Passion Project by Justin Underwood

summary(cars)
##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

Executive Summary

The following report examines the relationship between the rate of robberies and the average Consumer Price Index (CPI) of regular gas. Through the analysis of multiple data sets, I aimed to understand any potential correlations or patterns between these two factors in different cities.

In the study, I collected and analyzed data from three major cities: Atlanta, Boston, Chicago, and Dallas. The data sets encompassed a range of years, allowing us to observe trends over time. I used visual representations, specifically line graphs, to depict the data and identify potential connections between robbery rates and average CPI.

In Atlanta, I observed intriguing trends in the data regarding the average CPI of regular gas and the rate of robberies. Over the examined years, the average CPI displayed some fluctuations, but no distinct pattern emerged. Conversely, the rate of robberies exhibited a noticeable decline, indicating a potential inverse relationship between gas prices and robbery rates in Atlanta.

In Boston, I observed a gradual decline in the rate of robberies over the years. Simultaneously, the average CPI of regular gas demonstrated slight fluctuations, but no distinct pattern emerged. This suggests that other factors may contribute more significantly to the changes in robbery rates in this city.

Chicago displayed varying trends between the two factors. While the rate of robberies experienced fluctuations over the years, the average CPI of regular gas remained relatively stable. This indicates that changes in robbery rates in Chicago are likely influenced by factors independent of the average CPI.

In Dallas, the rate of robberies demonstrated some fluctuations, but overall, it exhibited a downward trend. Similarly, the average CPI of regular gas in Dallas exhibited a general decline. Although no conclusive relationship can be determined from these observations alone, the parallel downward trends may suggest a potential association between lower gas prices and a decrease in robbery rates in Dallas.

Overall, my analysis indicates that the rate of robberies does not consistently correlate with the average CPI of regular gas across the three cities. While there may be some localized associations, it is essential to consider additional factors that could influence changes in robbery rates, such as socioeconomic conditions, law enforcement strategies, and demographic shifts.

This report highlights the need for further research and a comprehensive analysis of various factors that contribute to changes in robbery rates. Understanding the intricate dynamics involved will assist policymakers and law enforcement agencies in formulating effective strategies to address and prevent robberies in different cities.

Introduction

This project delves into a comprehensive analysis of the average Consumer Price Index (CPI) of regular gas and the rates of robberies in major cities across the United States. By examining the datasets from Dallas, Atlanta, Chicago, and Boston, I aim to uncover potential insights, patterns, and correlations between these two critical factors. The primary purpose of this project is to shed light on the relationship between gas prices and robbery rates, contributing to a deeper understanding of the dynamics that influence crime rates in urban settings.

Understanding the complex interplay between economic factors, such as gas prices, and crime rates is crucial in today’s society. Fluctuations in gas prices can significantly impact the cost of living, transportation expenses, and the financial circumstances of individuals and communities. Consequently, it is important to explore whether changes in gas prices have any discernible influence on criminal activities, particularly robberies, which often stem from socioeconomic factors.

By examining the datasets from Dallas, Atlanta, Chicago, and Boston, I can analyze these cities’ unique characteristics and economic landscapes to gain a broader perspective. Each city represents a distinct urban setting, providing valuable insights into the relationship between gas prices and robbery rates within different contexts. Additionally, exploring nationwide data for the United States allows us to observe broader trends and variations across different regions.

The motivation behind this project stems from the need for evidence-based research and insights that can inform policymakers, law enforcement agencies, and urban planners. By gaining a deeper understanding of the relationship between gas prices and robbery rates, informed decisions can be made regarding crime prevention strategies, resource allocation, and the formulation of targeted policies aimed at reducing criminal activities.

The project’s purpose is twofold: first, to examine the relationship between gas prices and robbery rates, assessing whether changes in gas prices coincide with any discernible patterns in crime rates. Second, to identify any potential variations or discrepancies among the cities studied, highlighting factors beyond gas prices that may influence crime rates, such as socioeconomic conditions, law enforcement strategies, and demographic factors.

Method & Data Sources:

Average US Price Data

https://www.kaggle.com/datasets/robertritz/average-price-data-bls

Crime in the United States

https://ucr.fbi.gov/crime-in-the-u.s To conduct this analysis, data on gas prices and robbery rates were sourced from different reputable platforms. The gas price data was obtained from the Kaggle dataset “Average Price Data (BLS)” available at https://www.kaggle.com/datasets/robertritz/average-price-data-bls. This dataset provides information on the average Consumer Price Index (CPI) of regular gas across various years. The dataset was downloaded in a CSV format for further analysis.

On the other hand, the robbery rate data for the United States was sourced from the official website of the Federal Bureau of Investigation (FBI) at https://ucr.fbi.gov/crime-in-the-u.s. The FBI’s Uniform Crime Reporting (UCR) program provides detailed crime statistics, including robbery rates, for different years. The relevant data was accessed from the website’s crime statistics section, specifically focusing on the robbery rate data for the desired years.

Once the datasets were acquired, they underwent a data formatting process to ensure compatibility and comparability. The gas price dataset was initially inspected for data quality, missing values, and consistency. Any necessary data cleaning steps, such as handling missing values or formatting inconsistencies, were performed to ensure accurate and reliable analysis.

Similarly, the robbery rate data for the United States was reviewed and validated for consistency and data quality. Any necessary data cleaning or formatting steps were applied to align it with the gas price dataset and ensure proper comparability between the two.

To analyze and compare the datasets, line plots were utilized. Line plots provide a visual representation of how gas prices and robbery rates change over time. By plotting the average CPI of regular gas on the y-axis and the corresponding years on the x-axis, a line plot can effectively demonstrate the trends and fluctuations in gas prices. Similarly, a line plot can be used to visualize the robbery rates in the United States over time, with the year on the x-axis and the robbery rate on the y-axis.

Analyzing the line plots involves examining the trends, patterns, and potential correlations between gas prices and robbery rates. Any notable correlations or deviations between the two datasets can be observed and explored further. Statistical techniques can also be employed to calculate correlations or conduct additional analyses to uncover any underlying relationships.

By following this methodology, I ensured that the gas price and robbery rate datasets were obtained from reliable sources, formatted appropriately, and analyzed using line plots to facilitate a meaningful comparison of the data sets.

Findings

# Set the CRAN mirror
options(repos = "https://cloud.r-project.org")

# Install the ggplot2 package
install.packages("ggplot2")
## Installing package into '/Users/junderwood3/Library/R/x86_64/4.2/library'
## (as 'lib' is unspecified)
## 
## The downloaded binary packages are in
##  /var/folders/cw/_3cbqvjs419655ldgk0lgfx80000gt/T//RtmpzpDmuF/downloaded_packages
library(ggplot2)
cpi_data <- data.frame(
  year = c(2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021),
  cpi_avg = c(3.5, 3.628, 3.518, 3.361, 2.44, 2.141, 2.4, 2.73, 2.63, 2.17, 3.05)
)

robbery_data <- data.frame(
  year = c(2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020),
  crime_rate = c(113.9, 113.1, 109, 101.3, 102.2, 102.9, 98.6, 86.1, 81.8, 73.9)
)
cpi_data <- data.frame(
  year = c(2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021),
  cpi_avg = c(3.5, 3.628, 3.518, 3.361, 2.44, 2.141, 2.4, 2.73, 2.63, 2.17, 3.05)
)

robbery_data <- data.frame(
  year = c(2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020),
  crime_rate = c(113.9, 113.1, 109, 101.3, 102.2, 102.9, 98.6, 86.1, 81.8, 73.9)
)

United States Reg Gas CPI

ggplot(cpi_data, aes(x = year, y = cpi_avg)) +
  geom_line(color = "blue") +
  labs(title = "Average CPI of Gas in the United States",
       x = "Year", y = "CPI Average") +
  scale_x_continuous(limits = c(2011, 2021), breaks = seq(2011, 2021, by = 1)) +
  theme_minimal()

United States Robbery Rate

ggplot(robbery_data, aes(x = year, y = crime_rate)) +
  geom_line(color = "red") +
  labs(title = "Average Rate of Robberies in the United States",
       x = "Year", y = "Crime Rate") +
  scale_x_continuous(limits = c(2011, 2020), breaks = seq(2011, 2020, by = 1)) +
  theme_minimal()

The charts I just plotted compare the average rate of robberies and the average CPI of regular gas in the United States over the years.

Average CPI of Gas in the United States:

-The chart shows the trend of the average CPI of gas in the United States from 2011 to 2021. -There is a noticeable downward trend in the average CPI, indicating a decrease in gas prices over the years. -From 2011 to 2014, the average CPI remains relatively high, suggesting higher gas prices during this period. -Starting from around 2015, there is a significant decline in the average CPI, indicating a decrease in gas prices. -The lowest point is reached in 2020, followed by a slight increase in 2021. -Overall, the chart illustrates the fluctuation and overall downward trend in gas prices during the specified period.

Average Rate of Robberies in the United States:

-The chart showcases the average rate of robberies in the United States from 2011 to 2020. -The data indicates a general decreasing trend in the average rate of robberies over the years. -From 2011 to 2013, there is a gradual decline in the average rate of robberies. -From 2014 to 2016, the average rate remains relatively stable at a lower level. -There is a slight increase in the average rate in 2017 and 2018, followed by a significant decrease in 2019 and 2020. The chart suggests a decreasing trend in the occurrence of robberies in the United States during the specified period.

By comparing these two charts, we can observe that while the average CPI of gas shows a downward trend, indicating a decrease in gas prices, the average rate of robberies also demonstrates a decreasing trend, suggesting a decline in robbery incidents.

This comparison highlights a potential inverse relationship between gas prices and robbery rates. However, further analysis and examination of additional factors are necessary to establish any causal relationship or correlation between these variables.

Atlanta

# Install and load the ggplot2 package
install.packages("ggplot2")
## Installing package into '/Users/junderwood3/Library/R/x86_64/4.2/library'
## (as 'lib' is unspecified)
## 
## The downloaded binary packages are in
##  /var/folders/cw/_3cbqvjs419655ldgk0lgfx80000gt/T//RtmpzpDmuF/downloaded_packages
library(ggplot2)

# Create data frames for the two datasets
cpi_data <- data.frame(
  year = c(2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021),
  cpi_avg = c(3.45, 3.53, 3.4605, 3.32, 2.31, 2.09, 2.31, 2.59, 2.475, 1.961, 2.89)
)

robbery_data <- data.frame(
  year = c(2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021),
  robbery = c(8151, 8362, 8639, 8716, 8549, 8411, 6715, 5612, 3667, 2810, 2990)
)

# Plotting CPI data
ggplot(cpi_data, aes(x = year, y = cpi_avg)) +
  geom_line(color = "blue") +
  labs(title = "Average CPI of Regular Gas in Atlanta",
       x = "Year", y = "CPI Average") +
  scale_x_continuous(limits = c(2011, 2021), breaks = seq(2011, 2021, by = 1)) +
  theme_minimal()

# Plotting robbery data
ggplot(robbery_data, aes(x = year, y = robbery)) +
  geom_line(color = "red") +
  labs(title = "Average Rate of Robbery in Atlanta",
       x = "Year", y = "Robbery Rate") +
  scale_x_continuous(limits = c(2011, 2021), breaks = seq(2011, 2021, by = 1)) +
  theme_minimal()

The first chart depicts the average CPI of regular gas in Atlanta from 2011 to 2021. It demonstrates the fluctuation and overall trend of gas prices during this period.

-The average CPI of gas in Atlanta starts at 3.45 in 2011 and experiences slight variations until 2014. -From 2014 to 2016, there is a significant drop in gas prices, with the average CPI reaching its lowest point at 1.961 in 2020. -In 2021, there is a slight increase in gas prices, as indicated by the rise in the average CPI to 2.89.

The second chart represents the average rate of robbery in Atlanta from 2011 to 2021. It illustrates the changes in robbery rates over time.

-In the early years, from 2011 to 2013, both the average CPI of gas and the rate of robbery in Atlanta exhibit relatively stable patterns. -From 2014 to 2016, there is a noticeable decline in both gas prices (reflected by the CPI) and robbery rates. -In 2020, a significant drop in both gas prices and robbery rates is observed, potentially influenced by external factors such as the COVID-19 pandemic. -Overall, while the CPI of regular gas in Atlanta fluctuates over the years, the rate of robbery follows a general decreasing trend.

-In the early years, from 2011 to 2013, both the average CPI of gas and the rate of robbery in Atlanta exhibit relatively stable patterns.

-From 2014 to 2016, there is a noticeable decline in both gas prices (reflected by the CPI) and robbery rates. -In 2020, a significant drop in both gas prices and robbery rates is observed, potentially influenced by external factors such as the COVID-19 pandemic.

-Overall, while the CPI of regular gas in Atlanta fluctuates over the years, the rate of robbery follows a general decreasing trend.

These findings suggest that there may be a correlation between gas prices and the rate of robbery in Atlanta. However, it is important to note that further analysis and consideration of other factors are necessary to establish any causal relationship or draw definitive conclusions.

Dallas

# Install and load the ggplot2 package
install.packages("ggplot2")
## Installing package into '/Users/junderwood3/Library/R/x86_64/4.2/library'
## (as 'lib' is unspecified)
## 
## The downloaded binary packages are in
##  /var/folders/cw/_3cbqvjs419655ldgk0lgfx80000gt/T//RtmpzpDmuF/downloaded_packages
library(ggplot2)

# Create a data frame for the dataset
dallas_cpi_data <- data.frame(
  year = c(2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019),
  cpi_avg = c(3.32, 3.4, 3.34, 3.16, 2.18, 1.91, 2.18, 2.41, 2.27)
)

# Plotting the Dallas CPI data
ggplot(dallas_cpi_data, aes(x = year, y = cpi_avg)) +
  geom_line(color = "blue") +
  labs(title = "Average CPI of Regular Gas in Dallas",
       x = "Year", y = "CPI Average") +
  scale_x_continuous(limits = c(2011, 2019), breaks = seq(2011, 2019, by = 1)) +
  theme_minimal()

# Install and load the ggplot2 package
install.packages("ggplot2")
## Installing package into '/Users/junderwood3/Library/R/x86_64/4.2/library'
## (as 'lib' is unspecified)
## 
## The downloaded binary packages are in
##  /var/folders/cw/_3cbqvjs419655ldgk0lgfx80000gt/T//RtmpzpDmuF/downloaded_packages
library(ggplot2)

# Create a data frame for the dataset
dallas_data <- data.frame(
  year = c(2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019),
  robbery = c(2599, 4093, 4202, 3856, 4177, 4604, 3791, 2206, 4400)
)

# Plotting the Dallas robbery data
ggplot(dallas_data, aes(x = year, y = robbery)) +
  geom_line(color = "green") +
  labs(title = "Robbery in Dallas",
       x = "Year", y = "Robbery Count") +
  scale_x_continuous(limits = c(2011, 2019), breaks = seq(2011, 2019, by = 1)) +
  theme_minimal()

The first chart represents the average CPI of regular gas in Dallas from 2011 to 2019. It showcases the fluctuations in gas prices during this period.

-The average CPI of gas in Dallas starts at 3.32 in 2011 and exhibits some variations over the subsequent years. -From 2011 to 2015, there is a gradual decrease in gas prices, with the average CPI reaching its lowest point at 2.18 in 2015. -In 2016, there is a further decrease in gas prices, with the average CPI dropping to 1.91, marking the lowest point during the analyzed period. -From 2016 to 2019, there is a slight increase in gas prices, with the average CPI rising to 2.41 in 2018 before slightly decreasing to 2.27 in 2019. -The average CPI of gas in Dallas exhibits a fluctuating pattern with no clear upward or downward trend.

The second chart represents the average rate of robberies in Dallas from 2011 to 2019. It illustrates the changes in robbery rates over time.

-The average rate of robberies in Dallas starts at 2,599 incidents in 2011 and displays fluctuations in subsequent years. -From 2011 to 2013, there is a notable increase in robbery rates, reaching a peak at 4,202 incidents in 2013. -From 2013 to 2016, there is a gradual decrease in robbery rates, with some variations. -From 2016 to 2019, there is a fluctuating pattern in robbery rates, with a slight increase observed in 2018. -The average rate of robberies in Dallas does not exhibit a clear consistent trend.

Analyzing the two charts together, we can make the following observations:

-There is no evident direct correlation between the average CPI of regular gas in Dallas and the average rate of robberies in Dallas. The fluctuations in gas prices do not appear to be closely linked to the variations in robbery rates.

-It is important to consider that gas prices and robbery rates can be influenced by various factors, including economic conditions, demographic changes, law enforcement efforts, and social dynamics. Additional analysis and the inclusion of other variables would be necessary to establish any potential relationship between these two factors.

In summary, the analysis suggests that there is no clear and direct relationship between the average CPI of regular gas and the average rate of robberies in Dallas during the specified time frame.

Boston

# Install and load the ggplot2 package
install.packages("ggplot2")
## Installing package into '/Users/junderwood3/Library/R/x86_64/4.2/library'
## (as 'lib' is unspecified)
## 
## The downloaded binary packages are in
##  /var/folders/cw/_3cbqvjs419655ldgk0lgfx80000gt/T//RtmpzpDmuF/downloaded_packages
library(ggplot2)

# Create a data frame for the dataset
boston_data <- data.frame(
  year = c(2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019),
  robbery = c(4066, 1910, 1868, 1680, 1551, 1530, 1703, 1419, 1039)
)

# Plotting the Boston robbery data
ggplot(boston_data, aes(x = year, y = robbery)) +
  geom_line(color = "blue") +
  labs(title = "Robbery in Boston",
       x = "Year", y = "Robbery Count") +
  scale_x_continuous(limits = c(2011, 2019), breaks = seq(2011, 2019, by = 1)) +
  theme_minimal()

# Create data frames for the two datasets
cpi_data <- data.frame(
  year = c(2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019),
  cpi_avg = c(3.56, 3.68, 3.52, 3.43, 2.41, 2.12, 2.4, 2.77, 2.62)
)
# Plotting CPI data
ggplot(cpi_data, aes(x = year, y = cpi_avg)) +
  geom_line(color = "blue") +
  labs(title = "Average CPI of Regular Gas in Boston",
       x = "Year", y = "CPI Average") +
  scale_x_continuous(limits = c(2011, 2019), breaks = seq(2011, 2019, by = 1)) +
  theme_minimal()

The first chart represents the average CPI of regular gas in Boston from 2011 to 2019. It displays the fluctuation in gas prices during this period.

-The average CPI of gas in Boston starts at 3.56 in 2011 and shows some variations in subsequent years. -From 2014 to 2015, there is a significant drop in gas prices, with the average CPI reaching its lowest point at 2.41. -In 2016, there is a slight increase in gas prices, as indicated by the rise in the average CPI to 2.12. -The average CPI of gas in Boston exhibits a fluctuating pattern with no clear upward or downward trend.

The second chart represents the average rate of robberies in Boston from 2011 to 2019. It illustrates the changes in robbery rates over time.

-The average rate of robberies in Boston starts at 4,066 incidents in 2011 and displays some variations in subsequent years. -From 2011 to 2013, there is a noticeable decrease in robbery rates, with the lowest point reached at 1,868 incidents in 2013. -From 2013 to 2017, there is a gradual increase in robbery rates, followed by a slight decrease in 2018 and 2019. -The average rate of robberies in Boston shows fluctuations with no clear consistent trend.

Analyzing the two charts together, we can make the following observations:

-There is no evident direct correlation between the average CPI of regular gas in Boston and the average rate of robberies in Boston. The fluctuations in gas prices do not appear to be closely linked to the variations in robbery rates. -It is crucial to consider that gas prices and robbery rates can be influenced by various factors, including economic conditions, social dynamics, and law enforcement strategies. Additional analysis and the inclusion of other variables would be necessary to establish any potential relationship between these two factors.

In summary, the analysis suggests that there is no clear and direct relationship between the average CPI of regular gas and the average rate of robberies in Boston during the specified time frame.

Chicago

# Install and load the ggplot2 package
install.packages("ggplot2")
## Installing package into '/Users/junderwood3/Library/R/x86_64/4.2/library'
## (as 'lib' is unspecified)
## 
## The downloaded binary packages are in
##  /var/folders/cw/_3cbqvjs419655ldgk0lgfx80000gt/T//RtmpzpDmuF/downloaded_packages
library(ggplot2)

# Create a data frame for the dataset
chicago_data <- data.frame(
  year = c(2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019),
  robbery = c(13975, 13476, 11815, 9804, 9649, 11957, 1667, 1635, 7983)
)

# Plotting the Chicago robbery data
ggplot(chicago_data, aes(x = year, y = robbery)) +
  geom_line(color = "red") +
  labs(title = "Robbery in Chicago",
       x = "Year", y = "Robbery Count") +
  scale_x_continuous(limits = c(2011, 2019), breaks = seq(2011, 2019, by = 1)) +
  theme_minimal()

# Create data frames for the two datasets
cpi_data <- data.frame(
  year = c(2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019),
  cpi_avg = c(3.72, 3.87, 3.77, 3.52, 2.59, 2.201, 2.4755, 2.77, 2.72)
)
# Plotting CPI data
ggplot(cpi_data, aes(x = year, y = cpi_avg)) +
  geom_line(color = "blue") +
  labs(title = "Average CPI of Regular Gas in Chicago",
       x = "Year", y = "CPI Average") +
  scale_x_continuous(limits = c(2011, 2019), breaks = seq(2011, 2019, by = 1)) +
  theme_minimal()

The analysis for the CPI of regular gas in Chicago and the average rate of robberies in Chicago is as follows:

The first chart represents the average CPI of regular gas in Chicago from 2011 to 2019. It illustrates the fluctuations in gas prices over this period.

-The average CPI of gas in Chicago starts at 3.72 in 2011 and experiences some variations in subsequent years. -From 2011 to 2015, there is a gradual decrease in gas prices, with the average CPI reaching its lowest point at 2.59 in 2015. -In 2016, there is a further decrease in gas prices, with the average CPI dropping to 2.201, marking the lowest point during the analyzed period. -From 2016 to 2019, there is a slight increase in gas prices, with the average CPI rising to 2.77 in 2018 and then slightly decreasing to 2.72 in 2019. -The average CPI of gas in Chicago exhibits a fluctuating pattern with no clear upward or downward trend.

The second chart represents the average rate of robberies in Chicago from 2011 to 2019. It showcases the changes in robbery rates over time.

-The average rate of robberies in Chicago starts at 13,975 incidents in 2011 and exhibits fluctuations in subsequent years. -From 2011 to 2013, there is a noticeable decrease in robbery rates, reaching a low point at 9,804 incidents in 2014. -From 2014 to 2016, there is a slight increase in robbery rates, with the peak observed at 11,957 incidents in 2016. -From 2016 to 2019, there is a significant decrease in robbery rates, with a notable drop to 1,635 incidents in 2018 before slightly increasing to 7,983 incidents in 2019. -The average rate of robberies in Chicago exhibits a fluctuating pattern with no clear consistent trend.

Analyzing the two charts together, we can make the following observations:

-There is no evident direct correlation between the average CPI of regular gas in Chicago and the average rate of robberies in Chicago. The fluctuations in gas prices do not appear to be closely linked to the variations in robbery rates. -It is important to note that gas prices and robbery rates can be influenced by various factors, including economic conditions, law enforcement efforts, social dynamics, and local policies. Further analysis and the inclusion of additional variables would be necessary to establish any potential relationship between these two factors.

In summary, the analysis suggests that there is no clear and direct relationship between the average CPI of regular gas and the average rate of robberies in Chicago during the specified time frame.

Recommendations & Discussion

After doing this analysis, here are some reccommendations for myself and how I could have made this report better.

  1. Additional Variables: If I were to conitinue this project I would explore the possibility of incorporating additional relevant variables that may influence robbery rates or gas CPI. Factors such as unemployment rates, income levels, population density, or law enforcement budgets could provide a more comprehensive understanding of the relationships and potential drivers behind the observed trends.

  2. Contextual Information: Next, I would include relevant contextual information about each city, such as significant events, policy changes, or socio-economic factors that may impact robbery rates or gas CPI. This would provide a deeper understanding of the local dynamics and potential influencing factors.

  3. Geospatial Analysis: I would alsoonsider incorporating geospatial analysis techniques to map the distribution of robbery rates and gas CPI across the cities. This would enable the identification of spatial patterns or hotspots, providing valuable insights for targeted interventions or policy planning.

  4. Policy Recommendations: Based on the analysis, I would also propose strategic policy recommendations for each city to address any observed issues or leverage opportunities. These recommendations would be informed by the insights gained from the data analysis and take into account the specific context and challenges faced by each city.

  5. Visualization Enhancements: I would also explore different visualization techniques or interactive tools to present the data in a more engaging and accessible manner. This could include interactive maps, animated charts, or interactive dashboards that allow users to explore the data and findings in a more interactive and personalized way.

  6. Limitations and Future Research: Lastly, Acknowledge and discuss the limitations of the analysis, such as data availability, potential confounding factors, or any other constraints. Provide suggestions for future research or areas of investigation that could further enhance the understanding of the relationship between robbery rates and gas CPI.Although I did that in my presentation, it would still be helpul for those who did not watch the presentation to know about the limitation of the research that I have done.

Overall, I really enjoyed working on this Passion Project. Even though there was no direct correlation between the rates of robbery and the cpi of regular gas in the United States, I now have the tools and knowledge now to try to continue this project if I wish to.