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911 DATA ANALYSIS

OBJECTIVE

Analyze the 911 calls data set from 2015 - 2020 from Montgomery County, Pennsylvania based on various department.

Quick Links :-

Tools Used :-

Pandas, Matplotlib, Seaborn, PowerBI

INTRODUCTION

The police/cops are always there to help out people that require their assistance. They are the reason that people are able to live peacefully and happily without any fear. This data set contains the data of such 911 calls that were made in the past.

Why this Dataset:- The data set contains the data about latitude and longitude of the places from where call was made. I also contains a short description of the call, the station that took the call, the reason behind the call and other relevant data.This file contains calls to 911 (emergency calls) in Montgomery County Pennsylvania.

Overall Goal:- 

1. What are the most common reasons for emergency calls, and how can public awareness and prevention programs be targeted effectively?


2. How do characteristics of emergency calls vary across different townships and neighborhoods?

EXPLORATION

I downloaded the csv file and used Pandas to check if there were any garbage/null values. I also removed duplicate info from the data.


Below is the sample image of the data column, there are a total of 9 columns that consist of geographical and call description data.

Screenshot 2024-05-31 200635.png
Screenshot 2024-05-31 200635.png

Next, the data was transformed to separate data into day, month and year. Separate the reason and department columns.

Below is the sample image of the cleaned and transformed dataset.

Screenshot 2024-05-31 202125.png

VISUALISATION

The dashboard is divided into 4 parts

Geographical data

This dashboard shows the geographical location of the 911 calls based on the Latitude and longitude columns.

911_analysis-images-0.jpg

Location Data

This dashboard analyses the call count based on the address and town and zipcodes.

It also has 2 slicers: Department and town

911_analysis-images-1.jpg

Department and Reason

This dashboard analyses the call count based on the Department and reason

It also has 2 slicers: Department Reason for calls.

911_analysis-images-2.jpg

Date and time

This dashboard analyses the call count based on the Year, Day, month and hour.

It also has 2 slicers: Department and Year

911_analysis-images-3.jpg

OBSERVATIONS

  • Lower Merion has the highest number of calls with 55470 calls with Abington next with 39927 calls.

  • SHANNONDELL DR & SHANNONDELL BLVD of Lower providence has the highest number of calls even though Lower Merion altogether has highest calls.

  • People ask for EMS a lot more as compared to other services.

  • Vehicle accident is the most common reason for 911 calls and it appears in calls for all 3 departments.

  • Midnight to early morning has lowest number of calls with Friday being the busiest day

RECOMMENDATION

  • The most common reason for emergency calls is vehicle accidents, with the Emergency Medical Services (EMS) department being the most frequently dispatched. To address this, public awareness and prevention programs should focus on road safety and accident prevention. Effective strategies include promoting safe driving practices, enforcing traffic laws, and increasing public awareness about the dangers of distracted and impaired driving. Additionally, community outreach programs can educate drivers on the importance of regular vehicle maintenance and the use of safety features such as seat belts and airbags. By targeting these areas, public awareness initiatives can significantly reduce the number of vehicle accidents and improve overall road safety.

  • The characteristics of emergency calls vary significantly across different townships and neighborhoods. In Lower Merion, calls are consistently high across various neighborhoods, indicating a widespread demand for emergency services. Conversely, in Lower Providence, the neighborhood of Shannondell, particularly along Shannondell Boulevard, experiences the highest number of emergency calls, while other areas within the township have relatively low call volumes. This disparity suggests that emergency response strategies in Lower Merion need to be uniformly robust, whereas in Lower Providence, targeted interventions in Shannondell could be more effective in addressing the concentrated demand for emergency services.

WHAT I LEARNED

  • How to create new column using DAX queries.

  • How to create a measure using DAX query.

  • Creating filters and slicers.

  • How to transform data in Pandas.

Download the dataset from

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