Analyzing Crime in Los Angeles
Find out when and where crime is most likely to occur, along with the types of crimes commonly committed in LA.
Los Angeles, California, attracts people from all over the world, offering lots of opportunities, not always of the good kind!
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. |
Explore the crimes.csv dataset and use your findings to answer the following questions:
- Which hour has the highest frequency of crimes? Store as an integer variable called
peak_crime_hour. - Which area has the largest frequency of night crimes (crimes committed between 10pm and 3:59am)? Save as a string variable called
peak_night_crime_location. - Identify the number of crimes committed against victims of different age groups. Save as a pandas Series called victim_ages, with age group labels "
0-17", "18-25", "26-34", "35-44", "45-54", "55-64", and "65+" as the index and the frequency of crimes as the values.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
crimes_0 = pd.read_csv("crimes.csv")
crimes_0.head()crimes_0.info()1. Finding the frequencies of crimes by the hour of occurrence
You can extract the hours from the relevant column, convert it to integer data type, and plot the frequencies.
crimes = pd.read_csv("crimes.csv", parse_dates=["Date Rptd", "DATE OCC"], dtype={"TIME OCC": str})
crimes.head()def display_crime_data_info(df):
display(df.info())
display(df.isna().sum())
display(df.describe())
display_crime_data_info(crimes)# Extract the first two digits from "TIME OCC", representing the hour, and convert to integer data type
crimes["HOUR OCC"] = crimes["TIME OCC"].str[:2].astype(int)
crimes.head()crime_hour_0 = crimes["HOUR OCC"].value_counts().sort_values(ascending=False)
crime_hour_0# Find the hour with the highest frequency of crimes
top_crime_hour_0 = crimes["HOUR OCC"].value_counts().idxmax()
top_crime_hour_0# Produce a countplot to find the largest frequency of crimes by hour
sns.countplot(data=crimes, x="HOUR OCC")
plt.show()# Which hour has the highest frequency of crimes? Store as an integer variable called peak_crime_hour
# Midday has the largest volume of crime
peak_crime_hour = 12
print(f'The hour that has the highest frequency of crimes is {peak_crime_hour}')2. Identifying the area with the most night crime
You'll need to filter the data for the relevant hours and count the number of crimes by area.
# Which area has the largest frequency of night crimes (crimes committed between 10pm and 3:59am)?
# Save as a string variable called peak_night_crime_location
# Filter for the night-time hours
# 0 = midnight; 3 = crimes between 3am and 3:59am, i.e., don't include 4
night_time = crimes[crimes["HOUR OCC"].isin([22,23,0,1,2,3])]
night_time.head()