Los Angeles, California 😎. The City of Angels. Tinseltown. The Entertainment Capital of the World!
Known for its warm weather, palm trees, sprawling coastline, and Hollywood, along with producing some of the most iconic films and songs. However, as with any highly populated city, it isn't always glamorous and there can be a large volume of crime. That's where you can help!
You have been asked to support the Los Angeles Police Department (LAPD) by analyzing crime data to identify patterns in criminal behavior. They plan to use your insights to allocate resources effectively to tackle various crimes in different areas.
The Data
They have provided you with a single dataset to use. A summary and preview are provided below.
It is a modified version of the original data, which is publicly available from Los Angeles Open Data.
crimes.csv
| Column | Description |
|---|---|
'DR_NO' | Division of Records Number: Official file number made up of a 2-digit year, area ID, and 5 digits. |
'Date Rptd' | Date reported - MM/DD/YYYY. |
'DATE OCC' | Date of occurrence - MM/DD/YYYY. |
'TIME OCC' | In 24-hour military time. |
'AREA NAME' | The 21 Geographic Areas or Patrol Divisions are also given a name designation that references a landmark or the surrounding community that it is responsible for. For example, the 77th Street Division is located at the intersection of South Broadway and 77th Street, serving neighborhoods in South Los Angeles. |
'Crm Cd Desc' | Indicates the crime committed. |
'Vict Age' | Victim's age in years. |
'Vict Sex' | Victim's sex: F: Female, M: Male, X: Unknown. |
'Vict Descent' | Victim's descent:
|
'Weapon Desc' | Description of the weapon used (if applicable). |
'Status Desc' | Crime status. |
'LOCATION' | Street address of the crime. |
# Import required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
crimes = pd.read_csv("crimes.csv", parse_dates=["Date Rptd", "DATE OCC"], dtype={"TIME OCC": str})
#Question 1
# Create crime hour column from TIME OCC column
crimes['crime_hour'] = crimes['TIME OCC'].str[:2].astype(int)
# Calculate class frequencies
crime_hour_freq= crimes['crime_hour'].value_counts()
# Peak crime hour
peak_crime_hour = crime_hour_freq.index[0]
print('The peak crime hour in LA is:', peak_crime_hour)
#Histogram showing the distribution of the hour in which a crime is committed
sns.set_style('white')
sns.set_context('notebook')
g1= sns.histplot(x='crime_hour', bins=24, data=crimes)
g1.set_title('Distribution of crime hour', y=1.03)
g1.set(xlabel='hour of crime (24 hr format)', ylabel='number of crimes committed')
plt.show()
#Question 2
#Create df that includes crimes committed between 10pm and 0359am
night_crime_df = crimes.query('(`TIME OCC` >= "2200" or `TIME OCC` < "0400")')
#Calculate class frequencies for night time crime location
night_time_location_freq = night_crime_df['AREA NAME'].value_counts()
#Peak night crime location
peak_night_crime_location = night_time_location_freq.index[0]
print('The peak night crime location in LA is:', peak_night_crime_location)
#Question 3
#Calculate maximum victim age
maximum = crimes['Vict Age'].max()
#Create age group bins list
age_group_bins = [0, 17, 25, 34, 44, 54, 64, maximum]
#Create age group label list
age_group_labels = ["0-17", "18-25","26-34", "35-44", "45-54", "55-64", "65+"]
#Create victim ages column
crimes["victim_ages_bins"] = pd.cut(crimes["Vict Age"], labels = age_group_labels, bins = age_group_bins)
#Calculate class frequency for victim ages column
victim_ages = crimes["victim_ages_bins"].value_counts()
print('The victim age group that suffered the most number of crimes in LA is:', victim_ages.index[0],'yrs')
#Countplot showing number of crimes committed within each victim age group
sns.set_style('white')
sns.set_palette('Greys')
sns.set_context('notebook')
g3=sns.countplot(x='victim_ages_bins', data= crimes)
g3.set_title('Number of crimes committed per victim age group', y=1.03)
g3.set(xlabel='victim age group', ylabel='number of crimes committed')
plt.show()In this project, I carried out an exploratory data analysis (using Python) on crime data in the city of Los Angeles, California. The goal of the analysis was to answer three crime related questions.
- What hour of the day does the most crime occur in LA?
- What area in LA has the most night time (Between 10pm and 03:59am) crimes?
- What victim age group suffered the most crime in LA?
- For question 1, 12noon was the hour with the most crimes in LA
- For question 2, central is the area with the most night time crime in LA
- Victims that fall within the ages of 26 to 34 (34 inclusive) years suffered the most crime in LA