Emergency Response Data Analysis | Data Analysis Project

Mai Aladin
3 min readAug 4, 2023

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Hello guys! In this case study, we will look into Georgia’s real world fake dataset how many incidents happened between the years 2021 and 2022, the travel times of each dispatch teams from stations to target incidents, the dispatch and target rates, the trends of incidents and main causes of it. This case study is for our clients in emergency response, the public officials, and the general audience.

Methods
Before analysis, I had downloaded fictional real world of data using Excel. Checking and reviewing of data types and formatting has been implemented specifically the incident date before transferring the cleaned data to Power BI. Next, I had created additional columns as follows:
1. Added dispatch percentage rate column by dividing the actual travel time to target travel time of 240 seconds
2. Added the target percentage rate column by dividing the actual en route target time in seconds to en route target travel time of 300 seconds.

I had also done these visuals for analysis:
1. Visual cards by presenting the KPIs of number of incidents, average arrival time in seconds, average dispatch time in seconds, the average dispatch rate and average target rate completed.
2. Bar charts to compare and contrast the top 5 causes and which stations had handled most number of incidents.
4. Line charts to present the trend of incidents filtered by year and month.

ANALYSIS
In the completion, we can infer the following analysis of our data.

1. Between 2021 and 2022, we had incurred 10,000 incidents with an avg total arrival time of 314.53 seconds which corresponds to 49% of dispatch rate and an average of 21% of en route targets reached and completed.
2. Causes of fire related incidents tops our emergency response which the stations of 4, 5 and 11 had the most response of all the stations.
3. We can conclude that we had a steady amount of incidents for the whole year but will reduce by the time of October.

RECOMMENDATIONS
As a practicing data analyst we rendered for the Georgia’s emergency response team, we can provide the following solutions.

1. For the emergency policy makers, create and pass bills for emergency response guidelines to promote awareness and preventive activities, programs for the communities. Policy makers should also provide quarterly assessments and activities for the communities to participate for these programs

2. For the business and entrepreneurial sectors, they can provide available, low cost but quality equipment for the disaster teams. Promote marketing strategies and educational contents that offers on medicines, first aid kits, safety equipment and emergency products should be implemented in their respective community.

3. For the general audience, active participation and volunteer in any activities to reduce fire related incidents can foster relationships and awareness for the community.

I’m open for project and work collaborations! Send me a message.
Send me a message.

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#dataanalysis #powerbi #datavisualization #emergencyresponse

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Mai Aladin
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