# Data-Driven Deployment of Cargo Drones: A U.S. Case Study Identifying Key Markets and Routes
## 3 Methodology
### Distance Band Truncation  
This stage of the methodology addressed the third research question, \"What is the distance distribution of origin-destination pairs that would be within EAA flight range capabilities?\". Without knowledge of future EAA port locations, the distance distribution of ports between FAF zone pairs spans the distance between the closest and furthest borders of the FAF zone pair. Hence, the geodesic distance between centroids of FAF zones provided an estimate for the mean distance between pairs of FAF zones. Subsequently, the workflow first located the centroids of each FAF zone by using a geographic information system (GIS) tool and then generated a distance matrix of all the pairwise combination of centroids. Hence, the matrix contained distances between the 16,512 (1292 \(-\) 129) centroid pairs, based on the ESRI 102005 \"Contiguous Equidistant Projection\" coordinate reference system. The GIS tool naturally produced zero for flight distances within a FAF zone, which was undesirable. The remedy was to estimate the nominal flight distance within a FAF zone as half the diagonal of a square with area equal to that of the FAF zone. The algorithm estimated the length of a side of the square by taking the square root of the area of the FAF zone.  
To determine the area of the FAF zone, a GIS algorithm dissolved all CONUS counties within a FAF zone with the land and water areas. The algorithm used the GIS shapefile from the USCB TIGER(r) database, which contains both the land and water areas of all U.S. counties [52]. The algorithm used the USCB 2017 commodity flow survey geographies database, which contained a FAF zone identifier for each U.S. County [53]. The workflow merged the TIGER(r) shapefile with data from the FAF geographies database by using the federal information processing (FIP) code for each county as the merge key. The CFS07_DDESTGEO field of the FAF geographies database matched the FAF zone codes except for 16 of them, which the author discovered by iterating the merging procedure and identifying missing values. Table 3 summarizes the FAF zone code revisions needed to complete the merge. Figure 2 illustrates the final GIS dissolve that resulted in 83 MSAs and 46 other FAF zones, along with their geospatial centroids. The assorted colors in the figure serve only to help more clearly distinguish between the regions and, particularly, adjacent regions.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**CFS** & **CFS Code** & **FAF Code** \\
**Area Name** & **Replaced** & **Substituted** \\ \hline Fresno-Madera, CA CFS Area & 69 & 65 \\ Philadelphia-Reading-Camden, PA-NJ-DE-MD (DE Part) & 100 & 101 \\ Philadelphia-Reading-Camden, PA-NJ-DE-MD (NJ Part) & 349 & 342 \\ Remainder of Delaware & 100 & 109 \\ Fort Wayne-Huntington-Auburn, IN & 189 & 183 \\ Wichita-Arkansas City-Winfield, KS & 209 & 202 \\ Louisville/Jefferson County-Elizabeth-Madison, KY-IN (KY Part) & 211 & 212 \\ Cincinnati-Wilmington-Maysville, OH-KY-IN CFS Area & 219 & 211 \\ (KY Part) & Omaha-Council Bluffs-Fremont, NE-IA (NE Part) & 310 & 311 \\ Remainder of Nebraska & 310 & 319 \\ Boston-Worcester-Providence, MA-RI-NH-CT (NH Part) & 330 & 331 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mismatched FAF zone codes in the CFSG database.