Overview of the dataset: This dataset includes all valid felony,
misdemeanor, and violation crimes reported to the New York City Police
Department (NYPD) in 2024.The data is collected from open data from NYC
open data and provided by New York City Police Department(NYPD). Here is
the link of dataset.
Loading packages and libraries:
options(repos = c(CRAN = "https://cloud.r-project.org"))
install.packages(c("dplyr", "tiydverse", "lubridate", "ggplot2", "leaflet", "readr", "sf", "htmlwidgets"))
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library(dplyr)
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library(tidyverse)
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library(lubridate)
library(ggplot2)
library(leaflet)
library(readr)
library(sf)
## Linking to GEOS 3.13.0, GDAL 3.10.1, PROJ 9.5.1; sf_use_s2() is TRUE
library(htmlwidgets)
Loading the dataset:
crime_data2024 <- read.csv("https://media.githubusercontent.com/media/zahid607/Final-Project/refs/heads/main/NYPD_Complaint_Data_2024.csv")
head(crime_data2024)
## CMPLNT_NUM CMPLNT_FR_DT CMPLNT_FR_TM CMPLNT_TO_DT CMPLNT_TO_TM ADDR_PCT_CD
## 1 298685380 12/30/2024 18:58:00 12/30/2024 19:00:00 111
## 2 298690828 12/31/2024 13:00:00 12/31/2024 13:10:00 113
## 3 298762768 12/30/2024 19:00:00 12/31/2024 7:00:00 111
## 4 298704506 12/28/2024 19:00:00 12/28/2024 19:15:00 101
## 5 298672727 12/31/2024 3:15:00 12/31/2024 3:20:00 105
## 6 298698016 12/31/2024 8:00:00 12/31/2024 9:00:00 116
## RPT_DT KY_CD OFNS_DESC PD_CD
## 1 12/31/2024 351 CRIMINAL MISCHIEF & RELATED OF 258
## 2 12/31/2024 344 ASSAULT 3 & RELATED OFFENSES 101
## 3 12/31/2024 341 PETIT LARCENY 357
## 4 12/31/2024 578 HARRASSMENT 2 638
## 5 12/31/2024 126 MISCELLANEOUS PENAL LAW 198
## 6 12/31/2024 344 ASSAULT 3 & RELATED OFFENSES 101
## PD_DESC CRM_ATPT_CPTD_CD LAW_CAT_CD BORO_NM
## 1 CRIMINAL MISCHIEF 4TH, GRAFFIT COMPLETED MISDEMEANOR QUEENS
## 2 ASSAULT 3 COMPLETED MISDEMEANOR QUEENS
## 3 LARCENY,PETIT OF VEHICLE ACCES COMPLETED MISDEMEANOR QUEENS
## 4 HARASSMENT,SUBD 3,4,5 COMPLETED VIOLATION QUEENS
## 5 CRIMINAL CONTEMPT 1 COMPLETED FELONY QUEENS
## 6 ASSAULT 3 COMPLETED MISDEMEANOR QUEENS
## LOC_OF_OCCUR_DESC PREM_TYP_DESC JURIS_DESC JURISDICTION_CODE
## 1 REAR OF COMMERCIAL BUILDING N.Y. POLICE DEPT 0
## 2 INSIDE RESIDENCE-HOUSE N.Y. POLICE DEPT 0
## 3 (null) STREET N.Y. POLICE DEPT 0
## 4 INSIDE RESIDENCE - APT. HOUSE N.Y. POLICE DEPT 0
## 5 INSIDE RESIDENCE-HOUSE N.Y. POLICE DEPT 0
## 6 INSIDE RESIDENCE-HOUSE N.Y. POLICE DEPT 0
## PARKS_NM HADEVELOPT HOUSING_PSA X_COORD_CD Y_COORD_CD SUSP_AGE_GROUP
## 1 (null) (null) NA 1061097 216033 25-44
## 2 (null) (null) NA 1046104 187464 <18
## 3 (null) (null) NA 1052283 211781 UNKNOWN
## 4 (null) (null) NA 1053361 158350 UNKNOWN
## 5 (null) (null) NA 1058127 199040 25-44
## 6 (null) (null) NA 1048028 178970 25-44
## SUSP_RACE SUSP_SEX TRANSIT_DISTRICT Latitude Longitude
## 1 WHITE M NA 40.75931 -73.72261
## 2 BLACK F NA 40.68101 -73.77699
## 3 UNKNOWN U NA 40.74770 -73.75447
## 4 BLACK F NA 40.60105 -73.75112
## 5 WHITE HISPANIC M NA 40.71270 -73.73351
## 6 BLACK M NA 40.65769 -73.77013
## Lat_Lon PATROL_BORO STATION_NAME
## 1 (40.759313, -73.722608) PATROL BORO QUEENS NORTH (null)
## 2 (40.681014, -73.776991) PATROL BORO QUEENS SOUTH (null)
## 3 (40.74770474705376, -73.75446623419606) PATROL BORO QUEENS NORTH (null)
## 4 (40.601049, -73.751124) PATROL BORO QUEENS SOUTH (null)
## 5 (40.712698, -73.733514) PATROL BORO QUEENS SOUTH (null)
## 6 (40.657687, -73.770132) PATROL BORO QUEENS SOUTH (null)
## VIC_AGE_GROUP VIC_RACE VIC_SEX
## 1 UNKNOWN UNKNOWN D
## 2 <18 BLACK M
## 3 25-44 BLACK M
## 4 25-44 UNKNOWN F
## 5 45-64 WHITE HISPANIC F
## 6 18-24 WHITE HISPANIC F
Structure of the dataset:
str(crime_data2024)
## 'data.frame': 577108 obs. of 35 variables:
## $ CMPLNT_NUM : chr "298685380" "298690828" "298762768" "298704506" ...
## $ CMPLNT_FR_DT : chr "12/30/2024" "12/31/2024" "12/30/2024" "12/28/2024" ...
## $ CMPLNT_FR_TM : chr "18:58:00" "13:00:00" "19:00:00" "19:00:00" ...
## $ CMPLNT_TO_DT : chr "12/30/2024" "12/31/2024" "12/31/2024" "12/28/2024" ...
## $ CMPLNT_TO_TM : chr "19:00:00" "13:10:00" "7:00:00" "19:15:00" ...
## $ ADDR_PCT_CD : int 111 113 111 101 105 116 106 105 113 111 ...
## $ RPT_DT : chr "12/31/2024" "12/31/2024" "12/31/2024" "12/31/2024" ...
## $ KY_CD : int 351 344 341 578 126 344 105 126 109 344 ...
## $ OFNS_DESC : chr "CRIMINAL MISCHIEF & RELATED OF" "ASSAULT 3 & RELATED OFFENSES" "PETIT LARCENY" "HARRASSMENT 2" ...
## $ PD_CD : int 258 101 357 638 198 101 396 198 409 101 ...
## $ PD_DESC : chr "CRIMINAL MISCHIEF 4TH, GRAFFIT" "ASSAULT 3" "LARCENY,PETIT OF VEHICLE ACCES" "HARASSMENT,SUBD 3,4,5" ...
## $ CRM_ATPT_CPTD_CD : chr "COMPLETED" "COMPLETED" "COMPLETED" "COMPLETED" ...
## $ LAW_CAT_CD : chr "MISDEMEANOR" "MISDEMEANOR" "MISDEMEANOR" "VIOLATION" ...
## $ BORO_NM : chr "QUEENS" "QUEENS" "QUEENS" "QUEENS" ...
## $ LOC_OF_OCCUR_DESC: chr "REAR OF" "INSIDE" "(null)" "INSIDE" ...
## $ PREM_TYP_DESC : chr "COMMERCIAL BUILDING" "RESIDENCE-HOUSE" "STREET" "RESIDENCE - APT. HOUSE" ...
## $ JURIS_DESC : chr "N.Y. POLICE DEPT" "N.Y. POLICE DEPT" "N.Y. POLICE DEPT" "N.Y. POLICE DEPT" ...
## $ JURISDICTION_CODE: int 0 0 0 0 0 0 0 0 0 0 ...
## $ PARKS_NM : chr "(null)" "(null)" "(null)" "(null)" ...
## $ HADEVELOPT : chr "(null)" "(null)" "(null)" "(null)" ...
## $ HOUSING_PSA : int NA NA NA NA NA NA NA NA NA NA ...
## $ X_COORD_CD : int 1061097 1046104 1052283 1053361 1058127 1048028 1034548 1058127 1047071 1047564 ...
## $ Y_COORD_CD : int 216033 187464 211781 158350 199040 178970 185628 199040 189114 217542 ...
## $ SUSP_AGE_GROUP : chr "25-44" "<18" "UNKNOWN" "UNKNOWN" ...
## $ SUSP_RACE : chr "WHITE" "BLACK" "UNKNOWN" "BLACK" ...
## $ SUSP_SEX : chr "M" "F" "U" "F" ...
## $ TRANSIT_DISTRICT : int NA NA NA NA NA NA NA NA NA NA ...
## $ Latitude : num 40.8 40.7 40.7 40.6 40.7 ...
## $ Longitude : num -73.7 -73.8 -73.8 -73.8 -73.7 ...
## $ Lat_Lon : chr "(40.759313, -73.722608)" "(40.681014, -73.776991)" "(40.74770474705376, -73.75446623419606)" "(40.601049, -73.751124)" ...
## $ PATROL_BORO : chr "PATROL BORO QUEENS NORTH" "PATROL BORO QUEENS SOUTH" "PATROL BORO QUEENS NORTH" "PATROL BORO QUEENS SOUTH" ...
## $ STATION_NAME : chr "(null)" "(null)" "(null)" "(null)" ...
## $ VIC_AGE_GROUP : chr "UNKNOWN" "<18" "25-44" "25-44" ...
## $ VIC_RACE : chr "UNKNOWN" "BLACK" "BLACK" "UNKNOWN" ...
## $ VIC_SEX : chr "D" "M" "M" "F" ...
Columns of the dataset:
colnames(crime_data2024)
## [1] "CMPLNT_NUM" "CMPLNT_FR_DT" "CMPLNT_FR_TM"
## [4] "CMPLNT_TO_DT" "CMPLNT_TO_TM" "ADDR_PCT_CD"
## [7] "RPT_DT" "KY_CD" "OFNS_DESC"
## [10] "PD_CD" "PD_DESC" "CRM_ATPT_CPTD_CD"
## [13] "LAW_CAT_CD" "BORO_NM" "LOC_OF_OCCUR_DESC"
## [16] "PREM_TYP_DESC" "JURIS_DESC" "JURISDICTION_CODE"
## [19] "PARKS_NM" "HADEVELOPT" "HOUSING_PSA"
## [22] "X_COORD_CD" "Y_COORD_CD" "SUSP_AGE_GROUP"
## [25] "SUSP_RACE" "SUSP_SEX" "TRANSIT_DISTRICT"
## [28] "Latitude" "Longitude" "Lat_Lon"
## [31] "PATROL_BORO" "STATION_NAME" "VIC_AGE_GROUP"
## [34] "VIC_RACE" "VIC_SEX"
Clean up date and time and prepare the data:
crime_data2024 <- crime_data2024 %>%
mutate(
RPT_DT = as.Date(RPT_DT, format = "%m/%d/%Y"),
CMPLNT_FR_TM = hms::as_hms(CMPLNT_FR_TM),
Hour = hour(CMPLNT_FR_TM),
Day = wday(RPT_DT, label = TRUE),
Month = month(RPT_DT, label = TRUE),
Week = isoweek(RPT_DT),
Season = case_when(
as.character(Month) %in% c("Dec", "Jan", "Feb") ~ "Winter",
as.character(Month) %in% c("Mar", "Apr", "May") ~ "Spring",
as.character(Month) %in% c("Jun", "Jul", "Aug") ~ "Summer",
TRUE ~ "Fall"
)
)
Index Crimes of the dataset:
index_crimes <- c("MURDER & NON-NEGL. MANSLAUGHTER",
"RAPE",
"ROBBERY",
"FELONY ASSAULT",
"BURGLARY",
"GRAND LARCENY",
"GRAND LARCENY OF MOTOR VEHICLE")
index_crime_data <- crime_data2024 %>%
filter(OFNS_DESC %in% index_crimes)
index_crime_data %>%
count(OFNS_DESC, sort = TRUE)
## OFNS_DESC n
## 1 GRAND LARCENY 48445
## 2 FELONY ASSAULT 29452
## 3 ROBBERY 16574
## 4 GRAND LARCENY OF MOTOR VEHICLE 14193
## 5 BURGLARY 13067
## 6 RAPE 1749
## 7 MURDER & NON-NEGL. MANSLAUGHTER 382
Index Crimes group by borough:
index_crime_summary <- crime_data2024 %>%
filter(OFNS_DESC %in% index_crimes) %>%
group_by(BORO_NM) %>%
summarise(Total_Index_Crimes = n()) %>%
arrange(desc(Total_Index_Crimes))
# View the result
print(index_crime_summary)
## # A tibble: 6 × 2
## BORO_NM Total_Index_Crimes
## <chr> <int>
## 1 MANHATTAN 31404
## 2 BROOKLYN 30751
## 3 BRONX 30393
## 4 QUEENS 27849
## 5 STATEN ISLAND 3448
## 6 (null) 17
Now Focus on robbery:
robbery_data <- crime_data2024 %>%
filter(OFNS_DESC == "ROBBERY", !is.na(Latitude), !is.na(Longitude))
Filter for robbery crime and group by borough & Bar
diagram:
robbery_by_borough <- crime_data2024 %>%
filter(OFNS_DESC == "ROBBERY") %>%
group_by(BORO_NM) %>%
summarise(Robbery_Count = n()) %>%
arrange(desc(Robbery_Count))
# Bar plot
ggplot(robbery_by_borough, aes(x = reorder(BORO_NM, Robbery_Count), y = Robbery_Count)) +
geom_bar(stat = "identity", fill = "LightBlue") +
geom_text(aes(label = Robbery_Count), hjust = -0.1, size = 4) + # Add labels
coord_flip() + # Rotate for readability
labs(title = "Robbery Counts by Borough",
x = "Borough",
y = "Number of Robbery Complaints") +
theme_minimal()
Robberies by Hour:
ggplot(robbery_data, aes(x = Hour)) +
geom_bar(fill = "tomato") +
labs(title = "Robberies by Hour", x = "Hour of Day", y = "Count") +
theme_minimal()
Robberies by day of week:
ggplot(robbery_data, aes(x = Day)) +
geom_bar(fill = "skyblue") +
labs(title = "Robberies by Day of Week", x = "Day", y = "Count") +
theme_minimal()
Monthly Robbery Trend:
# Group by month
monthly_robbery <- robbery_data %>%
group_by(Month) %>%
summarise(Count = n())
ggplot(monthly_robbery, aes(x = Month, y = Count)) +
geom_col(fill = "steelblue") +
labs(title = "Monthly Robbery Trends in 2024", y = "Number of Robberies") +
theme_minimal()
Seasonal Robbery Trend:
ggplot(robbery_data, aes(x = Season)) +
geom_bar(fill = "steelblue") +
labs(
title = "Number of Robbery Crimes by Season",
x = "Season",
y = "Number of Robbery Crimes"
) +
theme_minimal()
Spatial Mapping with Leaflet (Hotspot Visualization):
leaflet(data = robbery_data) %>%
addTiles() %>%
addCircleMarkers(
lng = ~Longitude,
lat = ~Latitude,
radius = 4,
color = "red",
fillOpacity = 0.4,
popup = ~paste("Date:", RPT_DT)
)
To save the map:
map <- leaflet(data = robbery_data) %>%
addTiles() %>%
addCircleMarkers(
lng = ~Longitude,
lat = ~Latitude,
radius = 4,
color = "red",
fillOpacity = 0.4
)
htmlwidgets::saveWidget(map, "robbery_hotspots_2024.html")
Conclusion: Based on the analysis of the crime_data2024 dataset,
several meaningful patterns emerged. Crime activity in New York City
varied notably by season, with increased incidents during the summer
months, possibly due to higher foot traffic and social activity. The
hourly distribution showed that most crimes occurred during late
afternoon and evening hours, indicating a potential link to commuting or
nightlife. Additionally, borough-level analysis revealed Bronx and
Brooklyn as hotspots for reported crimes, particularly in Summar for
robbery-related cases.These insights suggest that crime prevention
efforts could benefit from being seasonal and time-targeted, especially
during high-risk hours and months. Further exploration into specific
crime types, neighborhood-level data, and socioeconomic factors could
provide a deeper understanding of root causes and help shape policy or
policing strategies.