Crimes in Kenosha: What are the Trends?
By
Alexandria Caputo
An Undergraduate Thesis
Submitted in Partial Fulfillment for the Requirements of
Bachelor of Arts
In
Geography and Earth Science
Carthage College
Kenosha, WI
December 2014
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Crime in Kenosha: What are the Trends?
Alexandria Caputo
December 2014
Abstract
Crime has been studied for as long as crimes have been committed. There is a solid
base of information available that portrays common spatial patterns and trends when
analyzing crime. Conducting hotspot analyses of five different crime type, including
burglary/theft/robbery, domestic violence, assault/battery, disorderly conduct and
drug/alcohol crimes. Often time police have limited time and resources in order to help
keep citizens safe, so analyzing where hotspots are located throughout the Kenosha
boundaries can help police focus their efforts on specific areas in which clustering
occurs. Based off these hotspots, overlaying crime layers with different socioeconomic
variables can show different trends as well. The Kenosha Police Department can use
this data to gain a better understanding of crime patterns in their jurisdiction and with
that they can develop methods of prevention.
Acknowledgements
I would like to thank my thesis advisor Dr. Wenjie Sun for helping guide me in
writing my senior thesis, always giving me feedback on my drafts and maps, and
providing me with the necessary resources to complete my thesis well. I would also like
to thank Dr. Kurt Piepenburg for assisting wherever I needed him and making sure
deadlines were met. I would like to thank Dr. Matt Zorn and Dr. Joy Mast for helping me
grow in my education throughout my first few years at Carthage. Without the knowledge
I obtained from being in your class, I would never have been given opportunities that I
have.
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Table of Contents
List of Tables….................................................................................................................4
List of Figures…………………………………………………………………………….…..…5
Introduction…………………………………………………………………………………...…6
Problem Statement…………………………………………………………………………......6
Hypotheses…………………………………………………………………………………..7 - 8
Hotspots ...............................................................................................................7
Demographic Factors……………………………………………………………….7 - 8
Literature Review……………………………………………………………………….….8 - 12
Crime Pockets……………………………………………………………………..……8
Seasonal Trends in Crime Rates……………………………………………...…9 - 10
What Causes Differing Crime Rates………………………………………….. 10 - 12
Methods …………………………………………………………………………….…....12 - 14
Site Selection………………………………………………………………………….12
Data Acquisition………………………………………………………………………13
Converting into Usable Layer File……………………………………………….….13
GIS: Mapping………………………………………………………………………….13
GIS: Collect Events Tool……………………………………………………………..13
GIS: Average Nearest Neighbor ……………………………………………….13 - 14
GIS: Getis-Ord GI Test…………………………………………………………….….14
Overlay Demographic Data with Hotspot Layers……………………………….….14
Results…………………………………………………………………………………....14 - 42
Distribution of Crimes……………………………………………………….…..15 - 21
Frequency of Crimes……………………………………………………….…...22 - 26
Average Nearest Neighbor ………………………………………………………….27
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Hotspot Analysis…………………………………………………………………..28-35
Demographic Maps………………………………………………………..…….36 - 42
Discussion and Conclusion ………………………………………………………………….43
Future Research……………………………………………………………….….43- 45
References………………………………………………………………………………….….45
List of Tables
Table 1: Average Nearest Neighbor Results
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List of Figures
Figure 1: Distribution of Crime
Figure 2: Distribution of Burglary/Theft/Robbery Crimes
Figure 3: Distribution of Domestic Violence Crimes
Figure 4: Distribution of Drug and Alcohol Crimes
Figure 5: Distribution of Assault and Battery Crimes
Figure 6: Distribution of Disorderly Conduct Crimes
Figure 7: Frequency of Burglary/Theft/Robbery per Location
Figure 8: Frequency of Domestic Violence Crime per Location
Figure 9: Frequency of Drug and Alcohol Crimes per Location
Figure 10: Frequency of Assault and Battery Crimes per Location
Figure 11: Frequency of Disorderly Conduct Crimes per Location
Figure 12: Hotspot Analysis of Burglary/Theft/Robbery Crimes
Figure 13: A Close-up Hotspot Analysis of Burglary/Theft/Robbery Crimes
Figure 14: Hotspot Analysis of Domestic Violence Crimes
Figure 15: Hotspot Analysis of Drug and Alcohol Crimes
Figure 16: A Close-up Hotspot Analysis of Drug and Alcohol Crimes
Figure 17: Hotspot Analysis of Assault and Battery Crimes
Figure 18: Hotspot Analysis of Disorderly Conduct Crimes
Figure 19: A Close-up Hotspot Analysis of Disorderly Conduct Crimes
Figure 20: Disorderly Conduct Hotspots VS Average Household Income
Figure 21: Drug and Alcohol Hotspots VS Average Household Income
Figure 22: Disorderly Conduct Hotspots VS Diversity Index
Figure 23: Drug and Alcohol Hotspots VS Diversity Index
Figure 24: Disorderly Conduct Hotspot VS Percent of Male Population Over 18
Figure 25: Drug and Alcohol Hotspot VS Percent of Male Population Over 18
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Crimes in Kenosha: An Analysis of Spatial Patterns and Trends
Alexandria Caputo
Introduction
Crime has been studied for as long as crimes have been committed. There is a
solid base of information available that portrays common trends found when analyzing
crime. Often time police have very limited time and resources in order to help keep
citizens safe. While it is important to understand crime trends on a national scale, not all
patterns are the same. Police departments should understand the crime trends for their
jurisdiction, in order to best predict and respond to crimes. What might work for one
department might not work for another one; therefore police need to be well-educated
on trends that can occur in their area.
The crime data available for the study area was for a five month time span during
the warmer months. It is important to note that crimes might follow a specific temporal
trend, therefore the study conducted focused on crimes that do not tend to follow a
seasonal trend, as data from all seasons was not available.
Problem Statement
Crimes often do not distribute randomly or uniformly in space, rather there are
pockets of more dangerous areas. It is important to find and target limited police
resources at the crime hotspots in order to more effectively and efficiently fight crime.
Demographic and socioeconomic factors may have an impact on the spatial distribution
of crimes.
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Hypotheses
Analyzing crime hotspots i.e. clustering in spatial data, can portray where crimes are
located throughout the Kenosha boundaries.
Crime Hotspots
1.
·
·
All crimes combined
H01 – There is no hotspot in the spatial distribution of all types of crime combined.
HA1 – There are hotspots in the spatial distribution of crimes combined.
2.
·
·
Burglary/Theft/Robbery
HO2 – There is no hotspot in the spatial distribution of burglary/theft/robbery crime.
HA2– There are hotspots in the spatial distribution of burglary/theft/robbery crime.
3.
·
·
Domestic Violence
HO3 – There is no hotspot in the spatial distribution of domestic violence crime.
HA3 – There are hotspots in the spatial distribution of domestic violence crime.
4.
·
·
Drug and Alcohol
HO4 – There is no hotspot in the spatial distribution of drug and alcohol crime.
HA4 – There are hotspots in the spatial distribution of drug and alcohol crime.
5.
·
·
Battery
HO5 – There is no hotspot in the spatial distribution battery crime.
HA5 – There are hotspots in the spatial distribution of battery crime.
6.
·
·
Disorderly Conduct
HO6 – There is no hotspot in the spatial distribution of disorderly conduct crime.
HA6 – There are hotspots in the spatial distribution of disorderly conduct crime.
Demographic Factors
7. Income
·
HO7 – Crime rate is not lower in higher income areas.
·
HA7 – Crime rate is lower in higher income areas.
8. Diversity
·
HO8 – Crime rate is not lower in lower diversity areas.
·
HA8– Crime rate is lower in lower diversity areas.
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9. Percent of Males over 18
·
HO9 – Crime rate is not higher in areas where there is a higher percent of males
who are above 18 years old.
HA9 – Crime rate is higher in areas where there is a higher percent of males who
are above 18 years old.
Literature Review
Crime Pockets
When studying crime rate trends in a particular area, there are bound to be areas
where there are crime pockets. Crime pockets are hotspots where there are more
crimes in a section of a neighborhood, compared to other parts of the neighborhood.
Peter K. B. St. Jean studied the geography of crime in Chicago, focusing mainly on the
“Bronzeville” neighborhood. Through his research he found that there were certain
“hotspots” in the study area. These “hotspots” are locations in which a significant
amount of dangerous activities cluster, in comparison to the rest of the area studied. In
these “hotspots”, St. Jean found that there were higher poverty rates and high
residential turnover, compared to other locations. There is a lack of neighborhood
cohesion among the neighbors, because of the high residential turnover which causes a
lack of trust and expectations among them. (St. Jean 2007).
Hotspot mapping is a really popular analytical technique which is used to identify
where the police should target their crime reduction resources. Hotspot mapping relies
on previous data to identify high concentrations of crime. Police use the hotspots to
predict where crime is most likely to occur in the months ahead. Hotspot mapping
accuracy depends on the type of crime. Hotspot mapping was better in predicting future
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street crimes than any other crime type however; it was still able to predict the future of
other crimes as well (Chainey 2008).
Seasonal Trends in Crime Rates
When looking at crime trends there is already a huge base of knowledge
available. When doing a study of crimes where there is limited data available, making
sure that you study select crimes that are not affected by seasonal changes is very
important. Crimes that are affected by the changing of the seasons tend to see a cyclic
pattern with spike or drop in rates throughout the year. There are many things that can
explain this seasonal change in crimes. There are two main theories that describe
seasonal changes in crimes: Temperature/aggression theory and routine activities
theory. Both theories were first proposed by Quetelet. The temperature/aggression
theory states that “uncomfortably hot temperatures increase the frustration that
individual’s feel, which leads to aggressive behavior.” The routine activities theory
states that when there are pleasant temperatures outside, more individuals spend less
time in their house and more time outdoors. They have more time to spend on crimes
as a result of this (Quetelet 1969).
Since Quetelet did his research in France, more recent studies are comparing
the patterns he saw in France to what they are finding in the United States. A group of
four professors from University of North Carolina at Chapel Hill and North Carolina State
University dedicated their efforts to testing Quetelet’s two theories. They found that
each of the two theories suggests a positive relationship between seasonal temperature
and changes in crime rates. They found that the temperature/aggression theory does
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show that hotter temperatures correlate to greater aggression and violent crimes.
However it does not affect property crime rates because property crime tends to involve
calculating behavior and not necessarily aggression. They have found that in the
routine activities theory that people are more likely to be outside when the weather is
nicer. With more time spent outside the home, there is more time to commit crimes.
They found that this theory however, does not mean an increase in aggressive crimes
(Hipp et al 2004). Two professors at Wayne State University in Detroit Michigan through
their research have found that crimes such as burglary, larceny vehicle theft, and arson
show no changes in rates with the changing seasons (Lim et al 2009).
What Causes Differing Crime Rates
There are many different factors that affect crime rates throughout the world.
There have been many studies that examine crimes at a national level. A main factor
that has been found to affect crime rates is inequality. Rosemary Gartner studied the
relationship between crime and inequality on a national scale. Her studies have found
that countries with a high degree of economic inequality have a higher level of violence.
She proposed that the nation’s homicide rate is shaped by four different contexts:
material, integrative, demographic, and cultural. Homicide rate is shaped by material
functions. There is a form of economic stress that results from an unequal distribution of
resources. When there is a family that has absolute deprivation from resources, there is
an increased risk of homicides when being compared to their counterparts. If there is
economic inequality, she found that this results in aggression and hostility. She also
found that people tend to kill those who they feel in competition with. The government
needs to get more involved in certain places: “Where government efforts provide a
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minimum living standard are more limited, and where income inequality is greater,
homicide rates will be greater for both females and males, adults and children.” The
next context that Gartner proposed was the rates differed based on social integration.
Weakened social integration lessens social control in the individual, which could
increase most types of homicide. People that integrate themselves into society really
well, tend to be able to control themselves in different environments and therefore less
likely to commit any form of homicide. Demographic factors also affect changes in
homicide levels. If a population is younger, then their activities are dispersed and people
are involved in so many things. Because of this they are less effective in controlling
themselves socially, and the opportunity for homicide rises. Culture is another thing that
affects rates of crime. If there are a lot of varying cultures that live in close proximity to
each other, there is more likely to be violence as a way to try to solve it (Gartner 1990,
1992).
There was another study that focused on the links between economic inequality,
family disruption, and urban African- American violence in more than 150 cities in the
United States. This study shows us that as economic inequality increases throughout an
area so do arrests of African-American youths for violent crimes. If an area has a higher
income inequality, this tends to increase the number of African-American single-parent
households, which is related to the increase of youth violence. Single parents have
more stress and fewer resources which causes them to have a more difficult time
monitoring their children (Shihadeh et al 1994).
We live in a world where people are focused on having the newest and “next big
thing” on the market. Unfortunately this causes a lot of trouble amongst people who do
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not have the appropriate monetary means to materialistic goods. There is this attitude
that you have to pursue the acquisition of goods by any means necessary, even if how
you achieve them is illegal (Kramer 2009). For the economically disadvantaged people
“this anomic orientation leads not simply to high levels of crime in general but to
especially violent forms of economic crime, for which the United States is known
throughout the world, such as mugging, car-jacking, and home invasion” (Messner
1997).
Another factor that affects crime rates is disruptive families. Quetelet found that if
a family has at least one member who actively engages in disruptive behavior, the
family is less likely to feel like they belong in a neighborhood. These families tend to
leave each time a member becomes disruptive in a neighborhood, to areas in which
rent is lower. This tends to cause concentrations of disruptive families because they all
find cheap rental properties, therefore a crime pocket occurs. The crimes that tend to
occur when disruptive families live in one area are domestic violence and acquaintance
battery (Quetelet 1969).
Methods
Site Selection
For this study, Kenosha, Wisconsin was chosen. Kenosha is located just north of
the Wisconsin-Illinois border between Milwaukee, Wisconsin and Chicago, Illinois. As of
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2013, Kenosha has a population of 167,757 people. Eighty-Seven percent of the
population are of the white race (US Census Bureau, 2013?).
Data Acquisition
Crime data was acquired from the Kenosha Police Department for the period of
May 1, 2014 until September 30, 2014. The information included type of crime, date and
time committed, and location. The data obtained came in the form of a spreadsheet with
over 1600 crime records available. The Excel spreadsheet was then formatted in order
to be used in ArcGIS software. The types of crimes analyzed were assault/battery,
domestic violence, burglary/theft/robbery, disorderly conduct, and drug/alcohol crimes.
GIS: Mapping
Mapping is commonly used to visualize spatially-tagged data and portray spatial
relationships. A simple data table of all the crimes committed would not be enough to
capture the spatial pattern of crimes that occurred in Kenosha throughout the five month
time period. Mapping out the crimes would provide a visual representation that would be
easier to understand than a data table.
GIS: Collect Events Tool
Some locations/addresses have multiple crimes happened during the 5 month
time period. This tool graphed out where these locations were using graduated symbols.
The bigger the marker on a point was, the more crimes that were committed at that
location. There were as few as 1 crime at an address and as many as 14 crimes so this
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graduated symbol map portrayed where crimes happened more often. The five types of
crimes were all analyzed separately and then together.
GIS: Average Nearest Neighbor
The Average Nearest Neighbor is a spatial statistic tool in ArcGIS. This tool
calculates a nearest neighbor index based on the average distance from each feature to
its nearest neighboring feature together with a z-score, which indicates the level of
statistical significance. The nearest neighbor index is then compared to that derived
from a hypothetical random distribution with the same number of features (crimes) and
the same study area, to infer whether the global pattern is clustered, dispersed, or
random.
GIS: Getis- Ord Gi* (Hotspot Analysis)
The Getis-Ord Gi* test identifies local clusters of high or low feature attribute
values. This test looks at individual locations and their attribute values, which in this
case is the number of crimes and then compares the value to those of neighboring
addresses and eventually identifies statistically significant hot spots (clusters of high
values) and cold spots (clusters of low values) using the Getis-Ord Gi* statistic. The five
types of crimes were all analyzed separately so the trends can be seen for each
different crime type, and then together to analyze general trends in crime.
Overlay Demographic Data with Crime Hotspot Layers
Mapping out demographic data, including income, diversity, and percent of males
over 18, with the crime hotspot layer on top will show possible spatial correlations
between demographics and crime rates.
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Results
After processing the data, visually portraying the data, and interpreting what it all
meant, it showed that there were spatial trends in certain crime types. My hypotheses
were accurate in the Burglary/Theft/Robbery, Drug and Alcohol, and Disorderly Conduct
crime types, in that they all portrayed hotspots of dangerous activity. My data showed
that my hypothesis was incorrect because assault/battery and domestic violence did not
portray any hotspots. My data supported my hypothesis that crime is not lower in higher
income areas, but does not support my hypotheses of crime being lower in lower
diverse areas, or crime is not higher in areas which have a higher population of males
over 18.
Distribution of Crimes
The following maps (Figure 1-6) show the distribution of crimes throughout
Kenosha.
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Figure 1: Distribution of All Crime
Around 1600 crimes were committed in Kenosha from May 1, 2014 through September
30, 2014.
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Figure 2: Distribution of Burglary/Theft/Robbery Crimes
The above map shows where 531 burglary/theft/robbery crimes were committed
throughout the five month time span. There are places where crimes are concentrated,
while there are places where there is one crime secluded from the others.
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Figure 3: Distribution of Domestic Violence Crimes
The above map portrays the distribution of 111 domestic violence crimes committed in
the Kenosha area.
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Figure 4: Distribution of Drug and Alcohol Crimes
The above map is a visual representation of the distribution of 326 drug and alcohol
crimes.
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Figure 5: Distribution of Assault and Battery Crimes
The above map portrays the distribution of 104 assault and battery crimes.
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Figure 6: Distribution of Disorderly Conduct Crimes
The figure above shows the distribution of 474 disorderly conduct crimes committed in
Kenosha in the five month span.
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Frequency of Crimes
The frequency of crimes per location portrays that some addresses have multiple
crimes that happen during the five month time frame.
The following figures (Figure 7- 11) portray the frequency of crimes occurring at a
particular address.
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Figure 7: Frequency of Burglary/Theft/Robbery per Location
As portrayed on the map above there are properties in Kenosha that experienced
more than one crime during the five month period. There were two particular addresses
in which there were 26-34 crimes of burglary/theft/robbery in the five month time span.
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There were many locations in which only one crime was committed at that address
during the same time period.
Figure 8: Frequency of Domestic Violence Crime per Location
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The above map portrays that each location has less than four crimes committed
at each address. On two occasions there are three crimes committed at a particular
spot, while most locations have one crime committed.
Figure 9: Frequency of Drug and Alcohol Crimes per Location
The figure above shows that there are a few areas with more than fifteen crimes
per location, while there are areas in which there are only a few crimes committed per
address.
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Figure 10: Frequency of Assault and Battery Crimes per Location
Battery and assault crimes per location are relatively few. There are a few locations in
which crime rate is slightly higher in particular locations.
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Figure 11: Frequency of Disorderly Conduct Crimes per Location
The figure above shows that there are only a few addresses in which there are a
significantly higher number of crimes. There is a cluster of crime that occurs within a few
blocks.
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Average Nearest Neighbor
Table 1: Average Nearest Neighbor Results
Observed Expected Nearest
Mean
Mean
Neighbor
Crime Type
Distance Distance Ratio
z-score
p-value
Burglary/Theft/Robbery
0.00089 0.002435 0.36556 -27.9685
0
Drug and Alcohol
0.000968 0.003184 0.304122 -24.0366
0
Domestic Violence
0.002794 0.005173 0.540206 -9.26735
0
Battery/Assault
0.001638 0.002983 0.549293 -8.79309
0
Disorderly Conduct
0.000702 0.002492 0.281689
-29.918
0
All
0.000633 0.002114 0.299306 -37.8907
0
The test results provide evidence that crimes are heavily clustered in Kenosha because
the observed mean distance is significantly less than the expected mean distance. The
z-score is the most important statistic in the above table. If the z-score is less than -2 or
-3 then it is evident that clustering did not occur by chance. With values as low as 37.89 to -8.79, it indicates that there is a high level of statistical significance to the
clustering of crime events. Burglary/Theft/Robbery, Drug and Alcohol, and Disorderly
Conduct appear to have a higher statistical significance of the clustering, than domestic
violence and battery/assault do.
Hotspot Analysis
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Using the Getis- Ord Gi test it was found that there were different patterns of
hotspots throughout. While some crimes had no hotspot apparent, there were others
that had many crime pockets.
The following figures (Figures 12-13) portray the hotspot analysis of
burglary/theft/robbery crimes.
Figure 12: Hotspot Analysis of Burglary/Theft/Robbery Crimes
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Figure 13: A Close-up Hotspot Analysis of Burglary/Theft/Robbery Crimes
Figure 12 and Figure 13 show that there are hotspots of crime in the neighborhood
south of 52nd street, east of Pershing Blvd, and west of Green Bay Road. The map also
shows cold spots north of 142. There are also a few more hotspots and cold spots
scattered around.
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Figure 14: Hotspot Analysis of Domestic Violence Crimes
The figure above shows that there are no hotspots for domestic violence crimes.
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Figure 15: Hotspot Analysis of Drug and Alcohol Crimes
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Figure 16: A Close-up Hotspot Analysis of Drug and Alcohol Crimes
Figure 15 and Figure 16 show that there are hotspots. With a 99% confidence level
there are many hotspots along 63rd St in between 22nd Ave and Sheridan Road. There
are also many cool spots, including along 52nd St, in between 30th Ave and Pershing
Blvd.
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Figure 17: Hotspot Analysis of Assault and Battery Crimes
The above figure shows that there are 4 areas in which a hotspot occurs, while
most areas show no hotspots.
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Figure 18: Hotspot Analysis of Disorderly Conduct Crimes
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Figure 19: A Close-up Hotspot Analysis of Disorderly Conduct Crimes
Figures 18 and 19 above show the hotspot analysis of Disorderly Conduct
Crimes. There are many hotspots just north of and along 63rd street, south of 60th street,
east of 30th avenue, and west of Sheridan Road.
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Demographic Analysis
Two crime types that showed clustering and hotspots were disorderly conduct
and drug and alcohol crime. These crime types were overlaid and compared to three
different demographic variables, including average household income, diversity index,
and percent of male population over 18.
Figure 20 and 21 below show the relationship between hotspots of disorderly
conduct crimes and drug and alcohol crimes and average household income.
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Figure 20: Disorderly Conduct Hotspots VS Average Household Income
The figure above shows that in areas of crime, hotspots i.e. clusters of high crime
locations, the levels of income are lower. There are a few crimes committed in the
highest average income locations; however, there is no hotspot of crime present there.
There are a high number of crimes committed in the central portion of Kenosha, and
also low income in those areas.
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Figure 21: Drug and Alcohol Hotspots VS Average Household Income
The above figure shows that in areas of lower income there are a significantly
higher number of crimes committed than in areas of higher income. There are a few
outliers, in which crime is committed in a high income location. Drug and Alcohol crime
hotspots align well with lower income neighborhoods.
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Figure 22: Disorderly Conduct Hotspots VS Diversity Index
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Figure 23: Drug and Alcohol Hotspots VS Diversity Index
In areas where diversity is higher, there is a clustering of higher crime rates for
both Disorderly Conduct and Drug and Alcohol Crimes.
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Figure 24: Disorderly Conduct Hotspot VS Percent of Male Population Over 18
The above figure shows that crimes are less concentrated in areas that have a
higher percent of males over 18.
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Figure 25: Drug and Alcohol Hotspot VS Percent of Male Population Over 18
The above map shows that high crimes are more clustered in areas that have a
lower percent of male population over 18.
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Discussion and Conclusion
The Kenosha Police Department can use the findings from this research to focus
on certain areas to patrol. The police department only has a limited amount of resources
and patrol officers available so knowing the distribution of crime and where certain
hotspots are can be a huge help. The police can focus on the more dangerous areas of
the city and concentrate its efforts to make sure that Kenosha is a safe place. Kenosha
has a lot of room to grow when it comes to management of crimes.
Hotspot analysis does tell a lot about crimes. As the data portrayed there were
areas in which there were hotspots for certain crimes, while not for others. The crimes
that showed hotspots were burglary/theft/robbery, drugs and alcohol, and disorderly
conduct. Police can focus on these said areas in order to target high crime areas.
Looking at different demographic and social factors can also be a sign of what
could affect crimes. As shown in the different figures, in the areas where crime is higher
income is significantly lower, diversity is higher, and percentage of males over 18 is
lower.
The above information can be used to analyze the spatial patterns and trends of
crime in Kenosha and be a helpful resource to the police department.
Further Research
There are numerous factors that could affect crime rates for different crime types
in a given area, therefore it is important to analyze crime on a regular basis. While the
data analyzed may be sufficient for now, in two years many thing could completely
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change. If crime is analyzed on a regular basis, then the police can look at rates and
patterns of crime and compare it to other data sets. Taking a look at more demographic
features would be important as well. Certain demographic factors could have an effect
on crime rates, therefore studying this can also enable police to make a very educated
decision on how to prevent more crimes in their city.
The data available was only for a five month time span; therefore crimes that are
affected by seasonal change were not analyzed. Since aggressive crimes, such as
homicides tend to occur more often in the warmer months, studying the trends of
homicides in a five month period is not going to help educate police officers. There are
going to be more homicides in the summer months than in the winter, so using hotspots
to predict where crimes are more likely to be committed will ultimately not have any
merit, due to the fact that rates change between summer months and winter months.
The crimes analyzed here have not been known to be affected by seasonal trends,
nevertheless, it would be interesting to compare the summer months to other times of
the year if data from the entire year is available.
If given the data for a whole year at a time, then all crime types should be
studied. Only five crime types were analyzed, but there are many other crimes that have
not been analyzed, therefore if a larger data set was obtained, repeating this process
using all the types of crimes would make the police more knowledgeable on the big
picture in Kenosha, Wisconsin.
Different demographic factors could be analyzed to better understand the
breakdown of crimes. Studying more demographic features and their relationship
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towards crime can be a really beneficial analysis.
References
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3. Gartner, Rosemary. "The Victims of Homicide: A Temporal and Cross-National
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