Month: December 2023

MTH522_FinalReport3

We have compiled our findings and are currently in the final stages of preparing our Project 3 report. Our goal is to finalize and complete it by the end of today.

Today, I commenced work on the project report by consolidating all the analyses conducted this far on the dataset. The following aspects were addressed during the analysis:

1. Examining trends in crime rates across different years.
2. Analyzing the geographical distribution of crime in various districts.
3. Identifying the specific offense with the highest number of occurrences.
4. Locating streets characterized by the highest reported incidents of crime.
5. Forecasting Tier 3 crimes for future analysis.
6. Investigating the correlation between household incomes and crime rates in various locations in and around Boston.
7. Exploring the correlation between poverty rates and crime rates in different locations in and around Boston.

I also had meeting with my group members to discuss the results of our findings.

In my investigation today, I explored the relationship between poverty rates and crime rates in different areas. The calculated correlation coefficient, standing at 0.782002, points to a significant positive correlation between crime rates and poverty rates.

This positive correlation suggests that as poverty rates rise, so do crime rates, and conversely, a decrease in poverty rates tends to coincide with a decrease in crime rates. The closer the coefficient is to 1, the more pronounced this positive correlation becomes. Visualizing the data reinforces this observation, clearly illustrating that areas with higher poverty rates also tend to exhibit higher crime rates.

Opting to center my analysis on household incomes and poverty rates as external factors, my objective was to uncover their correlation with crime rates across various areas. Beginning with an exploration of household incomes, the dataset columns include district, median income, total households, and percentage distributions across income brackets, spanning from $14,999 and under to $150,000 and above. In the context of our analysis, the focus was particularly on the median income of that area.

To investigate the correlation between median household income and crime rates in various areas, I initially had to extract crime rates from the first dataset and manually mapped of districts and district codes in both datasets for effective merging. Subsequently, I calculated the correlation coefficient for median income and crime rates in each specific area, yielding the following result and interpretation:

Coefficient: -0.598716

Interpretation:

• • The negative correlation coefficient suggests an inverse relationship between median income and crime rate.
  • As median income increases, the crime rate tends to decrease, and conversely, as median income decreases, the crime rate tends to increase.
  • The proximity of the coefficient to -1 indicates a stronger negative correlation.

Upon visualizing the trend, a clear pattern emerged, illustrating that areas with higher incomes consistently exhibit lower crime rates.

Exploring the dynamics of crime rates in a specific area requires a consideration of diverse influential factors, encompassing socioeconomic conditions, demographics, and more. In our endeavor to attain a thorough comprehension, we scrutinized additional datasets, with a notable focus on the “BOSTON NEIGHBORHOOD DEMOGRAPHICS, 2015-2019.” This dataset, meticulously compiled by the BPDA Research Division, leverages U.S. Census Decennial data to delineate demographic transformations in Boston’s neighborhoods spanning the period from 1950 to 2010, utilizing consistent tract-based geographies.

The dataset’s most recent demographic insights are extrapolated from the 5-year American Community Survey (ACS), furnishing a holistic panorama of Boston’s neighborhoods based on Census-tract approximations. Within this dataset, diverse dimensions such as age, race, nativity, education, household income, and poverty rates are comprehensively covered. For our analysis, I chose to concentrate on household incomes and poverty rates as an external factor, aiming to discern its correlation with crime rates across different areas.