increase in cases of violence. These results provide further evidence that the health impacts of climate change are proportionally higher among populations that are already at high risk and underscore the need to comprehensively address climate change.
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Journal of Urban Health
Bulletin of the New York Academy of
Medicine
ISSN 1099-3460
J Urban Health
DOI 10.1007/s11524-013-9791-1
Climate Change and Levels of Violence
in Socially Disadvantaged Neighborhood
Groups
Dennis Mares
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Journal of Urban Health: Bulletin of the New York Academy of Medicine
doi:10.1007/s11524-013-9791-1
* 2013 The New York Academy of Medicine
Climate Change and Levels of Violence in Socially
Disadvantaged Neighborhood Groups
Dennis Mares
ABSTRACT The current study examines the link between climate change and
neighborhood levels of violence using 20 years of monthly climatic and crime data
from St. Louis, MO, USA. St. Louis census tracts are aggregated in neighborhood
groups of similar levels of social disadvantage, after which each group is subjected to
time series analysis. Findings suggest that neighborhoods with higher levels of social
disadvantage are very likely to experience higher levels of violence as a result of
anomalously warm temperatures. The 20 % of most disadvantaged neighborhoods in
St. Louis, MO, USA are predicted to experience over half of the climate change-related
increase in cases of violence. These results provide further evidence that the health
impacts of climate change are proportionally higher among populations that are already
at high risk and underscore the need to comprehensively address climate change.
KEYWORDS Climate change, Violence, Neighborhood dimensions, Social disorganization,
Routine activities
INTRODUCTION
Violent crime is a serious health issue that unevenly affects the American population
and exacts a large impact on the qualit y of life and health of residents as well as
imposes a large nancial burden on health care providers.
13
Neig hborhood
conditions are cited in the criminological literature as one of the more consistent
and pervasive factors in predicting high levels of violence.
47
The National Institutes of Health
8
recently called attention to the relevance of
climate change and described a large number of possible negative health effects of
climate change. The climate change literature suggests that economically disadvan-
taged populations may experience a larger health impact,
914
but no study to date
has examined if climate change also inuences neighborhood levels of violence.
Several recent epidemiological studies have touched on the relationship in an
indirect fashion. For ex ample, one study suggests that health impacts of climate
change may be heightened in high-crime neighborhoods because residents may keep
doors and windows shut during extremely hot days, but this study does not address
interpersonal violence.
10
Another epidemiological study found a relationship
between climatic conditions and sexual assaults, but did not examine if neighbor-
hood conditions inuenced this relationship.
14
The following study uses 20 years of monthly data from St. Louis, MO, USA to
examine the likely relation between climate change and violent crime in a range of
Mares is with the Southern Illinois UniversityEdwardsville, Edwardsville, IL, USA.
Correspondence: Dennis Mares, Southern Illinois UniversityEdwardsville, Edwardsville, IL, USA.
(E-mail: dmares@siue.edu)
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neighborhoods. Previous research on the link between climatic conditions and
violence has predominantly focused on the impact of seasonality.
1528
It is well-
documented that violent crimes generally increa se during the warmer months of
the year, but debate continues on the causes. Some studies explain the link
between violence and warmer climatic conditions as a result of a heataggression
link that has been observed in laboratory experiments.
2935
It is hypothesized
that unusual heat levels may trigger irritation and discomfort and thereby
heighten aggression.
Other studies suggest that there is an indirect connection between violence and
warmer conditions. In these studies, levels of violence uctuate throughout the year
as people change their routine activities.
20,25,27,28
Pleasant weather, for instance,
brings victims and offenders in closer proximity, as more people are out and about,
resulting in a higher level of violence, particularly robberies and assaults.
17,18,28
The theoretical foundations of both routine activities theory and heataggression
theories remain largely untested. In order to see if changing routine activities impact
violence levels during warmer weather, one would actually need to estimate how
peoples routine activities change. Particularly at the weekly or monthly level s, these
data are simply not yet available. Researchers, however, argue that the specic
temperatureviolence curve can provide an approximation of the dilemma. Routine
activities theorists point out that, while violence increases during pleasant weather,
people are less likely to commit acts of violence when it is too hot (curvilinear
relationship). Heataggressio n perspectives often argue that th e link between
violence and temperature is simpler: more heat, more irritation, and consequently,
more violence (linear relationship).
Rotton and Cohn
28
and Anderson, Bushman, and Groom
29
are currently the only
empirical studies that have qua ntitatively measured the relation between climate
change and interpersonal violence in the USA. Both studies found a positive
relationship between increasing average temperatures and levels of violence. Rotton
and Cohn
28
, in particular, provided a specic enumeration of climatic inuences on
violence, suggesting a small but signicant positive correlation between higher
average temperatures and higher levels of violent crime. Several other studies have
examined the connection between climate change and interstate and civil conicts,
but the results of these studies are not always supportive of a climate change
violence connection, plus state level violence is quite distinct from interpersonal
violence.
3640
The currently available studies focusing on climate change and interpersonal
violence in the USA
28,29
suffer from two aws. One, they use annual data to
estimate the impact of climate change on levels of violence. This is problematic as
recent climate research shows that the impact of climate change (increasing
temperatures) is not equally distributed throughout the year.
13,41,42
Particularly in
the USA, winter temperatures have recently increased far more so than summer
temperatures.
38,41
Two, several recent studies have found that levels of violence in
disadvantaged neighborhoods may be more affected by seasonal variations in
temperature.
16,43
By extension, some neighborhoods may be at greater risk of
increased violence as a result of climate change, but no study has yet addressed this
issue. Criminologists have, however, produced a large body of work illustrating the
relevance of neighborhood conditions in producing or controlling crime.
46,45,46
Particularly instructive is the research on social disorganization theory, which has
provided ample evidence for the role that economic disadvantage and neighborhood
stability play in controlling levels of violence.
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In short, there is a need to understand how climate change may differently impact
violence in communities with varying levels of socioeconomic resilience. Based on
the common research ndings that crime is higher during pleasant weather and
crime being higher in socially disadvantaged neighborhoods, one would predict that
socially disadvantaged neighborhoods are likely to experience higher increases in
violence as a result of climatic changes.
The primary goal of this study is to estimate the potential effects of climate
change on levels of violence in different types of neighborhoods. While being
inherently descriptive and exploratory, this study cannot co ndently settle the
theoretical debate on the root causes of the heatviolence link. Nonetheless, the
Discussion section will discuss some of the theoretical implications of this study.
METHODS
In order to examine the potential link between climate change and violence, monthly
data are collected from several public sources. Violent crime data are obtained from
the Saint Louis Metropolitan Police Departments Uniform Crime Report database.
All reported homicides, rapes, aggravated assaults, and robberies with complete
location and date information between January 1, 1990 and December 31, 2009 are
used for the current analysis. Temperature data for Lambert St. Louis Airport during
the same period are obtained from NOAAs National Climate Data Center.
47
Control variables are constructed using Census and Bureau of Labor Statistics.
48
Figure 1 shows the average monthly temperature anomal ies (19902009) for both
the USA and St. Louis. Overall monthly temperatures in St. Louis are 1.15 °F above
the long-term means (19702000), but January temperatures have averaged 3.4 °F
above normal. This seasonal trend in climate change in St. Louis closely mirrors the
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4
January
February
March
April
May
June
July
August
September
October
November
December
Anamoly (F)
United States
St. Louis
FIGURE 1. Monthly temperature anomalies St. Louis and USA, 19902009.
CLIMATE CHANGE AND LEVELS OF VIOLENCE IN DISADVANTAGED NEIGHBORHOODS
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trend of climate change in the USA during the same period, albeit at slightly higher
levels.
42
Crime rates in St. Louis are higher than in most of the USA, but trends in this city
mirror those of the USA. For good reasons, St. Louis has been featured in many
prior criminological studie s that sought to generalize ndings.
4953
Richard Rose-
nfeld, a leading expert of trends in violence, even suggests that St. Louis can be used
to estimate national trends in homicides.
51
Figure 2 illustrates this pattern and
shows that St. Louis mirrors national trends quite well. This is quite typical for
larger cities in the USA, as national trends in violence are driven to a large extent by
young urban males who live in inner city communities.
54
It appears paradoxical to expect a positive relat ionship between climate change
and violence when annual rates of violence are decreasing, but temperatures trend
upward. Nonetheless, Figure 3 provides an illustration for this hy pothesis. Just
because annual levels of violence recede does not mean that the decline is balanced
throughout the year.
Warmer than normal summers do not appear to equal higher than normal levels
of violen ce. Violence during the winter months, however, appears to be more
sensitive to temperature shifts. Figure 3 suggests that colder winters see a deeper
decline in violence, whereas warmer winters see a comparative increase in violence.
Given the climatic anomalies displayed in Figure 1, we may thus argue that climate
change likely inuences violence levels by changing the amplitude of crime trends,
particularly during the colder months of the year. In effect, climate change may be
reducing the normal seasonal uctuations in violence, which means that a declining
trend in violence (including summers) is partially offset by warmer winters.
In order to approximate the relationship between climate change and violent
crime in St. Louis neighborhood s more specically, 20 years of monthly reported
violent crime data (approximately 200,000 cases) were geocoded in ArcGis 9.3. This
provides a match rate of nearly 96 % to a specic census tract. It would have been
ideal to examine census tracts individually, but violent crimes do not occur
frequently enough at this level of aggregation and thus pose issues of non-normality.
For instance, some of the least disadvantaged neighborhoods report fewe r than 100
0
100
200
300
400
500
600
700
800
1990 1995 2000 2005 2010
Year
US Rate
0
500
1000
1500
2000
2500
3000
3500
4000
4500
STL Rate
US Rate
STL Rate
FIGURE 2. Rates of violence St. Louis and USA, 19902010. Source for US data: Bureau of Justice
Statistics: http://bjs.ojp.usdoj.gov/ucrdata/; accessed July 20, 2012.
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violent incidents during the 240 monthly observations, creating a problematic low
count distribution that cannot be appro priately modeled in time series analysis
procedures.
55,56
While some researchers argue that the level of aggregation matters,
this is most often a problem in studies that are interested in a highly theoretical
issue.
57
The current study is, however, simply interested in nding out if places with
lower levels of social control are more impacted by climatic changes.
Using groups of census tracts rather than individual census tracts can be also
benecial because it averages out highly localized processes, such as turf wars and
highly active individuals that can severely skew results for individual census tracts.
Groups of census tracts are also a more preferable comparison units than cities in which
the socioeconomic, cultural, and climatic conditions are likely not comparable.
The current study aggregates census tracts into 5 groups (22 tracts per group) by
ordering census tracts on their level of social disadvantage using commonly used
census indicators including poverty level, percent vacant homes, proportion of
young Black males, female-headed households, high school dropouts, unemploy-
ment levels, and proportion of rental units (for a fuller explanation, see Appendix 1).
A similar approach is taken by Kubrin and Weitzer, who divided St. Louis census
tracts into quartiles to examine retaliatory homicides (Table 1).
50
RESULTS
In order to test the hypothesis that climate change is having a larger deleterious
effect on violent crimes in socially disadvantaged neighborhoods, regression models
are constructed for each neighborhood group (group 1 is least disadvantaged, group
5 is most disadvantaged). The dependent variable for the study is the log-
transformed sum of reported aggravated assaults, robberies, rapes, and homicides
occurring per month per neighborhood group (N0 240, 12 months×20 years). By
creating a log-transformed dependent variable, the coefcients for the independent
variables are easily interpretable as they roughly refer to percent change.
0
10
20
30
40
50
60
70
80
90
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
0
50
100
150
200
250
300
Temperature
Violence Group 5
FIGURE 3. Monthly trends of temperature and violence in St. Louis most disadvantaged
neighborhoods.
CLIMATE CHANGE AND LEVELS OF VIOLENCE IN DISADVANTAGED NEIGHBORHOODS
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The main independent variable (Tempanomaly) provides a proxy for climate
change and is measured as the actual monthly temperature anomalies, a commonly
used measure of climate change.
13,38,41
The measure is created by subtracting the
monthly values of the long-term (30 years) mean from the actual monthly
temperatures. While one may object that monthly temperature anomalies do not
capture climate change as a larger process, the point is exactly that recent climate
change has not been equitable month to month. Substantial seasonal variation in
climate change has been obse rved in the USA, with winter months especially seeing
warmer than average temperatures.
13,41,42
Michael Mann recen tly argued that
climate change should be measured on the scale of decades.
43
With 20 years of data
in the current study, one can hardly make the claim one is measuring such a
fundamental process. Nonetheless, temperature anomalies as measured in the
current study can generate an estimate of the current impact of climate change on
violence. Whether specic monthly temperature anomalies are the direct outcome of
climate change or whether they are the outcome of normal weather variability is not
critical. Violent individuals are unlikely to care whether a 60-degree-day in January
is the outcome of climate change or natural variability; they just know it is pleasa nt
out. As long as we can uncover the relationship between above or below normal
temperature and violence, we can estimateif we know the current effect of climate
change on temperaturewhat the probable impact of climate change on levels of
violence is.
Because previous research has indicated strong seasonality effects on violence that
are the outcome of normal seasonal variation in temperatures, the 30-year average
monthly temperature (Seasonality) for St. Louis is incorporated as a control
variable. This variable thus captures typically expected seasonal uctuations in
temperature and violence. Given the importance of economic factors in previous
criminological research, an economic component is integrated in the analysis. The
variable CPI represents the monthly US consumer price index. This measure
captures a degree of economic growth as products tend to get more expensive as
afuence spreads throughout society (other economic variablesincluding unem-
ployment and regional home saleswere examined but failed to yield signicant
results and are thus excluded from the nal model reported here). Monthcontrol
TABLE 1 St. Louis neighborhood groups: social disadvantage indicators and crime
St. Louis
a
Group 1 Group 2 Group 3 Group 4 Group 5
Total population 348,189 79,123 80,731 75,940 58,000 54,273
Percent homes vacant 17.798 5.921 12.353 19.099 22.543 28.116
Percent Black males,
1524 years old
3.990 0.269 2.471 4.420 6.131 6.839
Percent female-headed
households
49.647 22.130 42.909 57.814 62.023 70.128
Percent high school dropout 14.220 10.312 14.480 12.742 12.411 23.093
Percent unemployed 13.039 4.724 7.383 11.957 17.224 25.683
Percent rental units 43.416 29.994 46.337 46.529 45.954 47.655
Percent below the poverty line 26.309 9.191 19.236 26.821 34.020 43.146
Average violent crime
rate per 100,000
2,831 494 1,703 3,175 4,327 5,059
a
Numbers may not add up to 100 % due to rounding and three census tracts with a small population that
were dropped from the ve groups
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captures the variation in the monthly number of days. This is an important variable
as the colder month s in the year are shorter. Finally, the log of the population is used
as a control factor to account for estimated changes in the overall population of St.
Louis.
Data ordered in time units (months in this case) often exhibit temporal
autocorrelation. Serial dependence in the error term violates a key assumption of
ordinary least squares analysis and, therefore, must be addressed.
55,56
In order to
correct for this problem and create a time series that is stationary, the analysis rst
identies underlying trends and includes these trend coefcients as a control in the
model. All models reported (see Table 2) also include the results of the Portmanteau
test of white noise after these trend adjustments are included in the nal models.
Findings for the Monthcontrol variable show the importance of correcting for
the number of days in a month as the variable is highly signicant in each
neighborhood group. Its impact is relatively consistent across neighborhood groups,
suggesting that violence increases around 6 % for each additional day. The
population control measure is also signicant in all the neighborhood groups, but
particularly those with high levels of social disadvantage. This likely suggests that
the population decline of St. Louis in the last two decades took place especially in
more disadvantaged neighborhoods.
The variable CPI exhibits an interesting relationship to levels of violence in
neighborhood groups. Whereas the citywide coefcient is signicant and negative
(0.602), the coefcients grow more negative as the level of disadvantage of a
neighborhood group increases. This suggests, con sistent with prior criminological
research,
4
that macroeconomic growth can have an important relationship to
violence, depending on specic neighborhood contexts.
What is further evident from all models is that a strong seasonality component is
present in all neighborhood groups. The seasonality component of the models does
not allow us to distinguish if higher violence during warmer months is the direct
result of expected seasonal temperature variation or whether this is the result of
exogenous factors connected to seasons (such as school closings). Citywide results
suggest that violence increases 0.638 % on average for each degree Fahrenheit
increase in monthly average temperatures. Considering that average temperatures
uctuate about 50 °F between the coldest (January) and warmest (August) months,
this nding suggests that a typical August should experience almost 32 % more
violence than an average January.
The correlation coefcients of seasonality are a bit more diverse when examining
neighborhood groups, ranging from a low of 0.429 in the more afuent
neighborhoods (group 1) to a high of 0.724 in the most severely disadvantaged
neighborhoods (group 5). This suggests that residents of disadvantaged neighbor-
hoods are likely at higher risk of increased violence during seasonally warmer
months. The most disadva ntaged group of neighborhoods experience approximately
36 % more violent crimes in the warmest month compared to the coldest month,
whereas the least disadvantaged group of neighborhoods typically sees an uptick of
about 21 %. This indicates that socially disadvantaged neighborhoods experience
greater variability in violent victimization during the course of a normal year and is
consistent with prior research.
11,44
When examining temperature anomaliesour proxy measure for climate change
an even more divergent pattern emerges. For instance, when a typical month is a single
degree Fahrenheit warmer than the expected seasonal temperature, violence in St. Louis
rises on average by 0.739 %. This citywide average appears to be mostly generated in
CLIMATE CHANGE AND LEVELS OF VIOLENCE IN DISADVANTAGED NEIGHBORHOODS
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TABLE 2 Time series regression results (standard errors in parentheses)
St. Louis
a
Group 1 Group 2 Group 3 Group 4 Group 5
Model ln(1,0,1)(1,0,0) ln(1,0,1)(0,0,0) ln(1,0,1)(0,0,0) ln(1,0,1)(0,0,0) ln(1,0,1)(1,0,0) ln(1,0,1)(1,0,0)
Q value (lag 40)
Q031.884 Q045.182 Q042.661 Q031.423 Q038.766 Q042.465
Sig00.816 Sig00.264 Sig00.357 Sig00.823 Sig00.526 Sig00.365
Month control 5.879*** 6.026** 5.787*** 5.797*** 5.603*** 6.233***
(0.005) (0.018) (0.010) (0.001) (0.009) (0.008)
Population Control 184.255* 40.528 123.777 162.778* 239.423* 249.221*
(0.825) (0.927) (0.730) (0.756) (0.988) (0.870)
CPI 0.602*** 0.039 0.469** 0.497* 0.640* 0.743***
(0.002) (0.002) (0.002) (0.002) (0.002) (0.002)
Seasonality 0.638*** 0.429*** 0.457*** 0.674*** 0.652*** 0.724***
(0.000) (0.001) (0.001) (0.000) (0.001) (0.000)
Tempanomaly 0.739*** 0.450 0.471 0.659* 0.728** 1.070***
(0.001) (0.005) (0.003) (0.002) (0.002) (0.002)
*PG0.05, **PG0.01, ***PG 0.001
a
Please note that all coefcients in this table are multiplied by 100 to assist interpretation
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the three most socially disadvantaged neighborhood groups (group 30 0.659, group 40
0.728, and group 50 1.070). What is particularly disconcerting is the greater overall
number of violent crimes occurring in these neighborhoods to begin with, magnifying
the impact of this coefcient.
For example, neighborhood group 5 averages 4,576 reported violent incidents
per year. Average monthly temperature s during the rese arch period have
increased by 1.15 °F. If we assume this 1.15 °F difference to be attributable to
climate change, it would mean that, during the research period, a typical year
would experience 1.23 % (1.070×1.15 °F) more violent crimes or about 56
actual violent incidents. In the other four neighborhood groups, the connection
between climate change and violence suggests that violent crimes during an
average year likely increase by 21 (group 4), 18 (group 3), 8 (group 2), and 2
(group 1). If this analysis reects actual changes, the group of most disadvan-
taged neighborhoods in St. Louis (group 5) likely experiences more than half of
the climate change-related increases in violence (56 out of 105), whereas the 2
groups with the lowest levels of disadvantage appear to receive only a small
nonsignicant slice (G10 %).
What is more, given that climate change-induced temperature anomalies
increased particularly during the colder months, the likely impact on violence fo r
disadvantaged neighborhoods should be particularly pronounced during Januaries
where temperature anomalies have averaged 3.4 °F in the last two decades. This
would suggest an average uptick of 3.6 % in violence during a typical January in the
most disadvantaged group of neighborhoods.
The analysis thus reveals that, after controlling for typical factors and expected
seasonality, temperature anomalies rem ain correlated to levels of violence in
disadvantaged neighborhoods. The most disadvantaged neighborhoods in St. Louis
are likely experiencing a double whammy. During typical years, violence is already
proportionally higher during the warmer months (due to seasonality), but climate
anomalies are particularly likely to increase levels of violence during the winter
months.
If this found relationship between climate anomalies and levels of violence in fact
displays a true causal connection, future climate change may (depending on extent
and timing) reduce seasonal uctuations in violence particularly in socially
disadvantaged neighborhoods by lifting up levels of violence during the cooler
seasons and bringing them more in line with the typical higher levels of violence
during warmer months. In other words, the amplitude of the typical seasonal
uctuations in violence may be reduced to where levels of violence are more
constant throughout the year (see Appendix 2 and especially the comparison of St.
Louis to New Orleans).
DISCUSSION
Several limitations of the current study should be pointed out and lead to some
caution in the interpretation of the ndings until further research becomes available.
For one, St. Louis is a relatively poor city with a high crime rate. While temperature
and climate change patterns mirror that of the USA, social and crime indicators do
not alwa ys. Nonetheless, inner city communities are important drivers of overall
violent crime rates, and St. Louis is ree ctive of this group of communities.
50,51,53
Findings for the current study are likely generalizable to some extent to many other
similar cities across the USA (see Appendix 2). Given the extremely high
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concentration of social disadvantage in St. Louis, the strength of the climate change
coefcient (Tempanomaly) is perhaps lower in other cities, but the positive
direction of the climate change coefcient can likely be found in most socially
disadvantaged locations in the USA. Given the disproportionate impact of extremely
disadvantaged neighborhoods on overall levels of violence in the USA, the St. Louis
estimates may approximate the impact that climate change is exerting on US
violence rates.
Another methodological issue is the reliability of the dependent variable as a
measure of actual violence. Police agencies rely on residents to report violent
crimes; hence, the measure in this study is a measure of reported violence, not
necessarily measuring all incidents of violence. A clear caveat for the current
study is the fact that reporting of violence may increase as temperatures are
warmer than normal. Routine activities theory could explain that, during
pleasant weather, people spend more time outside and thus more likely to
witness a violent crime.
Another potential issue in the current study is the exclusion of relevant exogenous
variables. One may argue that more independent variables should have been
included in the current analysis. Unfortunately, relevant monthly data are not as
widely available. Knowing the uctuations in the proportion of young men, for
instance, may likely have yielded additional insights, but this information was not
available at the city or neighborhood level.
A nal issue relates to the underlying causes of the found link between
climate change and crime. The current study cannot denitively establish why
neighborhood differences exist in correlations between our climate change
proxy and levels of violence. Nonetheless, some of the reported ndings are
theoretically intriguing. For one, the results partially invalidate heataggression
theories as there appears to be a difference in the impact of weather/climate on
violence levels in neighborhood groups. Psychological perspectives on heat and
crime i mplicitly propose that t he theories apply to everyone equally; the current
results question that argument and suggest that neighborhood factors at the
very least mediate the impact of heat. Secondly, heataggression theories argue
that heat is the underlying cause of higher violence. The current ndings do not
completely dismiss this idea (seasonal temperature differences remain highly
correlated to violence), but this study does suggest that violence may also
increase when temperatures go from normal to above normal, particularly
during the coolest months of the year. It is difcult to conceive how a 40-
degree-day in January leads to heat-induced irritation and aggressive acts.
Whereas heataggression theories may not be as appropriate to explain the
found patterns in this study, routine activities arguments could be employed to
provide somewhat of an ad hoc explanation. Disadvantaged inner city
communities like those in St. Louis are characterized by a lifestyle in which
many events often take place in public outdoor places.
45,46,53
Pleasant or at
least tolerable weather conditions during the winter months may allow violence-
prone individuals to resume their activities earlier in the year, thus boosting
violence during those normally cool months. This could even affect indoor
domestic assaults because these incidents often occur between related individ-
uals who do not live together. Tolerable weather makes travel easier for
residents i n economically distressed communities as car ownership is often far
below typical levels. A greater number of individuals on the street would likely
create a greater pool of both potential offenders and victims. Unfortunately,
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these explanations for the found patterns could not be directly tested for this
study, which remains a typical problem with the routine activities theory.
CONCLUSION
The current study examines the relation between climate change and levels of
violence in different groups of neighborhoods. Using data from St. Louis, MO,
USA, the ndings indicate that climate change i s likely having a greater impact
on levels of violence in disadvantaged communities than levels of violence in
more afuent communities. After controlling for confounding factors, the most
disadvantaged group of communities in S t. Louis typica lly experience an
average 1 % monthly increase in violent crimes for each degree increase in
anomalous temperatures. In fact, results show that the 20 % of most
disadvantaged neighborhoods are predicted to absorb over 50 % of climate
change-related increases in violence. On the other hand, the least disadvantaged
neighborhoods in the study display no signicant correlation between violence
and temperature anomalies. Combined, these results suggest that those already
at higher risk for a plethora of health issues connected to climate change are
also likely to experience higher levels of violent victimization.
APPENDIX 1: NEIGHBORHOOD GROUPING METHODOLOGY
In order to create similar neighborhood groups, census data from 2000 (the
midpoint in the series) are collected for all 113 census tracts in the city of St. Louis (3
tracts are excluded because their population was below 500) using the Neig hbor-
hood Change Data base (Neighborhood Change Data Base [computer program].
Washington: The Urban Institute; 2004).
Next, a social disorganization index is developed for each census tract using seven
measures of structural disadvantage. These seven measures include the percentage of
people below the federal poverty threshold, the unemploymen t rate, the rate of high
school dropouts, the percentage of female headed households, the proportion of
young Black males (1524 years old), the percentage of properties that are rental
units, and the percentage of homes that are vacant. Reliability analysis on the
elements of the index yields a Cronbachs alpha of 0.804, suggesting substantial
similarity between the individual elements to justify grouping them. Previous
neighborhood studies have used similar indexing techniques to measure extreme
disadvantage in neighborhoods.
7,49,50
The 7 census measures are subsequently standardized and aggregated to create a
social disorganization rank score for the remaining 110 census tracts. In order to
promote normally distributed dependent varia bles, a choice is made to group the
census tracts into 5 equ al groups of 22 to allow for further study. Group 1 is the
least disadvantaged group, whereas group 5 is the most disadvantaged group of
census tracts. This strategy creates enough monthly counts of violence in the least
disadvantaged groups to conduct further analysis.
As Table 1 in the article body indicates, some variability in the total population
between the neighborhood groups exists. Considering that violent crime counts are
actually higher in the groups of neighborhoods with the lowest population, this
should not pose an issue for analysis. The ve groups display the expected
connection between higher levels of social disadvantage and higher levels of
CLIMATE CHANGE AND LEVELS OF VIOLENCE IN DISADVANTAGED NEIGHBORHOODS
Author's personal copy
violence. The poverty rate in group 1 (9.191 %), for instance, is well below that of
group 5 (43.146 %). What is particularly noteworthy is the large difference in the
percentage of young Black males (1524 years old) in the groups. Group 1 only
contains 0.269 % of this high-risk group, whereas group 2 has 10 times as many at-
risk youth (2.471 %). This illustrates the continuing racia l divide in St. Louis where
socially disadvantaged neighborhoods tend to be predominantly African American.
Group 1 (see Table 1) with the lowest levels of disadvantage also has the lowest
levels of violent crime. Subsequent groups show increasing crime rates and
increasing levels of disadvantage. In fact, group 5 has a violent crime rate more
than 10 times that of group 1 (5,059 vs. 494). This indicates that separating distinct
neighborhood groups using the disadvantage measure likely captures the essence of
socially disorganized neighborhoods and their (in)ability to control crime.
46
APPENDIX 2: ADDITIONAL ANALYSIS COMPARISON SITES
One of the reviewers brought up an important issue. How do other places stack up
to the ndings in St. Louis? While it is difcult to locate monthly data by
neighborhoods, a quick comparison of four additional cities (Cleveland (NIBRS)
*
,
New Orleans (UCR), Boston (UCR), and Phoenix (UCR)
.
), reveals more support for
the general idea in this paper (see Table 3). The city in the analysis most comparable
to St. Louis is New Orleans, followed by Cleveland. New Orleans also has ex tremely
disadvantaged neighborhoods and an exceptionally high level of violence. Perhaps
one of the key differences is that New Orl eans has a substantial population of
afuent residents in the downtown area, which may explain the slight difference in
the Tempanomaly variable. What is interesting is that the climate change proxy
variables of St. Louis and New Orleans are fairly close (0.739 and 0.651) despite the
differences in time period examined. What is of further interest is the fact that New
Orleans seasonality pattern is smaller than that of all other places. This is not odd
because New Orleans has less annual temper ature variation than all other places as
its c limate is subtropi cal (winters are relatively pleasant). Cleveland also shares
many similarities with St. Louis, but unfortunately, onl y 5 years of data were
available at present; this likely underestimates the coefcients for seasonality and
climate change. The other two cities (Boston and Phoenix), which were selected here
because they house a more afuent population, display smaller coefcients for both
seasonality and climate change.
The results of this brief comparison thus fall in line with the results of our
neighborhood comparison. The advantage of the neighborhood group approach
is that the socioeconomic, cultural, and c limatic conditions are kept relatively
constant within one city, whereas this is probably not as clear-cut when
comparing cities.
*
Collected from the National Archive of Criminal Justice Data. National Incident-Based Reporting
System, 20042009: Extract Files. ICPSR33601-v1. Ann Arbor, MI: Inter-university Consortium for
Political and Social Research [distributor].
.
Collected from monthly UCR counts collected by Michael Maltz. Available at http://cjrc.osu.edu/
researchprojects/hvd/usa/ucrfbi/. Last accessed November 20, 2011.
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TABLE 3 St. Louis and comparison sites
St. Louis 19902009 Cleveland 20042009
a
New Orleans 19902004
b
Boston 19902004
b
Phoenix 19902004
b
Model ln(1,0,1)(1,0,0) ln(1,0,1)(1,0,0) ln(1,0,1)(0,0,0) ln(1,0,1)(1,0,1) ln(1,0,2)(0,0,0)
Q value (lag 40)
Q0 31.884 Q0 22.365 Q0 31.454 Q0 31.595 Q0 50.524
Sig0 0.816 Sig0 0.989 Sig0 0.831 Sig0 0.826 Sig0 0.123
Month control 5.879*** 6.472*** 4.353*** 5.273*** 2.388***
(0.005) (0.014) (0.009) (0.007) (0.005)
Population control 184.255* 63.582 229.091 110.206 34.439*
(0.825) (0.604) (2.315) (1.060) (0.133)
CPI 0.602*** Not estimated
c
1.475*** 1.278*** Not estimated
c
(0.002) (0.004) (0.002)
Seasonality 0.638*** 0.479*** 0.226* 0.462*** 0.321***
(0.000) (0.001) (0.001) (0.001) (0.000)
Tempanomaly 0.739*** 0.560** 0.651* 0.525** 0.575***
(0.001) (0.002) (0.003) (0.002) (0.002)
Average temperature 57.467 51.186 69.124 51.479 74.787
Per capita income 16,108 17,258 21,003 23,353 19,833
2000 family poverty rate
d
20.8 % 22.9 % 23.7 % 15.3 % 11.5 %
2000 high school completion 27.5 % 33.2 % 23.4 % 24.0 % 22.9 %
Single parents as percent
of all households
12.6 % 15.3 % 14.3 % 9.4 % 7.7 %
*PG 0.05, **PG 0.01, ***PG 0.001
a
Cleveland temperature data: Tempanomaly 20042009 was calculated from deviation of long-term mean 19732000
b
Boston, New Orleans, and Phoenix temperature data 19902004. Long-term mean was based on period 19641984. Anomalies were calculated as actual minus long term mean value
c
Could not be estimated because of high cross-correlation with the population variable
d
Census data from http://quickfacts.census.gov/qfd/index.html , downloaded July 18, 2012
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... Mares (2013b), in St. Louis, MO, USA, discovered a significant relationship between most crime categories and climate change. In other studies, on climate change and violence in the same city, Mares found that neighbourhoods with high social disadvantage have a high degree of violence as a result of anomalous temperature change (Mares 2013b). Sorg and Taylor (2011) studied the effect of temperature on urban street robbery in Philadelphia, USA, and found that robbery increased when temperatures were higher. ...
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This chapter addresses the theoretical, empirical, and practical issues surrounding the temperature–aggression hypothesis. A brief history of the temperature–aggression hypothesis and paradox involving violence and lethargy is described in the chapter. It outlines the major issues and theories surrounding heat effects and provides an integrated model of aggression. In correlation studies of the temperature–aggression hypothesis, there may be complexities that artificially give rise to strong heat effects. The existential question in the chapter refers to whether there is a true direct causal effect of hot temperatures on aggression. Negative affect escape theory focuses on the current state of the individual and their behavioral motives. Two theories that focus on the influence of environmental factors on aggressive cognitions and behaviors are Bandura's groundbreaking social learning theory (SLT) and Berkowitz's contemporary cognitive neoassociation theory (CNT) of emotion. The temperature–aggression hypothesis refers to the theoretical statement that uncomfortable temperatures cause increases in aggressive motivation and (under the right conditions) in aggressive behavior. The General Affective Aggression model theory states that heat effects are both direct and indirect.
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The Changing Rates of Violence in the U.S. The period from 1980 to 1998 has seen some sharp swings in the rate of violence in the United States. The homicide rate in 1980 was at a peak value of 10.2 per 100,000 population, and by 1985 it had fallen to a trough of 7.9. It then climbed a full 24 percent to a peak of 9.8 in 1991, and has been declining markedly since then, reaching a level of 6.3 in 1998, a level that is lower than any annual rate since 1967. The rate of robbery has followed a very similar pattern, oscillating since 1972 between rates of 200 and 250 per 100,000 population, reaching its peaks and troughs within one year of the peaks and troughs of the murder trends. It has also displayed a steady decline since its 1991 peak, and its 1998 rate of 165.2 is lower than any experienced since 1969. These patterns are depicted in Figure 2.1.This chapter focuses primarily on homicide (the ultimate violent act) and secondarily on robbery (the taking of property by force or threat of force) as the principal indicators of violence. In homicide, there is usually a body to be explained, and homicides typically involve intensive police investigation. Robbery is also a relatively well-defined crime and is reported to the police by the victim over one-half the time. © Cambridge University Press 2000, 2006 and Cambridge University Press, 2010.
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There is a prediction implicit in any discussion of age structure and crime. It is that the generation that made the crime wave can break it, too. Landon Y. Jones, Great Expectations: America and the Baby Boom Generation adult homicide rates have fallen continuously for over twenty years. Although it has not gone completely unnoticed, the decline in adult homicide has not figured prominently in recent scholarly attention devoted to violent-crime trends in the United States, which has been dominated by the issue of youth violence. The result is that little is known about patterns of adult violence – a notable exception being domestic or “intimate partner” violence – and we have neither the theory nor requisite research for understanding the decline in adult homicide, including the drop in intimate partner killings. That would not be a big problem for homicide research if adults contributed little to the overall homicide rate. Yet persons 25 years old or older make up over 60 percent of all homicide victims and nearly one-half of the offenders. Moreover, with the aging of the baby boomers, adults are not only the largest but also the fastest growing segment of the U.S. population (U.S. Bureau of the Census 1996a, p. 24, Table 23). An accurate, comprehensive, and policy-relevant portrayal of the recent violence – the goal of this volume – must, therefore, include a description of the patterns among adults, and should offer some assessment of alternative explanations for the two-decade-long decline in adult homicide. © Cambridge University Press 2000, 2006 and Cambridge University Press, 2010.