Project: Analyzing Crime in Los Angeles

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: `A` - Other Asian `B` - Black `C` - Chinese `D` - Cambodian `F` - Filipino `G` - Guamanian `H` - Hispanic/Latin/Mexican `I` - American Indian/Alaskan Native `J` - Japanese `K` - Korean `L` - Laotian `O` - Other `P` - Pacific Islander `S` - Samoan `U` - Hawaiian `V` - Vietnamese `W` - White `X` - Unknown `Z` - Asian Indian
`'Weapon Desc'`	Description of the weapon used (if applicable).
`'Status Desc'`	Crime status.
`'LOCATION'`	Street address of the crime.

# Re-run this cell
# 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})
crimes.head()

Exploratory Data Analysis

# Checking for duplicates
print(crimes.duplicated().sum())

The dataset has no duplicated row.

# Checking for missing values
print(crimes.info())

The dataset has some columns with missing values: Vict Sex, Vict Descent and Weapon Desc, which seems to be fine for this analysis. The information of victim's age and descent was optional, while the weapond description only applied for certain cases, so those missing values are valid.

# Checking for basic summary statistics in the numeric variables
print(crimes.describe())

The victim's age display valid values between 2 and 99. The mean victim's age in Los Angeles city is 39 +/- 15 years old.

# Checking for the number of categorie in the non-numeric variables
nonnum_vars = crimes.select_dtypes("object")
print([(col, crimes[col].nunique()) for col in nonnum_vars.columns])

We can see that the number of types of crimes committed Crm Cd Desc are 104, while there are 74 types of weapon used for the crimes. Thee variables could be regrouped in a deeper analysis.

Frequency of crimes

Frequency per hour

To get the frequency per hour, we need to obtain the hour part of the time of occurrence column, which it's a string object.

# Getting the hour from the time of occurrence column
crimes["TIME OCC INT"] = crimes["TIME OCC"].astype(int) # Converting the string into numeric type
hour_of_crime = np.trunc(crimes["TIME OCC INT"]/100).astype(int) # Getting only the hour
print(hour_of_crime.describe())