# Data-Driven Deployment of Cargo Drones: A U.S. Case Study Identifying Key Markets and Routes
## 4 Results and Discussions
### Regional Demand Distribution  
Figure 6 shows the MSA distribution by weight moved in thousand tons (KTons) for each of the four commodity categories selected in the previous stage of the data-mining workflow. Each colored box represents an MSA. The figure presents the outlier MSAs for the movement of commodity categories using trucks. Trucks in the Los Angeles (LA), California (CA), MSA transported the majority of the top three commodity categories by a significant margin.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Commodity** & **Representative Content** \\
**Category** & Food for grocery and convenience stores, supplies and food for restaurants and fast-food chains, hardware or plumbing supplies, and office supplies. \\  & Cell phones, batteries, electronic entertainment products, electric cooking appliances, computers, office equipment, recorded media, computer software, electronic components and circuit boards, semiconductor manufacturing machinery, electric motors and generators, cooking appliances, domestic appliances, telephone, and communications equipment. \\  & Non-electric motors and parts, pumps, compressors, fans, parts for air conditioning and refrigeration, dishwashers, manufacturing machines and tools, powered hand tools and apparatus, gears, and bearings for manufacturing equipment. \\  & Chemical mixtures for medical use, biological products, bandages, sutures, dental fillings, bone reconstructive cements, and other chemical preparations for medical use. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Representative content of the selected commodity categories.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Commodity** & **Million** & **Trillion** & **\%** & **\%** & **Rank** & **Rank** & **Truckload** \\  & **Tons** & **Dollars** & **KTons** & **USD M** & **Truck** & **Air** & **Equivalent** \\ \hline Mixed Goods & 424.2 & 1.44 & 3.3\% & 10.6\% & 1 & 11 & 18,851,782 \\ Electronics & 73.2 & 1.12 & 0.6\% & 8.2\% & 3 & 1 & 3,253,678 \\ Machinery & 118.8 & 0.97 & 0.9\% & 7.1\% & 4 & 4 & 5,280,730 \\ Pharmaceuticals & 19.8 & 0.65 & 0.2\% & 4.8\% & 6 & 5 & 882,067 \\ \hline Total & 636.0 & 4.2 & 5.0\% & 30.7\% & & & 28,268,257 \\ \hline All Commodities & 12,669.0 & 13.6 & & & & & 563,065,851 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Commodity categories selected for spatial demand analysis.  
Trucks in the Texas (TX) MSAs of Dallas-Fort Worth (DFW) and Houston moved the next largest amount of the four commodity categories. Table 6 summarizes the weight of the four commodity categories moved in the top eight MSAs. The four selected commodity categories moved in the eight MSAs accounted for 20.1% of the weight moved in the  
Figure 6: MSA weight distribution of (**a**) mixed goods, (**b**) electronics, (**c**) machinery, and (**d**) pharmaceuticals.  
CONUS. Electronics was the largest proportion by weight (28%) moved using trucks among the top eight MSAs. Additionally, electronics is by far the top commodity category moved by air. Therefore, it has the greatest propensity for mode shift from trucks, especially for time-sensitive orders that could benefit from the faster delivery times offered by EAA. This mode shift could potentially lead to significant changes in the logistics industry, including changes in delivery times, cost structures, and operational efficiencies. Future research should explore these potential impacts in more detail.