The first week at the Data School ended with an exciting challenge: our first Friday Project. My task was to address a real-world problem with life-saving implications. The Atlanta Fire Department faced difficulties in meeting its target response times, and the goal was to provide actionable insights. Two critical benchmarks were the focus: Time to Dispatch (4 minutes from alert to departure) and Time to En Route (5 minutes to reach the incident site). While delays were a known issue, the reasons behind them remained unclear. My objective was to outline a detailed plan for how to process the data, identify patterns, and provide meaningful insights for stakeholders.
Planning the Process: Step-by-Step
The first step in my plan would involve integrating three separate datasets: incident details, fire stations, and incident types. This integration would require addressing several known challenges:
- Standardizing Incident Type Codes: For example, correcting missing dashes to ensure consistency.
- Utilizing Geographic Data: Calculating distances between fire stations and incident sites using latitude and longitude coordinates.
- Transforming Time Fields: Categorizing "Time to Dispatch" and "Time to En Route" into logical groups, such as Boolean filters to indicate whether the target times were met.
These steps would ensure that the raw, messy data could be transformed into a structured dataset, making it easier to analyze and present meaningful results.
Key Focus Areas
Once the data was cleaned and structured, the next phase would involve identifying potential patterns and forming hypotheses. I proposed the following areas of exploration:
- Infrastructure Challenges: Assessing whether incidents in certain neighborhoods, such as those with poor road access or dense urban layouts, are more prone to delays.
- Traffic Patterns: Investigating if peak hours or other traffic-related factors contribute to delays.
- Station Efficiency: Comparing newer stations, such as Station 32, with more established ones to determine if experience impacts response times.
These hypotheses would guide further exploration and set the foundation for actionable recommendations.
Tools and Presentation Plan
To communicate the insights effectively, I would recommend using the following tools:
- Tableau: For creating visual representations of delays and patterns.
- Excel or Python: For data cleaning and transformation.
- Notion or a similar tool: For documenting the workflow and project details in a way that stakeholders can easily understand and pick up.
The deliverable would include a detailed plan for data preparation, an annotated dataset ready for analysis, and a clear presentation outlining potential areas for improvement. Stakeholders would benefit from having a structured framework to guide further investigations.
Next Steps
This exercise highlighted the importance of planning in data projects. Before diving into analysis, it’s crucial to have a clear roadmap for handling data. Structuring and cleaning data is not just a technical task—it lays the foundation for making meaningful business decisions.
Looking ahead, I suggested additional areas for exploration, such as how staffing levels, weather conditions, or incident type frequency might affect response times. While this project was a simulation, it provided valuable insights into how data-driven approaches can tackle real-world challenges.
In the end, this project was exciting to manage and gave me a clear understanding of how writing down each step makes planning more effective and straightforward. It also made me look forward to the opportunity to implement these plans and see the real impact they could make.