# Data-Driven Deployment of Cargo Drones: A U.S. Case Study Identifying Key Markets and Routes
## 4 Results and Discussions
### Demand Cluster Intersection  
Figure 3 shows the results from the three clustering algorithms. The first and second rows of the figure are weight-value scatter plots for air and trucks, respectively. In the air analysis, [PERSON] and k-means discerned similar points in cluster 5 (C5) and cluster 2 (C2), respectively, except for one noticeable outlier in the top right of the chart. For trucks, [PERSON] and k-means agreed on some points within cluster 3 (C3) and cluster 1 (C1), respectively. DBSCAN tended to isolate the outlier and cluster the remaining points together. Overall, k-means clustering provided the most logically attractive groupings of high-value, low-weight and low-value, high-weight commodities. Figure 4 points to the commodity categories in the k-means high-value, low-weight clusters that have the best propensity for air transport. The selected commodity categories were mixed goods, electronics, machinery, and pharmaceuticals due to their high market value that EAAs can capture from trucks. The remaining unselected commodity categories were both lower in value and much less frequently transported by air. \begin{table}
\begin{tabular}{l c c} \hline \hline
**CFS** & **CFS Code** & **FAF Code** \\
**Area Name** & **Replaced** & **Substituted** \\ \hline Remainder of New Hampshire & 330 & 339 \\ New York-Newark, NY-NJ-CT-PA (PA Part) & 429 & 423 \\ New York-Newark, NY-NJ-CT-PA (NJ Part) & 349 & 341 \\ Knoxville-Morristown-Sevierville, TN & 479 & 473 \\ Portland-Vancouver-Salem, OR-WA (WA Part) & 539 & 532 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 2: MSAs, other FAF Zones, and their centroids. Figure 4: K-means clustered high-value, low-weight commodity categories transported by (**a**) air and (**b**) using trucks. Figure 3: Comparison of clustering results for Louvain (**a**,**b**), DBSCAN (**c**,**d**), and k-means (**e**,**f**). The selected commodity categories represent time-sensitive and high-value items. The selected commodity categories represent time-sensitive and high-value items. The time urgency of orders for these items could significantly influence the distribution of EAA usages. For example, the machinery category includes replacement parts to keep manufacturing lines moving; the mixed goods category includes fresh supplies for restaurants; and the pharmaceutical category includes urgently needed medical items. Electronics emerged as a high-value outlier in both air and truck transport, with a more extreme disparity in air transport. Figure 5 ranks the commodity categories transported by value proportion and maps the intersection of each high-value, low-weight cluster for trucks (cluster 3) and air (clusters 1 and 3). There is a point of diminishing returns in value proportion for commodity categories outside of each high-value, low-weight cluster. Figure 5: Commodity category distribution by value proportion carried using (**a**) trucks and (**b**) by air. Electronics, machinery, and pharmaceuticals were common categories in both high-value clusters for truck and air. The commodity category of mixed goods was the highest in value proportion moved using trucks. This indicates its potential for mode shift to cargo EAAs even though it is mapped outside of the high-value, low-weight cluster for air transport. Table 4 presents a summary of the representative composition of each selected commodity category [54]. Table 5 summarizes the weight, value, and rank of the four selected commodities that have the highest propensity for mode shift to cargo EAAs. To provide a perspective on the weight, a typical North American semi-trailer truck (18-wheeler/big rig) carries 45,000 pounds (22.5 tons) of cargo [55]. The table shows that the four commodity categories moved among all FAF zones in 2017 required the equivalent of 28,268,257 (28 million) semi-trailer trucks (truckload equivalent). Based on growth estimates from the NFSP, truck transport will increase 35% by 2040 [15].