According to MentalHealth.gov, mental health includes our emotional, psychological, and social well-being and it largely affects quality of life for an individual. The factors that contribute to mental health issues include biological factors, family history, and life experiences such as trauma and abuse. Trauma and/or abuse, in particular, can occur or result from being a victim of a crime, especially from felonies, where the level of violence, danger, severity, and risk of crime tends to be higher compared to other crime categories. Although New York City does not have one of the highest crime rates in the country, it is still one of the most crowded and populated cities, setting itself up as a unique location for crimes. As a result, the ripple effects of crimes can have an impact on NYC residents physically, but also have a more lasting and devastating effect on them mentally that tangibly affects others not directly affected by or involved in the crime.
In addition to the unique impacts of crime in New York City, the location provides a wealth of open and transparent data for anyone to study and analyze. Gathering such data and computing statistics on crime in NYC may provide critical insights regarding steps to be taken for other similar high-density and highly populated areas, which can be helpful for law enforcement and government officials to deter crime. For these particular reasons, we focused on examining the various factors, patterns, and variables associated with crime in New York City.
We decided to study sex-related, drug-related, and weapons-related felonies in particular for a couple of reasons. First, we felt that there was a significant overlap among these three types of felonies - drug-related crimes, for example, are often committed concurrently with weapons-related crimes. Secondly, we felt that it was important to know the statistics on felonies, especially as experienced by victims, since such information may provide important indicators for mental health advocacy. Third, our raw data contained over 6 million observations and we wanted to reasonably limit our scope a bit. Fourth, as mentioned above, felonies are generally ranked higher compared to misdemeanors and violations, in terms of violence, risk, severity, and danger, providing us with potentially more important insights than the other categories. These reasons led us to explore data on the three main types of felonies (sex-related, weapon-related and drug-related) that occurred in New York City from 2014 to 2017.
We used the dataset collected by the New York City Police Department (NYPD); specifically, the NYPD Historic Complaint dataset, which provides longitudinal information on complaints filed to the NYPD, the type of crimes committed by a suspect, suspect demographics, victim demographics, location of crime, date and time of crime, and other variables.
The link to the raw dataset is here. Review the “The Data” page for information on how we cleaned the dataset to obtain a final analytic dataset.
We conducted various exploratory and formal analyses of different variables and factors associated with reported felonies across the boroughs in New York City from 2014 to 2017, including sociodemographics, day/seasonality, and other important trends.
We were interested in looking at a dataset containing information on NYPD crime complaints because there is a wealth of information and variables in the dataset. It is particularly exciting that we were able to study the variables longitudinally, as well as by discrete categories such as borough and crime category (sex-related, weapons-related and drug-related).
Furthermore, the dataset provided both victim and suspect demographic information, allowing us to link this information to either crime category, the crime location, or to each other. Lastly, the dataset provided ample opportunities to visualize interesting patterns associated with crime category, crime location (boroughs, distinct zip codes, and subsets of areas), time of crime, and the demographics of victims.
For the analyses, we used both graphical elements and functions (e.g., ggplot, plotly, shiny, dashboard) and data frames/tables to demonstrate longitudinal trends and trends by type of crime or location. We also included a Flex Dashboard and Shiny app controls to provide stratification by different variables.
Review our “Exploratory Analyses” and “Statistical Analysis” pages in particular for a further exploration of what we examined.
Overall, there were 19,753 victims for weapon-related felonies, 18,293 for drug-related felonies, and 8,646 for sex-related felonies between 2014 and 2017. Victims for weapons-related crimes were mostly within the 25-44 age-group (38%) and were more likely to be black (53%). Most victims for drug-related crimes were unknown or unidentified. The majority of victims of sex-related crimes were below 18 years (42%), were female (90%), and were black (38%).
Most crimes were heavily concentrated in the Bronx and Brooklyn. Bronx captured the highest overall felony rates, although, just as other boroughs, there has been a consistent decline in the rate throughout the 4 years. Interestingly, in Brooklyn, drug-related felonies were on the rise within the years examined.
When we looked specifically at weapons-related crimes, we observed that they mostly occurred on the streets, and usually over the weekends and late in the night as well. Similar to weapons-related crimes, drug-related crimes occurred on the streets. However, they were most likely to occur throughout the week and also from late mornings into the night. Sex-related crimes were very different from the other two categories because they were more likely to occur around midnight in apartments throughout the whole week. Although not reported in our main findings, it is important to note that most suspects of all the crimes were males.
Given these results, we may want to join hands with the U.N. in championing their 16 Days of Activism against Gender-Based Violence Campaign cause. Our data suggests that young women in New York City are at high risk of violence, and of mostly sex-related crimes. These experiences may eventually increase the burden of disease as they may contribute to mental health issues among the victims.
Email any one of us if you have any questions: Amin Yakubu | Jacky Choi | Yaa Asantewaa Klu | Jyoti Ankam