911_Project

911 Calls Capstone Project

Overview

This project involves analyzing 911 call data from Kaggle to understand the patterns and trends in emergency calls. The dataset includes various fields such as latitude, longitude, description, zipcode, and timestamps. The objective is to explore the data, extract meaningful insights, and visualize the results using Python and data science tools.

Dataset

The dataset consists of the following columns:

• lat: Latitude (float)
• lng: Longitude (float)
• desc: Description of the Emergency Call (string)
• zip: Zipcode (float, with some missing values)
• title: Title (string)
• timeStamp: Timestamp of the call (string)
• twp: Township (string, with some missing values)
• addr: Address (string)
• e: Dummy variable (always 1, integer)

Setup

Import Libraries

First, import the necessary libraries:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

Load Data

Read the CSV file into a DataFrame:

df = pd.read_csv('911.csv')

Explore Data

Check the information and head of the DataFrame:

df.info()
df.head()

Analysis

Basic Questions

1. Top 5 Zipcodes for 911 Calls

df['zip'].value_counts().head(5)

1. Top 5 Townships for 911 Calls

df['twp'].value_counts().head()

1. Unique Title Codes

df['title'].nunique()

Creating New Features

1. Extracting Reason for Calls

df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])

1. Most Common Reason

   df['Reason'].value_counts()
   

2. Countplot of 911 Calls by Reason

sns.countplot(x='Reason', data=df, palette='viridis')

Time Information

1. Convert timeStamp to Datetime

   df['timeStamp'] = pd.to_datetime(df['timeStamp'])
   

2. Extract Hour, Month, and Day of Week

   df['Hour'] = df['timeStamp'].apply(lambda time: time.hour)
   df['Month'] = df['timeStamp'].apply(lambda time: time.month)
   df['Day of Week'] = df['timeStamp'].apply(lambda time: time.dayofweek)
   

3. Map Day of Week to String

   dmap = {0: 'Mon', 1: 'Tue', 2: 'Wed', 3: 'Thu', 4: 'Fri', 5: 'Sat', 6: 'Sun'}
   df['Day of Week'] = df['Day of Week'].map(dmap)
   

4. Countplot of Day of Week with Hue

   sns.countplot(x='Day of Week', data=df, hue='Reason', palette='viridis')
   plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
   

5. Countplot of Month with Hue

sns.countplot(x='Month', data=df, hue='Reason', palette='viridis')
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

Note: Missing months can be filled by plotting the counts per month.

Monthly Analysis

1. Group by Month

   byMonth = df.groupby('Month').count()
   byMonth['lat'].plot()
   

2. Linear Fit on Number of Calls per Month

   sns.lmplot(x='Month', y='twp', data=byMonth.reset_index())
   

Daily Analysis

1. Create Date Column

df['Date'] = df['timeStamp'].apply(lambda time: time.date())

1. Plot Counts of 911 Calls per Date

   df.groupby('Date').count()['lat'].plot()
   plt.tight_layout()
   

2. Separate Plots for Each Reason

df[df['Reason'] == 'Traffic'].groupby('Date').count()['lat'].plot()
plt.title('Traffic')
plt.tight_layout()

df[df['Reason'] == 'EMS'].groupby('Date').count()['lat'].plot()
plt.title('EMS')
plt.tight_layout()


df[df['Reason'] == 'Fire'].groupby('Date').count()['lat'].plot()
plt.title('Fire')
plt.tight_layout()

Heatmaps and Clustermaps

1. Heatmap of Calls by Day of Week and Hour

   dayHour = df.groupby(by=['Day of Week', 'Hour']).count()['Reason'].unstack()
   sns.heatmap(dayHour, cmap='viridis')
   sns.clustermap(dayHour, cmap='coolwarm')
   

2. Heatmap of Calls by Day of Week and Month

   dayMonth = df.groupby(by=['Day of Week', 'Month']).count()['Reason'].unstack()
   sns.heatmap(dayMonth, cmap='coolwarm')
   sns.clustermap(dayMonth, cmap='coolwarm')
   

Conclusion

The analysis of 911 call data reveals insights into the distribution of emergency calls by reason, time, and location. Visualizations such as count plots, heatmaps, and clustermaps help in understanding the patterns and trends in the data.

Feel free to explore further and modify the analysis to suit your needs!

Files

• 911.csv: The dataset file.
• 911_calls_capstone.ipynb: Jupyter notebook containing the analysis code.

License

This project is licensed under the MIT License.

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