911 Calls - Exploratory Data Analysis

13.10.2016 -

Python, Pandas, Seaborn

Introduction

For this project we'll analyze the 911 call dataset from Kaggle. The data contains the following fields:

lat : String variable, Latitude
lng: String variable, Longitude
desc: String variable, Description of the Emergency Call
zip: String variable, Zipcode
title: String variable, Title
timeStamp: String variable, YYYY-MM-DD HH:MM:SS
twp: String variable, Township
addr: String variable, Address
e: String variable, Dummy variable (always 1)

Let's start with some data analysis and visualisation imports.

1import numpy as np
2import pandas as pd
3import matplotlib.pyplot as plt
4import seaborn as sns
5%matplotlib inline
6
7sns.set_style('whitegrid')
8plt.rcParams['figure.figsize'] = (6, 4)
9
10# Reading the data
11df = pd.read_csv('data/911.csv')
12df.info()

<class 'pandas.core.frame.DataFrame'>
    RangeIndex: 99492 entries, 0 to 99491
    Data columns (total 9 columns):
    lat          99492 non-null float64
    lng          99492 non-null float64
    desc         99492 non-null object
    zip          86637 non-null float64
    title        99492 non-null object
    timeStamp    99492 non-null object
    twp          99449 non-null object
    addr         98973 non-null object
    e            99492 non-null int64
    dtypes: float64(3), int64(1), object(5)
    memory usage: 6.8+ MB

1# Checking the head of the dataframe
2df.head()

Output:

	lat	lng	desc	zip	title	timeStamp	twp	addr	e
0	40.297876	-75.581294	REINDEER CT & DEAD END; NEW HANOVER; Station ...	19525.0	EMS: BACK PAINS/INJURY	2015-12-10 17:40:00	NEW HANOVER	REINDEER CT & DEAD END	1
1	40.258061	-75.264680	BRIAR PATH & WHITEMARSH LN; HATFIELD TOWNSHIP...	19446.0	EMS: DIABETIC EMERGENCY	2015-12-10 17:40:00	HATFIELD TOWNSHIP	BRIAR PATH & WHITEMARSH LN	1
2	40.121182	-75.351975	HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...	19401.0	Fire: GAS-ODOR/LEAK	2015-12-10 17:40:00	NORRISTOWN	HAWS AVE	1
3	40.116153	-75.343513	AIRY ST & SWEDE ST; NORRISTOWN; Station 308A;...	19401.0	EMS: CARDIAC EMERGENCY	2015-12-10 17:40:01	NORRISTOWN	AIRY ST & SWEDE ST	1
4	40.251492	-75.603350	CHERRYWOOD CT & DEAD END; LOWER POTTSGROVE; S...	NaN	EMS: DIZZINESS	2015-12-10 17:40:01	LOWER POTTSGROVE	CHERRYWOOD CT & DEAD END	1

Basic Analysis

Let's check out the top 5 zipcodes for calls.

1df['zip'].value_counts().head(5)

19401.0    6979
19464.0    6643
19403.0    4854
19446.0    4748
19406.0    3174
Name: zip, dtype: int64

The top townships for the calls were as follows:

1df['twp'].value_counts().head(5)

LOWER MERION    8443
ABINGTON        5977
NORRISTOWN      5890
UPPER MERION    5227
CHELTENHAM      4575
Name: twp, dtype: int64

For 90k + entries, how many unique call titles did we have?

1df['title'].nunique()

Data Wrangling for Feature Creation

We can extract some generalised features from the columns in our dataset for further analysis.

In the title column, there's a kind of 'subcategory' or 'reason for call' alloted to each entry (denoted by the text before the colon).

The timestamp column can be further segregated into Year, Month and Day of Week too.

Let's start with creating a 'Reason' feature for each call.

1df['Reason'] = df['title'].apply(lambda x: x.split(':')[0])
2df.tail()

Output:

	lat	lng	desc	zip	title	timeStamp	twp	addr	e	Reason
99487	40.132869	-75.333515	MARKLEY ST & W LOGAN ST; NORRISTOWN; 2016-08-2...	19401.0	Traffic: VEHICLE ACCIDENT -	2016-08-24 11:06:00	NORRISTOWN	MARKLEY ST & W LOGAN ST	1	Traffic
99488	40.006974	-75.289080	LANCASTER AVE & RITTENHOUSE PL; LOWER MERION; ...	19003.0	Traffic: VEHICLE ACCIDENT -	2016-08-24 11:07:02	LOWER MERION	LANCASTER AVE & RITTENHOUSE PL	1	Traffic
99489	40.115429	-75.334679	CHESTNUT ST & WALNUT ST; NORRISTOWN; Station ...	19401.0	EMS: FALL VICTIM	2016-08-24 11:12:00	NORRISTOWN	CHESTNUT ST & WALNUT ST	1	EMS
99490	40.186431	-75.192555	WELSH RD & WEBSTER LN; HORSHAM; Station 352; ...	19002.0	EMS: NAUSEA/VOMITING	2016-08-24 11:17:01	HORSHAM	WELSH RD & WEBSTER LN	1	EMS
99491	40.207055	-75.317952	MORRIS RD & S BROAD ST; UPPER GWYNEDD; 2016-08...	19446.0	Traffic: VEHICLE ACCIDENT -	2016-08-24 11:17:02	UPPER GWYNEDD	MORRIS RD & S BROAD ST	1	Traffic

Now, let's find out the most common reason for 911 calls, according to our dataset.

1df['Reason'].value_counts()

EMS        48877
Traffic    35695
Fire       14920
Name: Reason, dtype: int64

1sns.countplot(df['Reason'])

Let's deal with the time information we have. Checking the datatype of the timestamp column.

1type(df['timeStamp'][0])

str

As the timestamps are still string types, it'll make our life easier if we convert it to a python DateTime object, so we can extract the year, month, and day information more intuitively.

1df['timeStamp'] = pd.to_datetime(df['timeStamp'])

For a single DateTime object, we can extract information as follows.

1time = df['timeStamp'].iloc[0]
2
3print('Hour:',time.hour)
4print('Month:',time.month)
5print('Day of Week:',time.dayofweek)

Hour: 17
Month: 12
Day of Week: 3

Now let's create new features for the above pieces of information.

1df['Hour'] = df['timeStamp'].apply(lambda x: x.hour)
2df['Month'] = df['timeStamp'].apply(lambda x: x.month)
3df['Day of Week'] = df['timeStamp'].apply(lambda x: x.dayofweek)
4
5df.head(3)

Output:

	lat	lng	desc	zip	title	timeStamp	twp	addr	e	Reason	Hour	Month	Day of Week
0	40.297876	-75.581294	REINDEER CT & DEAD END; NEW HANOVER; Station ...	19525.0	EMS: BACK PAINS/INJURY	2015-12-10 17:40:00	NEW HANOVER	REINDEER CT & DEAD END	1	EMS	17	12	3
1	40.258061	-75.264680	BRIAR PATH & WHITEMARSH LN; HATFIELD TOWNSHIP...	19446.0	EMS: DIABETIC EMERGENCY	2015-12-10 17:40:00	HATFIELD TOWNSHIP	BRIAR PATH & WHITEMARSH LN	1	EMS	17	12	3
2	40.121182	-75.351975	HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...	19401.0	Fire: GAS-ODOR/LEAK	2015-12-10 17:40:00	NORRISTOWN	HAWS AVE	1	Fire	17	12	3

The Day of the Week is an integer and it might not be instantly clear which number refers to which Day. We can map that information to a Mon-Sun string.

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

Output:

	lat	lng	desc	zip	title	timeStamp	twp	addr	e	Reason	Hour	Month	Day of Week
99489	40.115429	-75.334679	CHESTNUT ST & WALNUT ST; NORRISTOWN; Station ...	19401.0	EMS: FALL VICTIM	2016-08-24 11:12:00	NORRISTOWN	CHESTNUT ST & WALNUT ST	1	EMS	11	8	Wed
99490	40.186431	-75.192555	WELSH RD & WEBSTER LN; HORSHAM; Station 352; ...	19002.0	EMS: NAUSEA/VOMITING	2016-08-24 11:17:01	HORSHAM	WELSH RD & WEBSTER LN	1	EMS	11	8	Wed
99491	40.207055	-75.317952	MORRIS RD & S BROAD ST; UPPER GWYNEDD; 2016-08...	19446.0	Traffic: VEHICLE ACCIDENT -	2016-08-24 11:17:02	UPPER GWYNEDD	MORRIS RD & S BROAD ST	1	Traffic	11	8	Wed

Let's combine the newly created features, to check out the most common call reasons based on the day of the week.

1sns.countplot(df['Day of Week'],hue=df['Reason'])
2plt.legend(bbox_to_anchor=(1.25,1))

It makes sense for the number of traffic related 911 calls to be the lowest during the weekends, what's also iteresting is that Emergency Service related calls are also low during the weekend.

1sns.countplot(df['Month'],hue=df['Reason'])
2
3plt.legend(bbox_to_anchor=(1.25,1))

Now, let's check out the relationship between the number of calls and the month.

1byMonth = pd.groupby(df,by='Month').count()
2byMonth['e'].plot.line(y='e')
3
4plt.title('Calls per Month')
5plt.ylabel('Number of Calls')

Using seaborn, let's fit the number of calls to a month and see if there's any concrete correlation between the two.

1byMonth.reset_index(inplace=True)
2
3sns.lmplot(x='Month',y='e',data=byMonth)
4plt.ylabel('Number of Calls')

So, it does seem that there are fewer emergency calls during the holiday seasons.

Let's extract the date from the timestamp, and see behavior in a little more detail.

1df['Date']=df['timeStamp'].apply(lambda x: x.date())
2df.head(2)

Output:

	lat	lng	desc	zip	title	timeStamp	twp	addr	e	Reason	Hour	Month	Day of Week	Date
0	40.297876	-75.581294	REINDEER CT & DEAD END; NEW HANOVER; Station ...	19525.0	EMS: BACK PAINS/INJURY	2015-12-10 17:40:00	NEW HANOVER	REINDEER CT & DEAD END	1	EMS	17	12	Thu	2015-12-10
1	40.258061	-75.264680	BRIAR PATH & WHITEMARSH LN; HATFIELD TOWNSHIP...	19446.0	EMS: DIABETIC EMERGENCY	2015-12-10 17:40:00	HATFIELD TOWNSHIP	BRIAR PATH & WHITEMARSH LN	1	EMS	17	12	Thu	2015-12-10

Grouping and plotting the data:

1pd.groupby(df,'Date').count()['e'].plot.line(y='e')
2
3plt.legend().remove()
4plt.tight_layout()

We can also check out the same plot for each reason separately.

1pd.groupby(df[df['Reason']=='Traffic'],'Date').count().plot.line(y='e')
2plt.title('Traffic')
3plt.legend().remove()
4plt.tight_layout()

1pd.groupby(df[df['Reason']=='Fire'],'Date').count().plot.line(y='e')
2plt.title('Fire')
3plt.legend().remove()
4plt.tight_layout()

1pd.groupby(df[df['Reason']=='EMS'],'Date').count().plot.line(y='e')
2plt.title('EMS')
3plt.legend().remove()
4plt.tight_layout()

Let's create a heatmap for the counts of calls on each hour, during a given day of the week.

1day_hour = df.pivot_table(values='lat',index='Day of Week',columns='Hour',aggfunc='count')
2
3day_hour

Output:

Hour	0	1	2	3	4	5	6	7	8	9	...	14	15	16	17	18	19	20	21	22	23
Day of Week
Fri	275	235	191	175	201	194	372	598	742	752	...	932	980	1039	980	820	696	667	559	514	474
Mon	282	221	201	194	204	267	397	653	819	786	...	869	913	989	997	885	746	613	497	472	325
Sat	375	301	263	260	224	231	257	391	459	640	...	789	796	848	757	778	696	628	572	506	467
Sun	383	306	286	268	242	240	300	402	483	620	...	684	691	663	714	670	655	537	461	415	330
Thu	278	202	233	159	182	203	362	570	777	828	...	876	969	935	1013	810	698	617	553	424	354
Tue	269	240	186	170	209	239	415	655	889	880	...	943	938	1026	1019	905	731	647	571	462	274
Wed	250	216	189	209	156	255	410	701	875	808	...	904	867	990	1037	894	686	668	575	490	335

7 rows × 24 columns

Now create a HeatMap using this new DataFrame.

1sns.heatmap(day_hour)
2
3plt.tight_layout()

We see that most calls take place around the end of office hours on weekdays. We can create a clustermap to pair up similar Hours and Days.

1sns.clustermap(day_hour)

And this concludes the exploratory analysis project.

911 Calls - Exploratory Data Analysis

.css-1bw77fa{color:var(--theme-ui-colors-primary);-webkit-text-decoration:none;text-decoration:none;}.css-1bw77fa:hover{-webkit-text-decoration:underline;text-decoration:underline;}Introduction

Basic Analysis

Data Wrangling for Feature Creation

Introduction