# Data-Driven Deployment of Cargo Drones: A U.S. Case Study Identifying Key Markets and Routes
## 3 Methodology
### Regional Demand Distribution  
This stage of the methodology addressed the second research question, \"Where would EAA deployments likely serve the largest demands?\". The zones defined in the FAF dataset encompassed areas that are metropolitan statistical areas (MSAs) and areas outside of MSAs, such as the remainder of a state, or entire states. The objective involved selecting MSAs as location candidates because of their high population density, significance to trade, and spatial localization. The empirical strategy to distinguish MSAs from other FAF zones was to detect where their metadata description contained the words \"area\" or  
\begin{table}
\begin{tabular}{p{42.7 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Algorithm** & **Theory of Operations, Advantages (A), and Disadvantages (D)** & **Hyperparameters** \\ \hline k-means & Initially selects representative points randomly for each of the number of specified clusters. It then iteratively reassigns points to clusters with the nearest centroid. Next, it recalculates the cluster centroid until convergence to the lowest within-cluster distance variance or when points cease switching clusters. The procedure stops after a specified number of iterations. Each rerun produces a different random initialization. The procedure also calculates a silhouette score for each point, with the highest average score representing the best clustering result. The silhouette score is a measure of within cluster similarity and outside cluster separation. A: well-studied and easy to apply to exceptionally large datasets. D: requires specification of the number of clusters and works best when clusters are symmetrical. Extracts communities from networks by constructing a k-nearest neighbor graph and weighing the edges through the number of shared neighbors. Defines clusters based on the density of edges inside communities relative to between communities. The resolution parameter affects the size of the identified clusters. A: efficient with exceptionally large networks. D: difficulty in detecting small communities. Density-based spatial clustering of applications with noise (DBSCAN). Groups together densely packed points and considers outlier points that lack close neighbors as noise. It defines a point to be core if k neighbors within a certain specified distance \\ DBSCAN & surround it. A set of core points defines a cluster. Non-core points may be parts of other clusters. A: finds non-linearly separable clusters. D: requires specification of the number of core point neighbors and the distance radius, which can be intractable for large feature spaces. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Unsupervised machine learning algorithms compared for cluster detection.  
\"part\". Consequently, the procedure identified 83 MSAs among the 129 FAF zones in the contiguous United States (CONUS).  
A pivot table algorithm compiled the MSA arrival and departure weights for each commodity category selected in the previous stage. A histogram of the pivot table output then presented the MSA distribution by weight moved. The distribution identified single MSAs that moved the most weight using trucks as target locations to serve the highest demands.