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<div id="header">
<h1 class="title toc-ignore">Investigating the Socioeconomic Dynamics of
Crime: Exploring the Relationship Between Income and Crime Rates in
Canada</h1>
<h4 class="author">Author: Yinuo Zhao</h4>
</div>
<div id="problem-description" class="section level1">
<h1>Problem Description</h1>
<p>The question of Research interest is: “How does crime rate relate to
income in Canada?”</p>
<p>In order to answer this question, data on both crime and
socioeconomic status are needed. However, I found no existing data set
that contains all desired information, therefore this needs to be
achieved through merging more than one data sets. Aftering choosing
carefully, the following two separate data sets are obtained:</p>
<ol style="list-style-type: decimal">
<li><p>“Income of individuals by age group, sex and income source,
Canada, provinces and selected census metropolitan areas”. Released
2023-05-02. This data set is annually updated and maintained by
Statistics Canada (Table 11-10-0239-01). Data is collected through the
Survey of Labor and Income Dynamics, Survey of Consumer Finances, and
Canadian Income Survey.</p></li>
<li><p>“Incident-based crime statistics, by detailed violations, Canada,
provinces, territories, Census Metropolitan Areas and Canadian Forces
Military Police”. Released 2023-07-27. This data set is also annually
updated and maintained by Statistics Canada (Table 35-10-0177-01,
formerly CANSIM 252-0051). Data is collected through the Uniform Crime
Reporting Survey.</p></li>
</ol>
<p>Understanding the relationship between crime rates and income in
Canada is crucial for policymakers, law enforcement agencies, and social
welfare programs. Exploring this correlation can shed light on the
socioeconomic factors driving criminal behavior and help formulate
targeted interventions to alleviate poverty and reduce crime.
Additionally, elucidating this connection can inform broader discussions
on social inequality, justice, and community well-being in Canadian
society.</p>
<p>Both data sets are downloaded directly from Statistics Canada, which
is usually considered to be an reliable source. Because they share the
same source, the data sets follows similar structure and all contains
the two columns GEO and REF_DATE where the former one refers to the
geographical region and the second one refers to the year of data. Thus,
it’s possible to combine the two data sets to obtain all information
needed.</p>
<p>However, it is worth mentioning that both data sets are huge and
contains unrelated information. Therefore, cleaning and wrangling are
needed for more convenient analysis and more efficient computing &
uploading, as the original data sets are oversize thus cannot be pushed
to github repository.</p>
</div>
<div id="summary-of-findings" class="section level1">
<h1>Summary of Findings</h1>
<ol style="list-style-type: decimal">
<li><strong>Looking at Crime Data:</strong></li>
</ol>
<ul>
<li>Saskatchewan consistently exhibits a significantly higher total
crime rate compared to other provinces throughout the period of 1998 to
2021.</li>
<li>Quebec and Ontario consistently demonstrate the lowest total crime
rates.</li>
<li>Across all provinces, there is a discernible decreasing trend in
total crime rates over the years.</li>
<li>Many provinces experiencing peak total crime rates in
2003-2004.</li>
<li>There is a decreasing trend in the rates of break and enter,
robbery, and prostitution in all provinces.</li>
<li>British Columbia stands out with a significantly high rate of
prostitution in 2004, doubling the number reported in Saskatchewan,
which held the second-highest rate that year.</li>
<li>Across all provinces, there is a discernible decreasing trend in the
average crime rate of all types, from year to year.</li>
</ul>
<ol start="2" style="list-style-type: decimal">
<li><strong>Looking at Income Data:</strong></li>
</ol>
<ul>
<li>On the whole, the average total income for all provinces exhibits a
steady upward trend.</li>
<li>Since 2003, Alberta has surpassed Ontario to become the province
with the highest average total income.</li>
<li>There is a slight decrease in average total income across all
provinces around 2019, likely attributed to the impact of the COVID-19
pandemic.</li>
<li>Employment income, investment income, and market income of provinces
are all increasing.</li>
<li>Employment and market income show a more steady growth pattern,
while investment income fluctuates dramatically from year to year.</li>
</ul>
<ol start="3" style="list-style-type: decimal">
<li><strong>Examining Crime and Income Together:</strong></li>
</ol>
<ul>
<li>Average total income and total crime rate are negatively correlated
across all provinces.</li>
<li>Quebec exhibits the strongest correlation between average total
income and total crime rate as the correlation is close to -1.</li>
<li>In the relationship between robbery crime rate and employment
income, all provinces display a negative trend except for Newfoundland
and Labrador. Manitoba demonstrates a weak relationship, as evidenced by
the considerable dispersion of points around the line.</li>
<li>Property crime rate decreases with increasing market income in all
provinces.</li>
<li>Employment income and prostitution crime rate have varying degrees
of association across provinces, with many showing a weak relationship.
Notably, Ontario exhibits a positive relationship between employment
income and prostitution crime rate while it is negative for other
provinces.</li>
</ul>
<ol start="4" style="list-style-type: decimal">
<li><strong>Zoom on Province - Ontario:</strong></li>
</ol>
<ul>
<li>Major crimes in Ontario are property crime and weapon
violations.</li>
<li>Strongest correlation is observed between self-employment income and
production under the Cannabis Act. However, it’s important to note that
this relationship may not be entirely reliable due to the limited data
available.</li>
<li>The second strongest correlation, with a coefficient of 0.98, is
observed between total income and incidents of possession of other
Controlled Drugs and Substances Act drugs. As income levels increase,
the number of incidents of possession of these drugs also tends to
rise.</li>
</ul>
<ol start="5" style="list-style-type: decimal">
<li><strong>Group by Income Level</strong></li>
</ol>
<ul>
<li>Created 4 levels for average total income using the quarantines,
from negative infinity to the first quantile is “Low”, from first
quantile to mean is “Med_Low”, from mean to 3rd quantile is “Med_High”,
from 3rd quantile above is “High”.</li>
<li>Total crime rate does not exhibit a clear trend of decreasing with
higher levels of total income, which contradicts previous observations
when examining the relationship between total crime rate and average
total income by province. while a relationship exists, it may be
influenced by other factors related to the demographics of each
province. Consequently, when considering all observations collectively,
the relationship becomes less apparent.</li>
</ul>
<ol start="6" style="list-style-type: decimal">
<li><strong>Fitting Statistical Models</strong></li>
</ol>
<ul>
<li>The linear regression model analyzed the relationship between Crime
Rate per 100,000 population and Average income (excluding zeros) across
different provinces represented by the categorical variable GEO.</li>
<li>Findings suggest a negative correlation between average income and
total crime rate, implying that higher average income tends to coincide
with lower crime rates.</li>
<li>Provinces exhibit varying baseline rates, with British Columbia
notably showing a significantly higher rate compared to the reference
province.</li>
<li>Interaction terms between income and provinces reveal differing
effects across regions, such as a more pronounced negative association
between income and crime rate in British Columbia.</li>
<li>The model demonstrates a robust fit with an adjusted R-squared value
of 0.9328, indicating significant influences of both income and province
on the crime rate per 100,000 population, with nuanced variations across
different regions.</li>
</ul>
</div>
<div id="acknowledgements" class="section level1">
<h1>Acknowledgements</h1>
<p>This project is created by Yinuo Zhao as part of the course JSC370H1
offered by the University of Toronto in Winter 2024, instructed under
Jun Ni (Jenny) Du and professor Meredith Franklin.</p>
</div>
<div id="references" class="section level1">
<h1>References</h1>
<ol style="list-style-type: decimal">
<li><p>Statistics Canada. (Year). Income of individuals by age group,
sex and income source, Canada, provinces and selected census
metropolitan areas. Statistics Canada. <a
href="https://doi.org/10.25318/1110023901-eng"
class="uri">https://doi.org/10.25318/1110023901-eng</a></p></li>
<li><p>Statistics Canada. (Year). Incident-based crime statistics, by
detailed violations, Canada, provinces, territories, Census Metropolitan
Areas and Canadian Forces Military Police. Statistics Canada. <a
href="https://doi.org/10.25318/3510017701-eng"
class="uri">https://doi.org/10.25318/3510017701-eng</a></p></li>
<li><p>Henderson, M. T., Wolfers, J., & Zitzewitz, E. (2010).
Predicting crime. Ariz. L. Rev., 52, 15.</p></li>
<li><p>Hooghe, M., Vanhoutte, B., Hardyns, W., & Bircan, T. (2010,
December). Unemployment, Inequality, Poverty and Crime: Spatial
Distribution Patterns of Criminal Acts in Belgium, 2001–06. The British
Journal of Criminology, 51(1), 1-20. <a
href="https://doi.org/10.1093/bjc/azq067"
class="uri">https://doi.org/10.1093/bjc/azq067</a></p></li>
<li><p>Perry, W. L., McInnis, B., Price, C. C., Smith, S., &
Hollywood, J. S. (2013). Predictive Policing: Forecasting Crime for Law
Enforcement. RAND Corporation.</p></li>
</ol>
</div>
<div id="about-jsc370h1" class="section level1">
<h1>About JSC370H1</h1>
<p>JSC370H1: Data Science II is a course restricted to students in the
Data Science Specialist program. Students will learn to identify and
answer questions through the application of exploratory data analysis,
data visualization, statistical methods or machine learning algorithms
to complex data. Software development for data science and reproducible
workflows. Communication of statistical information at various technical
levels, ethical practice of data analysis and software development, and
teamwork skills. Topics will be explored through case studies and
collaboration with researchers in other fields. (source: UofT Academic
Calendar)</p>
</div>
</div>
</div>
</div>
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});
</script>
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<script>
$(document).ready(function () {
window.buildTabsets("TOC");
});
$(document).ready(function () {
$('.tabset-dropdown > .nav-tabs > li').click(function () {
$(this).parent().toggleClass('nav-tabs-open');
});
});
</script>
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<script>
$(document).ready(function () {
// temporarily add toc-ignore selector to headers for the consistency with Pandoc
$('.unlisted.unnumbered').addClass('toc-ignore')
// move toc-ignore selectors from section div to header
$('div.section.toc-ignore')
.removeClass('toc-ignore')
.children('h1,h2,h3,h4,h5').addClass('toc-ignore');
// establish options
var options = {
selectors: "h1,h2,h3",
theme: "bootstrap3",
context: '.toc-content',
hashGenerator: function (text) {
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},
ignoreSelector: ".toc-ignore",
scrollTo: 0
};
options.showAndHide = true;
options.smoothScroll = true;
// tocify
var toc = $("#TOC").tocify(options).data("toc-tocify");
});
</script>
<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
(function () {
var script = document.createElement("script");
script.type = "text/javascript";
script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
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