Jessica S. Tisch
Police Commissioner,
New York Police Department
More Information: New York Police Department Website
Jessica Tisch, appointed as Police Commissioner of New York City in November 2024, manages the strategic planning, policy development, and operational decision making of New York Police Department (NYPD). As Police Commissioner, Jessica must promote and prioritise public safety by effectively addressing present issues to maintain order.
Careful and thorough analysis of provided data highlighted the need for the implementation of serious safety measures aimed at reducing crime between 8pm-12am. Systems such as crime mapping and hot-spot policing for severely impacted boroughs would target a reduction in all offence types, providing optimal safety for vulnerable communities.
library(dplyr)
library(hms)
data_uncleaned <- read.csv("NYPD_Complaint_Data_Historic_recent.csv")
data_clean <- na.omit(data_uncleaned)
suppressWarnings({
new_data <- mutate(data_clean,CMPLNT_FR_TM = as_hms(CMPLNT_FR_TM), Time = case_when(CMPLNT_FR_TM >= 00:00:00 & CMPLNT_FR_TM < 10:00:00 ~ "Between 12am and 10am", CMPLNT_FR_TM >= 10:00:00 & CMPLNT_FR_TM < 20:00:00 ~ "Between 10am and 8pm", CMPLNT_FR_TM >= 20:00:00 ~ "After 8pm"))})library(tidyverse)
library(ggplot2)
ggplot(new_data, aes(x=LAW_CAT_CD)) +
geom_bar(fill = "lightblue",
alpha = 0.6) +
theme_bw() +
labs( title = "Offence Category Counts", x = "Offence Type", y = "Number of Offences")
library(ggplot2)
library(tidyverse)
ggplot(new_data, aes(x=LAW_CAT_CD, fill = Time)) + geom_bar(alpha = 0.8) + labs(title = "Reported Time of Different Offence Types", x = "Offence Type", y = "Count") + theme_bw()
These graphs depict the number of reported offence types, and the time period when they were reported. It can be seen that the majority of reports involved felonies, having an extremely vast majority occur after 8pm. With felonies being the most serious category of criminal offence, in addition to occurring in the late hours of the night, this poses a crucial safety and security threat to the communities in which they occur.
One way in which NYPD can monitor crime trends (such as increasing numbers of different offence types, and when they occur), is by implementing a crime-mapping system. By using the data collected, policing resources can be dispersed accordingly; targeting a reduction in overall crime rates and aiming to greatly improve the safety of New York citizens.
library(ggplot2)
pie(table(x=new_data$BORO_NM)) 
library(knitr)
library(kableExtra)
tab <- table(new_data$BORO_NM, data_clean$LAW_CAT_CD)
as.data.frame.matrix(tab) %>%
kable(format = "html", caption = "Crime Counts by Borough and Type") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = FALSE, position = "center")| FELONY | MISDEMEANOR | VIOLATION | |
|---|---|---|---|
| 3 | 1 | 1 | |
| BRONX | 616 | 340 | 85 |
| BROOKLYN | 675 | 629 | 209 |
| MANHATTAN | 1194 | 1354 | 326 |
| QUEENS | 238 | 243 | 69 |
In both the pie chart and the table above, it can be seen that Manhattan has the highest instances of all crime types; and by a significantly large degree. Additionally, felonies account for the majority of offences in Manhattan, Brooklyn, and The Bronx. This raises extreme concern for the safety and security of the communities which dwell in these areas. In order to manage and eventually rectify this issue, NYPD should implement hot-spot policing systems in all 4 boroughs.
Hot-spot policing is an efficient approach to managing increased numbers of crime in specific/targeted areas. By concentrating policing activities in impacted sectors - as opposed to dispersing scarce policing resources across large geographic expanses (Braga & Weisburd, 2010) - NYPD would aim to control and reduce the reoccurring instances of all types of criminal offences.
Thus, NYPD is recommended to implement both crime-mapping and hot-spot policing systems to target a reduction in all criminal offence types; as well as utilise their policing resources efficiently to target problem areas - such as Manhattan.
Some limitations of this data set include:
There were many missing values which had to be omitted from the dataset, and when following this process some missing values still remained (as shown in the ‘Crime Counts By Borough and Type’ table).
Although this data is updated on a regular basis (last updated: 16/04/25), all data sourced is from the year 2019; meaning that the information collected may not be reflective of current circumstances.
Article: The Effects of Hot-Spot Policing on Violence: A Systematic Review and Meta-Analysis
This article greatly informs readers about the effectiveness and use of hot-spot policing in reducing violent crime. It draws on previously conducted research, such as Randomised Controlled Trials and Quasi-Experimental Evaluations, to gauge the effectiveness of proactive policing. This involves initiatives such as the dispersal of policing resources, tailoring interventions to address the root causes of prominent offence types, and creating areas of high police-visibility to deter criminal behaviour.
When having issues with rendering my .qmd file, I copied and pasted the error message into ChatGPT to see how I could fix the issue. It guided me on what code I should use to suppress the warning messages.
When creating a table to represent my data, I wanted it to look more visually appealing. I asked ChatGPT how I should approach this and it informed me on how to use the kable_styling() function.
When asked “How is hot-spot policing effective in reducing crime?”, ChatGPT provided me with information on how this system is utilised.
In this project, I made use of:
Quarto.org to help in formatting my code chunks
Quarto.org to help in creating tab-sets to organise my figures
Appsilon.com to inform me on different code chunk options
YouTube to guide me on how to remove variables from my R global environment
My External Evidence:
Additionally, I also used the following exemplar projects as guidance when formatting my own HTML: