https://github.com/akunna1/wake-county-fire-response-analysis
This project analyzes Wake County Fire Department incident data to calculate and visualize response times, identify patterns by station and time of day, and evaluate response specifically for actual fires
https://github.com/akunna1/wake-county-fire-response-analysis
dplyr ggplot2 lubridate r-programming tidyr tidyverse
Last synced: about 2 months ago
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This project analyzes Wake County Fire Department incident data to calculate and visualize response times, identify patterns by station and time of day, and evaluate response specifically for actual fires
- Host: GitHub
- URL: https://github.com/akunna1/wake-county-fire-response-analysis
- Owner: akunna1
- Created: 2025-09-07T23:22:41.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-09-08T00:40:01.000Z (about 2 months ago)
- Last Synced: 2025-09-08T01:15:51.273Z (about 2 months ago)
- Topics: dplyr, ggplot2, lubridate, r-programming, tidyr, tidyverse
- Language: R
- Homepage: https://akunnatechstudio.com/geospatial
- Size: 536 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🚒 Wake County Fire Response Analysis
## Overview
Analyze Wake County Fire Department data to understand **response times**, **station performance**, and **peak hours** for fire calls, focusing on actual fire incidents.
## Data
* **File:** `Fire_Incidents.csv`
* **Columns include:**
`X`, `Y`, `OBJECTID`, `incident_number`, `incident_type`, `incident_type_description`, `arrive_date_time`, `cleared_date_time`, `dispatch_date_time`, `exposure`, `platoon`, `station`, `address`, `address2`, `apt_room`, `GlobalID`, `CreationDate`, `Creator`, `EditDate`, `Editor`
### Sample rows:
```
X Y OBJECTID incident_number incident_type incident_type_description arrive_date_time cleared_date_time dispatch_date_time exposure platoon station address
-78.62660452 35.87021286 474765 07-0031665 NULL 2007/11/15 11:17:00+00 2007/11/15 11:25:00+00 2007/11/15 11:10:00+00 0 6647 LAKE HILL DR RALEIGH, NC 27601
-78.69364226 35.79289581 474766 08-0017918 NULL 2008/06/29 06:20:00+00 2008/06/29 06:42:00+00 2008/06/29 06:17:00+00 0 539 METHOD RD RALEIGH, NC 27606
-78.6277871 35.81217058 474767 08-0032426 NULL 2008/11/18 04:19:00+00 2008/11/18 04:24:00+00 2008/11/18 04:12:00+00 0 2100 RUARK CT RALEIGH, NC 27601
-78.59542157 35.76121328 474768 07-0023051 444 Power line down 2007/08/21 22:52:00+00 2007/08/21 22:58:00+00 2007/08/21 22:47:00+00 0 A 12 1216 BEVERLY DR RALEIGH, NC 27601
```
## Tools & Libraries
* **R Libraries:** `tidyverse`, `lubridate`, `dplyr`, `tidyr`, `data.table`, `ggplot2`
## Key Features
* Clean and preprocess data
* Calculate response times
* Summarize average, median, min, and max response times by station
* Analyze trends over time
* Identify peak hours for fire calls
* Separate analysis for actual fires
## Outputs
* Cleaned dataset (`clean_fire_incidents_data`)
* Station summary tables (`station_summary`, `actual_fire_station_summary`)
* Hourly call counts (`dispatch_hour_count`, `actual_fire_dispatch_hour_count`)
* Visualizations: response times over time by station
## Usage
1. Place `Fire_Incidents.csv` in your working directory.
2. Run the R script.
3. Explore tables and visualizations to uncover fire response patterns.
## 💡 Insights
* Some stations respond faster on average than others.
* Response times fluctuate over time, possibly due to varying call volume or staffing.
* Peak call times help identify when the department is busiest, aiding resource planning.