My analysis of the “911 Daily Dispatch Count by Agency” dataset is a comprehensive and methodical exploration into the trends and patterns of emergency dispatches in the Boston area. The study delved deep into the data, which covered dispatch counts for major emergency services including the Boston Police Department (BPD), Boston Fire Department (BFD), and Emergency Medical Services (EMS).
One of the key findings from my analysis is the identification of distinct yearly and monthly trends in dispatch counts for each agency. The BPD, in particular, showed a significant increase in dispatches from 2010 to 2013, followed by a decline in 2014, suggesting shifts in community dynamics or operational strategies. In contrast, BFD and EMS displayed more consistent dispatch patterns over the years. This variation in trends between agencies underscores the diverse nature of emergencies they respond to and highlights the importance of tailored resource allocation and preparedness strategies.
My study also made insightful observations about potential seasonal variations, especially with higher dispatch counts for BPD in specific months like March and December. The application of time series analysis techniques, including the Augmented Dickey-Fuller (ADF) test and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, revealed non-stationarity in the data, indicating the presence of underlying trends or cyclic behaviors. This aspect of the analysis is crucial for emergency services planning and resource management, as it aids in anticipating periods of high demand.
Furthermore, the implementation and evaluation of an ARIMA (1,1,1) model provided a methodological approach to forecast future dispatch needs. While the model offered valuable insights, my analysis also pointed out the need for refinement, especially in addressing outliers and improving the distribution of residuals. The diagnostic plots from the ARIMA model were instrumental in identifying areas for improvement, emphasizing the model’s limitations and the necessity for additional data to enhance its predictive accuracy.
The comprehensive use of visual tools like correlograms and histograms not only facilitated a deeper understanding of the complex data patterns but also aided in the interpretation of the model’s diagnostics. These visualizations played a significant role in conveying intricate analytical findings in an intuitive manner, making them accessible for decision-making processes.
Overall, my analysis stands out as a robust and nuanced approach to understanding emergency dispatch trends, offering valuable insights that could significantly impact resource distribution, emergency response strategies, and policy-making in the realm of public safety and emergency management.